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Assessment of a Telerehabilitation and a Telehomecare Program for Chronically Ill Veterans


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ASSESSMENT OF A TELEREHABIL ITATION AND A TELEHOMECARE PROGRAM FOR VETERANS WI TH CHRONIC ILLNESSES By ROXANNA M. BENDIXEN A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLOR IDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2006

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Copyright 2006 by Roxanna M. Bendixen

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To my loving husband; he is the reason.

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iv ACKNOWLEDGMENTS I would first like to express my appreciati on to the Veterans Ad ministration Office of Academic Affairs, Pre-Doct oral Associate Health Rehabilitation Research Fellowship and the VA Rehabilitation Outcomes Research Center for funding of this dissertation. Additionally, I’d like to thank the VA Commun ity Care Coordination Services for their support and assistance with the data necessary to complete this study. I wish to convey my gratitude to a number of individuals who have guided and supported me throughout my doctoral studies. Fi rst and foremost, I wish to recognize my doctoral committee. I esp ecially thank my committee chairperson, Dr. William Mann, for trusting in me to work on this project a nd believing in me to make it a success. You have always supported and inspired me and I thank you. Dr. Charles Levy, my writing and brainstorming partner, thank you for alwa ys being available for me and for keeping me laughing. Dr. Craig Velozo, I appreciat e your guidance and support at the RORC and your heartfelt advice. Dr. Bruce Vogel, thank you for your contributions on methodological issues and assistance with st atistical matters. I may not have fully understood many of our conversations, but you always made me reach higher and try harder. Special thanks are given to Mr. Steve Olive for your invaluable help with VA databases, your assistance with SAS progr amming issues, and your friendship…all of which were essential for this dissertation. I am more than grateful to you for the long talks, sparring conversations, and using all my cell phone minutes.

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v I am also very fortunate to have my d ear friends and collea gues in Occupational Therapy and the Rehabilitation Science Doctor al Program, Megan, Jess ica, Patricia, Rick, Cristina, Eric, Bhagwant, Leigh, Sande, Inga Pey-Shan, Jai Wa, as well as those who have gone before me, Arlene, Michael, Denni s and Michelle. I must also mention and thank Elena, Joanne, Emily, Sandy, Orit and Sherri lene. You have made this the greatest experience I’ve ever had. I will be connected to you always. Special thanks to my fellow LAMPees, Kathy, Steve and Wendy; I wouldn’t be here without your assistance, hard work, and fr iendship. Also thanks to TCCP, especially Joanne, for assistance with data collection and coding. I thank my family for their love and encouragement, but mostly for understanding that it just takes some peopl e a little longer. And most notably my husband, John, whose idea it was for me to pursue a PhD. My true love, your support and sacrifices have made this pursuit possible. We actually did it baby.

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vi TABLE OF CONTENTS page ACKNOWLEDGMENTS.................................................................................................iv LIST OF TABLES.............................................................................................................ix LIST OF FIGURES...........................................................................................................xi ABSTRACT......................................................................................................................x ii CHAPTER 1 INTRODUCTION........................................................................................................1 Challenges in Healthcare..............................................................................................2 The Veterans Healthcare System..................................................................................5 The impact of aging and ch ronic illness in the VA...............................................6 VA telehealth applications....................................................................................6 Models of VA telehealth care................................................................................7 Technology Care Coordination Program......................................................................8 The Low Activities of Daily Living (ADL) Monitoring Program.............................11 Daily Remote Monitoring by LAMP and TCCP........................................................14 Theoretical Model.......................................................................................................14 The International Classification of Func tioning, Disability and Health model...15 Telehealth / ICF framework................................................................................16 Specific Aims..............................................................................................................18 Summary.....................................................................................................................20 2 REVIEW OF THE LITERATURE............................................................................22 Aging, Chronic Illness and Disability........................................................................22 Environmental contributors to functional decline...............................................23 Access to healthcare services..............................................................................25 Information technology.......................................................................................26 Benefits to the use of IT...............................................................................28 Barriers to the use of IT................................................................................29 Telehealth Applications..............................................................................................31 Telehomecare......................................................................................................32 Telerehabilitation.................................................................................................37 Telehealth applications within the Veterans Health Administration...................42

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vii Summary.....................................................................................................................46 3 HEALTH RELATED COST ANALYSIS.................................................................49 Methods......................................................................................................................51 Cost Data.............................................................................................................51 Linking of the Treatment Groups to the Comparison Group Pool......................53 Reported long-term chronic diseases...........................................................54 Enrollment date............................................................................................57 Inpatient bed days of care pre-enrollment....................................................57 Matching..............................................................................................................58 Telehealth vs. Standard Care...............................................................................59 Study Design.......................................................................................................60 Statistical Analysis..............................................................................................61 Results........................................................................................................................ .63 LAMP and Matched Comparison Group............................................................63 Hospital bed days of care.............................................................................64 Clinic visits...................................................................................................65 Emergency room visits.................................................................................66 Nursing home bed days of care....................................................................66 TCCP and Matched Comparison Group..............................................................67 Hospital bed days of care.............................................................................69 Clinic visits...................................................................................................69 Emergency room visits.................................................................................70 Nursing home bed days of care....................................................................70 Cost Analysis: Differencein-Differences Approach.........................................70 Treatment Group Comparisons...........................................................................72 Discussion...................................................................................................................74 4 HEALTH STATUS AND OUTCOMES FROM THE VETERANS SHORT FORM-12 HEALTH SURVEY..................................................................................81 Development of the Veteran’s SF-36.........................................................................81 Veteran’s SF-36 Health Survey...........................................................................82 Development of the Veteran’s SF-12..................................................................83 Methods......................................................................................................................86 Design..................................................................................................................86 Participants..........................................................................................................87 Administration of the SF-12V.............................................................................88 Scoring.................................................................................................................89 Statistical Analysis..............................................................................................90 Results........................................................................................................................ .90 Discussion...................................................................................................................98 5 PERSONAL INTERVIEWS FROM TELEHEALTH PARTICIPANTS................106 Qualitative Research and Healthcare........................................................................106

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viii Methods....................................................................................................................110 Selection of Subjects.........................................................................................110 Data Collection..................................................................................................112 Coding Process..................................................................................................113 Reliability and Validity.....................................................................................115 Results.......................................................................................................................1 16 Description of Sample.......................................................................................116 Descriptions and Themes..................................................................................116 Interpretation / meaning of the data...................................................................117 Care coordination.......................................................................................117 Technology.................................................................................................121 Adaptive equipment...................................................................................127 Satisfaction with telehealth........................................................................129 Reliability and validity...............................................................................131 Member checking.......................................................................................131 Comparison with Quantitative Analysis............................................................132 Discussion.................................................................................................................132 6 DISCUSSION...........................................................................................................137 Cost Analysis............................................................................................................138 Health-Related Quality of Life.................................................................................142 Personal Interviews...................................................................................................146 Summary...................................................................................................................147 APPENDIX: INTERVIEW GUID E FOR PARTICIPANTS AND/OR CAREGIVERS.........................................................................................................151 LIST OF REFERENCES.................................................................................................152 BIOGRAPHICAL SKETCH...........................................................................................171

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ix LIST OF TABLES Table page 2-1 Health-related applications for information technology..........................................27 3-1 Baseline characteristics of telere habilitation group, Low ADL Monitoring Program (LAMP), and matched comparison group.................................................63 3-2 Healthcare expenditure s for LAMP (n=115) one-year pre-enrollment and oneyear post-enrollment.................................................................................................64 3-3 Healthcare expenditu res for LAMP matched comparison group (n=115) oneyear pre-enrollment and one -year post-enrollment..................................................64 3-4 Baseline characteristics of tele homecare group, Technology Care Coordination Program (TCCP), and matched comparison group..................................................67 3-5 Healthcare expenditure s for TCCP (n=112) one-year pre-enrollment and oneyear post-enrollment.................................................................................................68 3-6 Healthcare expenditures for matche d comparison group (n=112) one-year preenrollment and one-year post-enrollment.................................................................68 3-7 Multivariable regression analysis su mmary examining the relationship among LAMP and matched comparison group...................................................................71 3-8 Multivariable regression analysis su mmary examining the relationship among TCCP and matched comparison group.....................................................................72 3-9 Multivariable regression analysis summary examining the relationship in healthcare costs betwee n LAMP and TCCP............................................................73 4-1 Short Form Health Surv ey-36V questions with resp ective Short Form Health Survey-12V questions..............................................................................................84 4-2 Characteristics of participants..................................................................................91 4-3 Differences between SF-12V baselin e and 12-month follow-up for LAMP (paired sample statistics)..........................................................................................92 4-4 Differences between SF-12V baseline and 12-month follow-up for TCCP (paired sample statistics)..........................................................................................93

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x 4-5 Group differences for SF-12V baseline scores........................................................95 4-6 Group differences for SF-12V at 12-month follow-up............................................95 4-7 TCCP group differences for SF-12V at baseline.....................................................96 4-8 Cross-sectional relati onship between presence of primary medical condition, physical component summary (PCS-12) at baseline and 12 months for two telehealth cohorts (LAMP and T CCP, n=229), and VA PCS norms.......................97 5-1 TCCP and LAMP sample demographics...............................................................111 5-2 Coding structure for qualitative interviews............................................................117 5-3 Coding results from qualitative interviewees.........................................................130

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xi LIST OF FIGURES Figure page 1-1 The International Classification of Functioning, Disability and Health (ICF) comparison of LAMP and TCCP.............................................................................18 3-1 Preparation of comparison pool for final matching to LAMP and TCCP...............55

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xii Abstract of Dissertation Pres ented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy ASSESSMENT OF A TELEREHABIL ITATION AND A TELEHOMECARE PROGRAM FOR VETERANS WI TH CHRONIC ILLNESSES By Roxanna M. Bendixen December, 2006 Chair: William C. Mann Major Department: Rehabilitation Science In the United States today, over 100 m illion individuals suffer from chronic illnesses. Each year chronic illnesses account for approximately 70 percent of all U.S. deaths and 75 percent of a ll healthcare costs. Chroni c conditions often lead to disabilities, which result in functional lim itations and loss of independence, thereby increasing medical expenditures. The elderly p opulation is at a higher risk for developing chronic conditions such as diabetes, heart diseas e, or arthritis, increasing their risk for disabilities. The disability ra te of the population over age 65 is at least three times higher than the general population. Given the rapi d growth of the aging population, and the chronic illnesses, disabilities, and loss of functional independence endemic to elders, novel methods of rehabilitation and care mana gement are urgently needed. Telehealth models that combine care coordination w ith communications technology offer a means for decreasing healthcare costs and increasing patient satisf action, and have been shown to be an important component in th e management of chronic illnesses. This dissertation examined the effect s of a Veterans Administration (VA) telerehabilitation program (Low ADL Monitoring Program LAMP) and a VA

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xiii telehomecare program (Technology Care Coordi nation Program TCCP) on healthcare costs, as well as patient reported health-rela ted quality of life meas ures. Additionally, a qualitative study utilizing a random sampling of veterans enrolled in LAMP and TCCP provided patients’ perceptions on telehealth interventions, the technology used for homebased remote monitoring, and satisfac tion with VA healthcare services. TCCP is based on a medical model of care. LAMP is based on a rehabilitative model of care. LAMP patients received adaptive equipment and environmental modifications, which focused on self-care and sa fety within the home. Care-coordinators for LAMP and TCCP remotely monitored th eir patient’s vital signs, such as blood pressure and weight, and provided educa tion and self-management strategies for decreasing the effects of chr onic illnesses. Healthcare costs post-enrollment were examined through a difference-in-differences multivariable model. Results determined that there were no significant differences between LAMP and their matched comparison group, TCCP and their matched comparison group, or LAMP and TCCP, following the 12-month intervention. For TCCP patients, daily remote monitoring resulted in increases in all healthcare costs. For LAMP patient s, the provision of ad aptive equipment and environmental modifications, plus intensive in-home monitoring of patients, lead to significant increases in clinic visits post -intervention, but decreases in hospital and nursing home stays. LAMP patients also incr eased in physical functioning based on selfreport from the Veterans Quality of Life SF-12V. Through personal interviews, veterans reported increased connectedness with the VA, found the technology easy to use, were satisfied with the services, and would recommend telehealth to their peers.

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1 CHAPTER 1 INTRODUCTION The declining health of our elders is one of the greatest me dical problems and greatest economic burdens facing the U.S. today (Fries, 2002). Approximately 70 percent of healthcare sp ending in the U.S. is focused on the health of our elder population (Centers for Disease Control [CDC], 2003a). In 2005, this amounted to more than 1.3 trillion dollars, and is expected to rise to 2.5 trillion dolla rs by 2014, totaling more than 13 percent of the gross domestic product (Heffler et al 2005). Of particular concern is the increase in chronic illness and disability in our aging population, which is projected to rise sharply through 2030 as the baby boom generation enters old age (Department of Health & Human Services [DHHS], 2004). Chr onic illnesses contri bute to disability, diminish quality of life, and increase h ealth and long-term care costs (CDC, 2003b; Ostchega, Harris, Hirsch, Parsons, & Kingt on, 2000). In fact, chronic illnesses are among the leading causes of death and functiona l disability in older adults (Freedman, Martin, & Schoeni, 2002; Murray & Lopez, 1996). The aging population, especially those who are chronically ill and disabled, place a strain on healthcare resources and challenge healthcare providers. Healthcare pr ograms that could assist elderly patients in the self-management of their chronic illnesses and limit hospital and emergency room visits could potentially reduce the overal l economic burden of these diseases. The purpose of this dissertation is to examine the effectiveness of two telehealth programs within the Veterans Health Admini stration (VHA) that were designed to serve at-risk elders. A retrospective, concurrent matched cohort study design was employed to

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2 determine healthcare costs and functional health status from a telehomecare program and a telerehabilitation program. Additionally, the telehealth participants’ personal experiences were investigated through qualitative interviews in their homes. This study provides valuable information regarding telehe alth models of care that may assist in managing chronic illness and disability in our elderly population, therefore reducing health-related costs and increasing safe ty and independence within the home environment. Challenges in Healthcare Tending to the multiple disease processes that often coincide in chronically ill elders can be quite challenging to healthcare providers. Pr imary care providers are often called to concurrently manage a variety of illnesses in the same patient, requiring increasingly complex medical regimens (E. H. Wagner, 2001). The best possible outcomes depend on the delivery of a multitude of services, including preventive care, disease management, and rehabilitation. Such coordinated services hold the greatest promise for improving the health of our elde rly. Yet the provisi on of evidence-based, comprehensive care is exceedingly difficult and numerous barriers exist (Grumbach & Bodenheimer, 2002). Preventive care measures include re gular health maintenance evaluations, immunizations and vaccines, and laborato ry testing (Godfrey, 2001). Screening for additional chronic and/or life-threatening dise ases or exacerbation of an illness is also important. An essential preventive measure fo r individuals with chronic illnesses is to be knowledgeable of their health care regimens and be in regular contact with their healthcare provider. Yet it is difficult for pr imary care providers and patients to maintain contact and keep track of necessary screen ings, laboratory tests and immunizations.

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3 Additionally, elders often have functional disabilities and nu merous comorbid conditions, reducing their ability to manage their chroni c diseases (E. H. Wagner, 2001). Moreover, studies have shown that preventive care has had limited success in decreasing the incidence of chroni c illness and disab ility (Godfrey, 1999; Tu lloch, 2005; Walker & Jamrozik, 2005). The reasons preventive car e is not always successful vary. Many conditions may be overlooked by conventional car e, such as urinar y tract infections, diabetes and anemia, as well as depression and dementia (Tulloch, 2005). Therefore, individuals who have not been adequately diagnosed will not receive the necessary preventive measures for self-management of th eir disease. Furthermore, there is limited training of nurses and physicians in preventi ve care for elders, as well as inadequate health education for elders themselves (Williams, Ricketts, & Thompson, 1998). Other barriers associated with receipt of preventive services include provider continuity and site continuity, as well as inadequate health in surance coverage and difficulty traveling to visit specialists (Doescher, Save r, Fiscella, & Franks, 2004). Disease management “is a system of c oordinated healthcare interventions and communications for populations with conditions where patient self-care efforts are a significant factor in supporting the physician/patient relations hip and their plan of care” (Disease Management Association of Amer ica [DMAA], 2006). Disease management programs for patients with chronic illnesses, such as diabetes, have become increasingly common in recent years as a mechanism to he lp educate patients on how to self-manage their disease (Congressional Budge t Office [CBO], 2004; New et al 2003; Stille, Jerant, Bell, Meltzer, & Elmore, 2005). Disease manage ment programs typically include clinical guidelines for disease phases, patient educ ation for self-management, aggressive

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4 screening for complications, and coordi nation of care among numerous healthcare providers (CBO, 2004; Gamm, Bolin, & Kash, 2005). Yet, under a system designed for acute and episodic care, healthcare providers as well as patients themselves, are not always focused on disease management (B odenheimer, Wagner & Grumbach, 2002a). Additionally, the impact of disease management programs is mixed. Disease management programs are difficult to effici ently provide because they require ongoing collaboration, patient self-management educ ation, compliance, routine reporting and outcomes measurement (CBO, 2004; Le ider & Krizan, 2004; Roglieri et al 1997; E. H. Wagner, 2000). Lastly, understanding the longe r-term consequences of chronic diseases is as important as the immediate management of the disease, and deserves attention. Rehabilitative interventions are aimed at re ducing disability a nd improving independence and function (Godfrey, 2001). In rehabil itation, a multidisciplinary team works cohesively with patients to carefully assess their strengths, deficits and personal desires for achieving their highest functioning level and living an independent life. Rehabilitation is a creative and individualized process of preparing an individual with a disability to preserve or regain optimal functional independence and adapt to physical limitations and architectural barriers (Godfrey, 2001; Hochst enbach, 2000). However, obstacles exist that make it difficult for the elderly to receive adequate and timely rehabilitative services. Such obstacles incl ude availability of specialists, appropriate assessments and recommendations of services and assistive devices, and traveling to access services. Additionally, when rehabilitation is received strictly in a clinical setting, carryover into the home may be sub-optimal. De livery of care within the home is able to

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5 target key areas to stem this, yet home rehab ilitative services are rarely provided and when provided are often of an inad equate duration and intensity. Other factors also impact the ability to receive adequate and timely healthcare. There are notable healthcare disparities for i ndividuals who live in rural areas, including problems of management and provision of se rvices due to difficulties with access and transportation outside the home (Eldar, 2001; Freedman et al., 2002). These challenges are further compounded by clinic and healthcare fac ilities that have a limited number of physical locations from which they can provide patient treatment. Difficulties in access also occur due to proble ms with recruitment and retention of practitioners in rural areas. To date, little progress has been made to ward restructuring healthcare systems to address these concerns. Recent reports in healthcare trends urgently recommend an overhaul of American’s healthcare system (Bodenheimer et al., 2002a; Institute of Medicine [IOM], 2001; E.H. Wagner, 2004). Given the rapid growth of the aging population, and the chroni c illnesses, disabili ties, and loss of f unctional independence endemic to elders, novel methods of care ma nagement and care delivery are urgently needed. The Veterans Healthcare System The Department of Veterans Affairs (VA) is responsible fo r operating nationwide programs for healthcare, financial assistance and burial benefits to veterans and their families. The most visible of the VA systems is healthcare. The Veterans Health Administration (VHA) is the largest integrated healthcare system in the U.S., providing a multitude of services to over 5 million vetera ns in fiscal year 2005 (Office of Public Affairs [OPA], 2006). Because the VHA provi des a uniform and comprehensive set of

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6 healthcare benefits for their patients, it is a useful system to explore resource use and patient outcomes. The impact of aging and chronic illness in the VA In fiscal year 2005, the VHA provided medical care to over 5.3 million veterans at a cost of $31.5 billion (OPA, 2006). Much of the VHA’s medical care is focused on a rapidly aging and chronically ill veteran popul ation. The number of veterans over the age of 85 is increasing by a mean rate of 11 pe rcent a year, and is projected to reach approximately 1.3 million by the year 2010 (Yu, Ravelo, Wagner, & Barnett, 2004). Although the increasingly aging veteran population has amplified the demand for healthcare services, studies have show that the presence of chronic illnesses combined with aging has a more significant effect on healthcare costs than age alone (Asch et al 2004; Yu, 2004; Yu, et al., 2003a). Vetera n’s enrolled in the VHA report a higher prevalence of recent or long term chronic disease than their community counterparts (Asch et al., 2004; Kazis et al 2004b; Rogers et al 2004). In a recent study of prevalence and costs of chronic conditi ons in the VHA, Yu and colleagues (2004), reported that among the VA patients aged 65 and older, 85 percent had one or more chronic conditions, with 40 percent having thre e or more. Chronic illnesses, which are the main reason veterans seek care through the VHA, accounted for 96 percent of the total VA healthcare expenditures in 2000. VA telehealth applications As more veterans are facing debilitating chr onic diseases, there is a need to ensure timely access to preventive care, disease mana gement, and rehabilitative care. Beginning in April 2000, the VHA initiated funding of se veral clinical demonstration projects related to telehealth to test the integra tion of care coordinatio n with communications

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7 technology for home-based disease management (Meyer, Kobb & Ryan, 2002). The complexity of our veteran’s healthcare n eeds places greater demand on coordination of care. In the past, care or case management was defined by an episode of care, either in the clinic or hospital, typically with a se t number of phone calls to follow-up on a patient after discharge. The VA care coordination m odel combines the role of a care coordinator with home telehealth technologi es that allow for consistent follow-up that transcends clinical programs and physical settings. Th e Care Coordinator is responsible for being a team member, providing a clinic al thread between therapists, specialists and general care, and providing consistent information on the veteran’s response to treatment at home. Telehealth models which combine care coor dination with communi cations technology offer a means for decreasing h ealthcare costs and increasing patient satisfaction, and have been shown to be an important component in the management of chronic illnesses (Bennett, Fosbinder, & Williams, 1997; Hooper, Yellowlees, Marwick, Currie, & Bidstrup, 2001; Joseph, 2006; Kobb, Hoffman, Lodge, & Kline, 2003; Noel, Vogel, Erdos, Cornwall, & Levin, 2004). Today, telehe alth and the use of telecommunications technology is widely used by the VA, which views telehealth as integral to the delivery of health services and education within their syst ems. Thus, it is not surprising that the VA has placed a major emphasis on the developmen t of various in-home telehealth models of care, such as telehomecare and telerehabilitation. Models of VA telehealth care Telehomecare. Telehomecare (THC) uses technology to enable the communication and transfer of information be tween the healthcare pr ovider at a clinical site and the patient in the home (Finkelstein et al., 2004). A typical application of THC is the use of telehealth tech nology with oversight by nurse practitioners who provide

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8 medical care for chronically ill individuals within their homes (Celler, Lovell, & Basilakis, 2003; Finkelstein, Speedie, & Pott hoff, 2006; Kobb et al., 2003; Noel et al., 2004). Using telehealth tec hnology, home-based video visits and monitoring of vital signs can be accomplished electronically, me dication compliance can be verified, and patient education can be enhanced. With in the VHA, the THC model is based on the traditional medical model of care. Professiona ls working in the field of THC are skilled in the management of chronic illnesses through diagnosis, medical intervention and patient education. THC interventions are typically disease-specific and focus on the monitoring of physiologic parameters. A very important clinical goal in THC is to minimize the impact of the condition, often through symptom tracking, which results in a medical intervention. Technology Care Coordination Program The Technology Care Coordination Program (TCCP) is a VA telehomecare program that uses telehealth technology in conjunction with nurse practitioners and a social worker to coordinate care for chroni cally ill veterans living in remote areas in North Florida/South Georgia. During our study period, veterans were eligible to be enrolled in TCCP if they met the following criteria: 1. had past high-cost medical care needs (>$25,000) and high hea lthcare utilization (two or more hospitalizations and fr equent emergency room visits), 2. had electricity and phone service, 3. accepted technology in their homes for monitoring purposes, and 4. signed an informed consent form or ha d the consent form signed by a proxy. The TCCP targeted veterans with multiple co-m orbidities such as congestive health failure (CHF), diabetes, hypertension, a nd chronic obstructive pulmonary disease (COPD). The care coordination (CC) team cons isted of two nurse practitioners, a social worker, and a program support assistant for o ffice support. Veterans were identified as

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9 high-facility use, high-cost by the VHA’s comput erized cost allocation system. Veterans were then contacted by telephone to determine their interest in participating in TCCP. Following initial contact, an enrollment appoint ment was made to visit the home, explain the program, and assign and install the t echnology for remote monitoring. TCCP participant’s health-related quality of life was measured by the Veteran’s Quality of Life SF-36V Health Survey Form (SF-36V) and th e shorter version, the SF-12V (Brazier et al 1992) at baseline and ever y 6-months thereafter. Veterans enrolled in TCCP were risk-stratif ied into three levels based on severity of disease, functional and cognitive status, living situation, and t ype of residence and provided with different remote monitoring devices based on the stratification. (1) Veterans with stable chronic illnesses and psychosocial issues impacting health received a videophone. The videophone is a stand-al one device that connects to a regular telephone line and allows video and audio i nput between the veteran and the CC. (2) Veterans with frequent hospitalizations, who liv ed in a private residence, and were able to read, received a Health Buddy (HB) (Health Hero Network, Inc., Redwood City, California). The HB is an in-home messaging device that serves as the interface between patients at home and CCs locate d at the VA. The HB presents veterans with a list of questions they answer by selecting one of four options to help monitor and assess a patient's clinical condition, a nd provides education for the pati ent based on their answers. An example of a question includes, “How do you feel today?” with answers excellent, good, fair, or poor; with a follow-up response based on the answer. Another question may be, “Have you fallen?” with answers ye s or no, and a follow-up question if the answer is yes, “Do you need medical atten tion?” with answers ye s or no. Should the

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10 patient require medical attention, the HB w ill provide the patient with the phone number for the VA and alert the patient’s care coordina tor for follow-up. Patient data is sent over a telephone line through a secure data center wh ere the data is then available for review on the Health Buddy Desktop. Patient res ponses are color-coded by risk level as High (red), Moderate (yellow) and Low (green) based on symptoms, patient behaviors and self-care knowledge. (3) Veterans with frequent hospitalizati ons, who lived in a congregate or private setting, were able to hand le peripherals, and had a diagnosis such as heart failure or emphysema, received an Av iva (Centralia, WA). The AVIVA Home Telecare System consists of a central sta tion, which connects via ordinary telephone lines to the patient station, which is placed in the patient's residence. Th e Aviva functions with a PC-based monitoring station and two-way vide o to allow care coordinators to visually monitor the patient remotely. The Aviv a program provides li ve audio and video communication with a CC. Telerehabilitation. Telerehabilitation (TRH) is an emerging practice defined as the remote delivery of rehabi litation services th rough compensatory strategies, training and education, monitoring, a nd long-term care of individua ls with disabilities using assistive technology (Office for the Advancement of Telehea lth [OAT], 2002). The focus of TRH is to increase access to rehabilitation services, and to allow individuals to achieve and maintain safe and independent lives in their own homes. TRH has the potential to manage multiple components of health, incl uding functional indepe ndence, self-care and self-management of illness (Burns, Crislip, Daviou, Temkin, & Vesmarovich, 1998; Cruise & Lee, 2005; Halamandaris, 2004b; Wi nters, 2002). TRH is a rehabilitative model of care, which views hea lth as more than the absence of disease. As health is

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11 intimately related to and influenced by the environment and the pe rson’s characteristics (Brandt & Pope, 1997; Dahl 2002), many TRH programs em phasize the whole person and focus on decreasing the impact of chroni c illnesses, thereby improving health and functional outcomes. TRH assesses the immediate environment (home) and provides interventions such as educati on and training, therapeutic exer cises, adaptive devices, and simple home modifications in an attempt to improve daily function (Cieza & Stucki, 2005). The Low Activities of Daily Li ving (ADL) Monitoring Program The Low ADL Monitoring Program (LAMP) is a VA telerehabilitation program designed to promote independence and reduce healthcare costs. LAMP services are home-based and use a combination of tradit ional and advanced technologies to promote independence and the maintenance of skills necessary to remain living at home. Occupational therapists (OT) serve as car e coordinators for veterans, and work collaboratively with healthcare providers, rehabilitation specialists and other clinicians, as well as with families and caregivers. LAMP interventions range from the provision and installation of assistive technology/adap tive equipment (AT/AE) and modifications of the home environment to daily therapeuti c regimens, and on-going support for selfcare needs. LAMP staff also pr ovides hands-on and remote trai ning in the use of AT/AE. For our study period, participants were eligible to be enro lled in LAMP if they met all of the following criteria: 1. lived at home, 2. had a functional deficit with at least two ADL’s, (transferring and mobility are considered ADL’s for the purpose of inclusion), 3. had electricity and phone service, 4. accepted technology in their homes for monitoring purposes, and 5. signed a consent form or had the consent form signed by a proxy.

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12 The LAMP target population included vetera ns with multiple co-morbidities such as arthritis, diabetes hypertension, and stroke. The LA MP CC team consisted of two licensed OTs, a technology expert also assisted with technolo gy installation and training, and a program support assist ant provided office support. Following eligibility determination, a licensed OT conducted a physical/functional, c ognitive, and home assessment in each of the study participants’ homes. The assessment included instruments that measured functional indepe ndence, cognition and qua lity of life. Two instruments were used to measure functiona l status: the Older Americans Research and Service Center Instrumental Activities of Daily Living (IADL) (Fillenbaum, 1988), and the motor subscale of the Functional Indepe ndence Measure (FIM) (Fricke, Unsworth, & Worrell, 1993; Pollak, Rheult, & Stoecker, 1996). Mental st atus was evaluated through the Mini Mental Status Examination (M. Fo lstein, S. E. Folstein, & McHugh, 1988) and the cognitive subscale of the FIM. Health-r elated quality of lif e was measured by the Veteran’s Quality of Life SF-36V Health Surv ey Form (SF-36V) a nd the shorter version, the SF-12V (Brazier et al 1992). Veterans enrolled in LAMP received functional, cognitive, and health-related qua lity of life measurements in their home at baseline and 12-month follow-up. A comprehensive home assessment was conducted and included evaluation of the home’s exterior and interi or, focusing on accessibility and safety. Subsequently, care plans were developed based on information obtained from these assessments. Care plans included the type of adaptive equipment needed to increase safety and independence within the home, the type of technology to be used for remote monitoring, and health-related diagnostic parameters. An additional home visit for installation and training on each piece of equipment was required.

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13 Three different communicati ons systems were used for LAMP remote monitoring: (1) a basic computer with internet capabilit y, (2) a smartphone (cell phone) with internet capability, and (3) the HB. Veterans who met criteria for computers or smartphones demonstrated either past computer knowledge or the cognitive and physical abilities necessary for computer or smartphone use. Motivation to learn and use the computer or smartphone was also considered. Veterans w ho did not meet criteria for computers or smartphones received a HB. The HB was inst alled during the initia l evaluation, whereas additional home visits were required for installation and traini ng on the use of the computer or smartphone. Veterans who require d more than 3 home visits for computer or smartphone training were switched to a HB. LAMP was based on preliminary work performed by Mann and colleagues (Mann, Hurren, Tomita & Charvat, 1995; Mann, Marchant, Tomita, Fraas, & Stanton, 2001; Mann, Ottenbacher, Fraas, Tomita & Grange r, 1999) which showed that functional decline may be attenuated through the provision of AT/AE. LAMP services were based on the experience of Mann’s 3-year National Institute on Disability and Rehabilitation Research (NIDRR) funded study where frail elders were provided adaptive equipment and monitored for self-care needs using computers with video-teleconferencing capability. Results from their study demonstrat ed that frail elders experienced functional decline over time, but indicated that compared to a control group the ra te of decline could be slowed, and institutional and certai n in-home personnel costs reduced, through a systematic approach to providing AT/AE a nd home modifications. Other studies have also demonstrated that the use of AT/AE can provide assistance for individuals with disabilities (Berry & Ignash, 2003; L.M. Verb rugge & Sevak, 2002; Gitlin, et al, 2006).

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14 Daily Remote Monitoring by LAMP and TCCP Daily remote monitoring comprises a multi-component, chronic disease management model through the review of pe rsonal health dialogues. TCCP and LAMP daily remote monitoring included patient asse ssment based on a vari ety of health-related diagnostic parameters, such as blood pressure or blood sugar readings. Disease-specific education was provided based on individual h ealthcare needs. Patient adherence to medication and treatment plans was also a ddressed. Maintaining daily contact with telehealth patients allowed for comprehe nsive patient-provider communication, and follow-up support. LAMP patients were as sessed daily on the same health-related diagnostic parameters as the TCCP patient s, but were also monitored on self-care parameters and the promotion of therapeutic lifestyle changes. LAMP daily self-care reports included information on falls, self-care and mobility throughout the home environment, as well as the ability to get out side of the home and participate in leisure and social activities. Communications techno logy provided both LAMP and TCCP Care Coordinators (CC) with the necessary informa tion to evaluate health status and provide immediate intervention and ongoing care management through the VA. Care management is important for accessible, coor dinated, and continuous healthcare across all settings, especially the home. Theoretical Model In all areas of healthcare theoretical models and frameworks are important for clinical practice, research and education. The World Health Organization (WHO) International Classification of Functioning, Di sability, and Health (ICF) is a framework designed to classify health and health-related states (Wor ld Health Organization [WHO], 2001). The ICF has broad applications to a variety of areas in medicine and

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15 rehabilitation, and provides the basis for unde rstanding the interrelationships between the person, the environment, health, and function. The ICF allows us to illustrate the instrumental similarities and diffe rences between THC and TRH. The International Classification of F unctioning, Disability and Health model The ICF is considered a biopsychosocial mode l, as it integrates the medical model, the disability model, and the social model to view health as being influence by the condition, the person, and the e nvironment (WHO, 2001, 2002). The ICF has shifted the focus from health as a “consequence of disease” to a “components of health” classification (2001). The ICF provides a scientific basis for viewing and studying all health conditions, allowing them to be compared using a common measure of function and disability (D ahl, 2002). Part I of the ICF framework focuses on Functioning and Disability; Part II of the ICF relates to Contextual Factors. Both Part I and Part II have interrelating components, and functioning and disability are seen as outcomes of interactions between both parts. The two interrelating components within Functioning and Disability are “body f unctions and structures ”, and “activity and participation.” Interrelating components w ithin Part II, Contextual Factors, are comprised of “environmental factors” and “per sonal factors.” To classify each of these components, the ICF uses qualifiers. Qualifie rs allow one to measure the presence or severity in the level of f unctioning within the body, pers on and society. Therefore, function is not limited to a single domain but is a dynamic blending of components across domains. This indicates that the true marker of success fo r any individual undergoing the process of rehabilitation is not only regaining physical and cognitive function, but by participation in life activities (Fougeyrollas & Beauregard, 2001).

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16 The ICF allows for the measurement and re porting of health at an individual and population level, and has been used in the ev aluation of numerous healthcare systems and the study of healthcare interv entions (Arthanat, Nochaj ski, & Stone, 2004; Bilbao et al 2003; Haglund & Henriksson, 2003; Lo max, Brown, & Howard, 2004; Mayo et al 2004; Stamm, Cieza, Machold, Smolen, & Stucki, 2004; Stucki et al 2002a; Stucki, Ewert, & Cieza, 2002b; Weigl et al 2003). The ICF is an important component for healthcare policy design and implementation. The implem entation of new strategies in healthcare requires coordinated efforts and si gnificant investment in researc h. This is especially true of applications in telehealth. Telehealth / ICF framework Chronic illnesses and their impact on veterans and the VA healthcare system are the focus of this study. Chronic illnesses re late to the ICF compone nt of Functioning and Disability. Chronic illnesse s involve domains within body functions and body structures such as the cardiovascular and respiratory systems, as in hypertension or chronic lung disease, neuromusculoskeletal and movement systems involved with osteoarthritis, and the metabolic system related to diabetes me llitus. The ICF compone nt of Activities and Participation are likely to be negatively affected through impairments within the body functions and structures, which often accompa ny chronic illnesses. Such impairments may limit one’s ability to independently perfor m self-care, engage in work, or spend time with family and friends. Contextual Factor s includes environmenta l factors and personal factors. Environmental factors include in terventions within the home environment, assistive devices and technology, and support an d relationships that may be enhanced through care coordination. Pe rsonal factors vary based on one’s particular background and are not a feature of a chr onic or disabling illness. In this study personal factors

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17 include age, marital status, and disease state. The ICF model demonstrates that chronic illness related impairments, combined with environmental and personal factors, may decrease one’s ability to function within the home. TCCP uses the medical model in conjunction with communications technology to coordinate care for chronically ill individuals. The medical model of care places an emphasis on diagnosing and successfully treati ng a disease. Functio ning and health are seen primarily as a consequence of a disease. This disease-specific model dominates the healthcare system (Kaplan, 2002). TCCP fo cuses on minimizing the impact of the condition through symptom tracking and the provision of a medical intervention. However, symptom tracking alone may not give us an accurate picture of how a chronic illness actually affects a person’s everyda y life (Kaplan, 2002; Ustun, Chatterji, Bickenbach, Kostanjsek, & Schneider, 2003). Ch ronic conditions are usually not cured, and require ongoing disease management, patient education, and provi sion of resources to assist patients to cope with the impact of the illness. The rehabilitation model of care is a continuous process that ranges from identifying difficulties and needs, relating the difficulties to impaired body functions and structures, targeting the person and the e nvironment through interventions, and managing the interventions (Stucki et al., 2002b). Ther efore, the severity of an illness may be reduced through the provision of environmental modifications and adaptive devices to remove the limitations that alter functioning. LAMP uses the rehabilitation model to coordinate care for chro nically ill indivi duals through assess ing personal and environmental factors in order to prov ide the appropriate technology for remote monitoring, as well as modifying the immedi ate home environment through the addition

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18 of grab bars to a shower st all, recessed doorways for accessibility, or ramps for entrance into a home. From the LAMP perspective, f unction is not only an outcome, but also an important component of assessment, interven tion, and quality of care (Cieza & Stucki, 2005). These factors can be put into perspe ctive through the use of the ICF model. Figure 1-1. The International Classification of Functioning, Disability and Health (ICF) comparison of LAMP and TCCP Specific Aims The proposed study will evaluate a tele homecare and telerehabilitation model of care for chronically ill veterans using both quantit ative and qualitative methods. Telehomecare and telerehabilitation strategies will be assessed from multiple perspectives including cost effectiveness, f unctional/health status, a nd patient satisfaction with telehomecare and telerehabilitation models. The purpose of this dissertation is to expl ore differences in health-related outcomes and costs between (1) veterans enrolled in a telerehabilitation intervention (LAMP); (2) Body Structure and Body Functions LAMP and TCCP Cardiovascular, Neuromuscular, Metabolic Activities LAMP ADL’s and IADL’s including Bathing, Mobility, Getting Outside of the Home Participation LAMP Self-care, Social Participation, Work Environmental Factors LAMP and TCCP Adaptive Equipment, Assistive Technology, Healthcare Support Personal Factors LAMP and TCCP Age, Marital Status, Diagnoses Health Condition (Disorder/Disease) Chronic Illness and Disability

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19 veterans enrolled in a telehomecare interven tion (TCCP); and (3) veterans who receive VA standard care without a telehomecare or tele rehabilitation intervention. In addition, we will qualitatively explore the “experience” of a telerehabilitation and a telehomecare intervention through personal stories from veteran telehealth enrollees. By targeting veterans with chronic illnesse s and disabilities, we anticipate that the provision of compensatory strategies (adap tive equipment) and ho me monitoring through communications technology will proactively manage the consequences of chronic illnesses, increase safety and independence, and thereby enhance functional independence and reduce institut ional care and other healthcare costs. This study is an important addition to the limited research ava ilable, as it combines both a cross-sectional qualitative analysis with retrospective quant itative analyses utili zing longitudinal (1-12 months) data. For these hypotheses, VA services are de fined as costs for hospital bed days of care, emergency room visits, nursing home be d days of care, and clinic visits. Four groups of veterans will be compared: (1) veterans receiving the LAMP intervention (telerehabilitation); (2) veterans receiving the TCCP intervention (telehomecare); (3) a comparison group of veterans matched to LA MP and TCCP based on primary diagnoses, number of hospital bed days of care 12-mont hs pre-study, and demographic variables of age and marital status. Specific Aim 1 : To quantify the effect of telereha bilitation and telehomecare in reducing healthcare costs among the four groups of veterans. Hypothesis 1: Veterans enrolled in LAMP, vete rans enrolled in TCCP, and their corresponding matched group of veterans w ho have not received telerehabilitation

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20 or telehomecare interventions will differ in their use of VA serv ices and healthcare costs. Specific Aim 2 : To define the effect that telereha bilitation exerts in promoting functional independence by comparing functional health status measurements within and between the two telehealth groups. Hypothesis 2: Veterans enrolled in LAMP a nd veterans enrolled in TCCP will differ in functional health status follo wing a 12-month enrollment period. Specific Aim 3: To evaluate the effect of telerehabilitation and telehomecare interventions on satisfaction with VA services. Research Question (Qualitative Data): How do veterans and their caregivers describe their experiences with telere habilitation and tele homecare interventions? This study utilized existing quantitative da ta sets comprised of patient’s medical history, clinical assessments, and VA health-re lated expenditures for chronically ill and disabled veterans enrolled in telerehabil itation, telehomecare or receiving standard VA care. We examined relationships between th e four groups of veterans based on costs of healthcare services, as well as LAMP and TCCP patient reported heal th-related quality of life measures. Additionally, as patient’s perceptions of disease, illness and health have been deemed critically important by the VA (Kazis et al., 2004b), a qualitative review was initiated utilizing a sampling of veterans enrolled in a telerehabilitation (LAMP) and telehomecare (TCCP) intervention. Summary The special care needs required for individua ls with chronic illness and disabilities, coupled with the VA integrated healthcare system, make it an excellent model for studying care delivery innovations. Thoughtful ev aluation of telehealth models will help

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21 to clarify potential roles of telehomecare a nd telerehabilitation interventions to reduce chronic illnesses and disabil ities and enhance safety and independence in the home. Outcomes from this study will allow assessment of the impact of veteran’s illnesses and physical impairments on system utilizati on. The cost analysis permits identification of the benefits of the teleho mecare and telerehabilitation syst ems compared to usual care. Results from this study will help advance knowledge and promote innovations that will contribute to optimal care of chronically ill and disabled veterans who are living at home.

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22 CHAPTER 2 REVIEW OF THE LITERATURE Aging, Chronic Illness and Disability According to the 2000 U.S. Census, more than 35 million people in the United States are aged 65 and older (Gist & Hetzel, 2004). This constitutes approximately 12.4 percent of the total U.S. populat ion. The number of elders aged 85 or older is the fastest growing cohort. By the year 2010, the 85+ population is expected to reach 6.1 million and account for approximately 1.2 percent of the total U.S. population (DHHS, 2004). Paralleling this population increase is the pr ojected increase in the numbers of elderly with poor health (DHHS, 2004). Illnesses aff ecting the elderly impact life expectancy and healthcare costs consider ably, placing more and more demands on the public health system and on medical resources (Joyce, Keeler, Shang, & Goldman, 2005). The majority of healthcare resources for the el derly are now devoted to the treatment of chronic conditions (CDC, 2003b). Total health care costs for individuals with chronic conditions are more than five times higher than healthy individuals (Partnership for Solutions [PFS], 2004). The elderly population is at ri sk for developing chronic conditions such as diabetes, heart disease, and arthritis. Chronic c onditions are the leading cause of death and disability in the elderly, accounting for appr oximately 70 percent of all deaths and 75 percent of all healthca re costs (CDC, 2003a). The disab ility rate of the population over age 65 is at least three times higher th an the general population (Chan et al 2002; Gist &

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23 Hetzel, 2004). Disability cause s functional limitations in activ ities of daily living (ADL), such as walking, transferring, bathing and to ileting. Approximately 43 percent of those individuals over age 65 report difficulties with self-care and mobility activities within the home (Gist & Hetzel, 2004). A systema tic review of literature by Freedman and colleagues determined a correlation between ag ing, chronic illnesses and disabilities and the need for personal assistance with da ily living tasks (Freedman et al., 2002). Difficulties performing ADLs, such as bathing or ambulating, generate the need for personal assistance or placement in a reside ntial facility and significantly increase medical expenditures (Gill & Kurland, 2003; Na ik, Concato, & Gill, 2004; Ostchega et al., 2000). Chan, et al. (2002) reviewed disability and healthcare costs and determined that functional limitations in ADLs may be an independent risk fact or for increases in healthcare expenditures. The au thors reported that the total mean healthcare costs for the most disabled (i.e., those reporting 5-6 ADL limitations) was more than seven times higher than for individuals wit hout functional limitations. Environmental contributors to functional decline Functional limitations imposed by chronic c onditions threaten an elders’ quality of life and the ability to age safe ly and independently. It is well-known that elders and those with disabilities prefer to remain in their homes and live autonomously (Bayer & Harper, 2000; Tang & Venables, 2000). Research ha s shown that the provision of adaptive equipment and home modifications may allow elde rs to perform self-care tasks at close to their highest ability and decrease the n eed for personal assistance (Gitlin et al 2006). The use of adaptive equipment and home m odifications that target environmental contributors to disability a nd functional decline have been shown to compensate for declining abilities for elde rs (Kraskowsky & Finlays on, 2001). Although there is no

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24 single approach that can address all functi onal limitations, numerous studies have shown the positive effect of adaptive equipment a nd home modifications when focused on areas that therapists and elders togeth er identify as problematic (Cumming et al 1999; Gitlin et al., 2006; Hoenig, Taylor, & Sloan, 2003; Mann et al., 1999; Ti nker & Lansley, 2005; Verbrugge, Rennert, & Madans, 1997). An incr easing percentage of elderly manage their ADL difficulties with the use of adaptive equi pment, especially in the areas of bathing, toileting and mobility (Spillman, 2004). E nvironmental modifications, such as the addition of grab bars in the bathroom, increa se safety and decrease the risk of falls. Assistive devices and environmental modifications have been found to help conserve energy and time, and provide a sense of secu rity (Kraskowsky & Finlayson, 2001; Tinker & Lansley, 2005). Moreover, the use of adaptive equipment and environmental modifications enable elders to re main in their own homes longer. Functional difficulties within the home e nvironment deserve attention from the medical community (Gitlin et al., 2006). Reha bilitation specialists, such as occupational therapists (OT), recognize the importance of ameliorating functional difficulties that may result from a mismatch between the elderly person and their home environment, resulting in the risk of accidents, such as falls. F unctional difficulties serve as eligibility criteria for home-based OT services, yet such serv ices are seldom provided unless an acute medical episode or hospital stay triggers a re ferral. Additionally, se rvices are often shortterm and focus on acute care goals in lieu of the long-term needs imposed by chronic illnesses. Such issues challenge rehabilitation efforts and increase individuals’ health risks and access to healthcare se rvices (Demiris et al., 2004). Improvements in quality of care should be aimed at an elde rs’ desire to remain independent and live at home, as well

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25 as control healthcare costs. By focusi ng attention on chronic conditions, functional limitations, and access to healthcare services, si zeable improvements in the quality of care should be achievable (Bodenhe imer et al., 2002a; IOM, 2001). Access to healthcare services Elders with chronic illnesses and disabi lities strain health care resources and healthcare providers. This economic strain and profit-driven hea lthcare systems have lead to cost containment efforts, limiting ac cess to services and co mpromising quality of care. The majority of elderly patients with chronic illnesses present with difficulties accessing care in a timely manner which in creases their risk for disabilities (Bodenheimer, Wagner, & Grumbach, 2002b; PFS, 2004). Access to healthcare may also be due to the problems of transportation and distance, as well as understaffed clinics and rehabilitation facilities. The Institute of Medi cine (IOM) defines qual ity of care as being contingent on access to healthcare in a timely and equitable manner (Hawkins & Rosenbaum, 2005; IOM, 2001). The failure to receive timely and ongoing care for chronic conditions can lead to serious health consequences a nd result in higher healthcare expenditures. Numerous factors exist which limit access to healthcare. Many elderly reside in rural communities with limited av ailability of adult specialty services, such as psychiatry, neurology, comprehensive wound care, and rehab ilitation. Rural areas frequently require long-distance travel by patie nts and their home healthcare providers. A recent study determined that the health of individuals w ho live in rural areas is worse than those who live elsewhere, even after adjusting for socioeconomic factors (Weeks et al 2004). Barriers to healthcare for rural-dwelling patie nts include geographic isolation, functional

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26 isolation, economic barriers, a sc arcity of health professions or a combination of these factors. Evidence has shown that responding to a pa tient’s needs in a timely fashion can improve the management and qua lity of thei r care (Balas et al 2000; Goldsmith, 2000). Timely and equitable access to care may require that we view the delivery of healthcare in a different way. One method to increase ava ilability of specialty healthcare services and provide timely access to healthcare is to expand the capacity of healthcare centers through the use of information technology. Information technology Information technology (IT) uses technology applications to manage and process information. Historically, the healthcare sector has used IT for administrative tasks, such as billing and inventory, but its use in the ar ea of clinical care has been limited. IT can play a critical role in the effective and effi cient delivery of clinical care. IT allows healthcare providers to systematically gather, process, analyze, communicate, and manage patients and patient data (Kelley, Moy, Stryer, Burstin, & Clancy, 2005). Telecommunication is defined as the use of technology to transfer information over a distance. Telecommunication has been used to quickly provide essential patient data to healthcare providers at a distan ce. Both patients and health care providers benefit through the use of telecommunications for immediate access to automated c linical information, diagnostic tests, and treatment results. Tel ecommunication has also been used to assist healthcare specialists in educating and tr aining new practitioners who may be in a different room of a building, a different state, or even a di fferent country.

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27 Table 2-1. Health-related appli cations for information technology* Health-related Areas Applicati ons for Information Technology Financial & Administrative Enrollment of patients Scheduling of appointments Billing for services Payment of providers Clinical Care Access to information for diagnoses Care delivery Reminders and alerts (re: vaccines, etc.) Video-based medical consultation Consultation with specialists Patient monitoring: in-home (monitoring vital signs, etc.) Disease Management Patient education Transfer of medical records/images Professional Education Medical literature searches Accessing reference material Distance education Consultations Credentialing Consumer Information & Health Online searches for health information Searches for doctors or health plans Health insurance benefits information Participation in support and chat groups Self-monitoring Access to personal health records Purchase products Medical consults (2nd opinions) Email between patient and provider Clinical trial information Public Health & Homeland Security Incident reporting Integration of data sources Videoconferencing among public health officials Surveillance (diseases or epidemics) Delivery of health alerts Response to bioterrorist attacks Research Enrolling patients in trials Collection of data Collaboration with colleagues Transfer of large data sets Searches of large databases Literature searches Outcomes measurement *Adapted from the National Research Council, 2005

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28 Numerous IT applications ar e currently available for hea lthcare providers. Table 21 provides a listing of health-re lated applications for IT (National Research Council, [NRC], 2005a). Benefits to the use of IT Despite the fact that learning to live with and manage a chronic disease or disabling condition is an important aspect of aging, curr ent medical care and he alth education does not adequately address this issue (Nodhturft et al 2000). IT can support selfmanagement of chronic illnesses through edu cation and collaboration from healthcare providers. IT has the potential to assist pa tients to learn the skills needed to manage illness, making healthcare more patient-centered. Numerous IT applications are currently being used to bring healthcare into the hom e and reduce the need for clinic care and inpatient services. Whereas clinical instru ction and intervention traditionally occur in hospitals and clinics, a grow ing range of information tec hnologies are being utilized, including remote monitoring, interactive videoconferenci ng and web-based e-Health applications. IT devices are increasingly employed to help gather, send and manage large amounts of health information needed to assi st both caregivers and pa tients in their selfcare efforts. The ability to remotely and timely mon itor patients’ physiological parameters, provide patient education and intervene quickly is essential for quality of care. Homebased monitoring can provide healthcare provi ders with daily information about their patient’s health, allowing for quick res ponse to healthcare needs. Home-based monitoring can provide patients with custom ized health education, access to providers, and support for their healthcare needs. Po tential advantages of using technology to deliver patient education incl ude its immediate availability, consistency of instructional

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29 content, increased accessibility, a private l earning environment within the home, and the ease of reinforcement of learning (Dang, Ma, Nedd, Aguilar, & Roos, 2006; Lewis, 1999). Additionally, monitoring healthcare needs and trends over time allows healthcare providers to determine the programs that are more effective and cost efficient. Home monitoring by the healthcare team can dete ct and remediate functional and health problems before they spiral out of cont rol, improving access, effectiveness, and efficiency of healthcare services. Although the potential for the use of IT in the healthcare industry is tremendous, barriers continue to exist. Barriers to the use of IT Currently, the internet offers enormous pot ential to make the delivery of healthcare more timely and patient centered. Yet many healthcare settings lack basic computer systems or support the use of the internet for information or decision making (NRC, 2005a). To date, email is only used sporad ically between patien ts and healthcare providers, but the interest is growing. Moreover, clinicians and patients have varied experience and comfort with IT and both may be wary of adopting the use of IT for healthcare delivery. A major impediment is both patients’ and healthcare providers’ concerns about privacy and confidentiality of data. The U.S. has issued neither national standards regarding the protection of health data, nor policies for the collection, storage and processing of health data through communicatio ns technology. Although this is viewed as a barrier to the use of IT, pr oponents of IT fear that enactm ent of stringent privacy rules and regulations may impede the integration a nd success of IT applications addressed to meet the quality needs of the current h ealthcare system (Detmer, 2000; DHHS, 2000).

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30 IT proponents also believe expansion of IT for healthcare delivery is impeded by reimbursement policies of the federal govern ment and private insurers. Healthcare payers, government and private, are reluctant to cover IT services as a part of health insurance because of the uncer tainty about efficacy and co st (Hersh et al., 2001). The demands for immediate financial returns by pr ivate industry and sponsoring organizations have precluded large-scale and long-term coordinated re search efforts (Krupinski et al 2002). The IOM (2001) reports that a challenging barrier to the establishment of IT applications in healthcare “r elates to human factors” [pg. 174]. Widespread adoption to the use of IT for healthcare delivery may requi re behavioral adaptations on the part of both the patient and the hea lthcare provider. Many of the concerns voiced by both clinicians and patients focus on the loss of face -to-face interactions and the demise of the patient-clinician relationship. Mangusson and Hanson (2003) view the debate as a moral and social one, stating that analysis should qualitatively eval uate complex issues relating to quality of life, as well as job satisfacti on for the healthcare prof essions. Research has shown positive views from patie nts and their healthcare provi ders. Hebert and Korabek (2004) determined through focus groups and inte rviews that patients were positive about the potential of technology to support their inde pendence, increase self-control over their care, and provide access to services. Nurses in their study felt improved outcomes would result from the provision of disease-manage ment education, and frequent monitoring and timely interventions would result in health im provements. Physicians were more reticent about the reliability and accura cy of the technology for assessing patients, and were concerned about reimbursemen t, liability and training (Hebert & Korabek, 2004).

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31 A further impediment to the use of IT in healthcare is the paucity of reliable information on the costs and benefits. The id ea that the use of IT can improve care and lower costs through fewer office visits and tim ely medical interventions has yet to be fully tested in rigorous settings. Before IT can become widespread in healthcare, research on technologies and the evaluation of health applications must be achieved. Although funding has been limited for large scal e studies, “IT demonstration projects can serve as venues for continued identificati on of technology needs” [page 257] and develop standards for the provision of hea lthcare services (NRC, 2005b). Telehealth Applications Telehealth, an approach that connects i ndividuals with their healthcare providers through the use of telecomm unications technology, addr esses many of the abovementioned aims. The 2001 Report to Congress on Telehealth defines telehealth as the “use of electronic information and telecomm unications technologies to support longdistance clinical heal th care, patient and professiona l health-related education, public health, and health administration” (OAT, 2002) [page 1]. Specialized medical devices, video-conferencing, computer networking, and software management systems allow for the evaluation, diagnosis and treatment of pa tients in locations such as their homes. Medical applications of telehealth are numerous. The main objectives include: More equitable distribution of healthcare through increased access to services for individuals with disabilities and others for whom access is difficult (i.e. rural areas). Removing the barriers of distance, time and travel from healthcare. Cost effectiveness by avoiding unnece ssary emergency room visits and hospitalizations. Preventative medicine and early interventi on of medical complications that might otherwise go unreported.

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32 Better diagnostic and prognostic capabiliti es, as patients submit vital health information daily, allowing for tracking of trends. A holistic team approach, which may comprise physicians, nurses, therapists, psychologists, and social workers. Patient-centered treatment and increased patient compliance, as patients are more aware of their vital parameters (blood pressure, blood glucose levels, body weight and temperature) and able to become activ ely involved in the process of managing their care and treatment interventions. Promotion of independence through maintena nce of life at home; enhanced quality of life through prevention of chronic illnesses (Celler et al., 2003; Hersh et al., 2001). Telehomecare Home-based telehealth applications, or telehomecare, represent a special application of growing significan ce. One of the central driving forces for telehomecare is the elderly patient’s wish to remain safely at home for as long as possible. The home, therefore, is becoming an increasingly im portant location for “care and cure” (Tang & Venables, 2000) [pg 8]. Using telehealth technology, home-base d video visits and monitoring of vital signs can be accomplishe d electronically, medication compliance can be verified, and patient e ducation can be enhanced (F inkelstein et al., 2004). Through telehomecare, remote health devices can record and transmit vital information such as blood pressure, blood gl ucose levels and electrocardiograms from home-based clients (Nakamura, Takamo, & Akao, 1999; Tsang et al 2001). Home monitoring can link patients to clinics, physician offices, di sease management companies, and home care agencies for the purpose of streamlining care delivery, maintaining a closer patient connection, and monitoring ea rly changes in patien t status (Field & Grigsby, 2002; Frantz, Colgan, Palmer & Ledgerwood, 2002). Monitoring devices typically incorporate alert syst ems that allow for rapid detection and treatment of early

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33 signs and symptoms of instabil ity. Home health devices of ten provide the patient with the education necessary for di sease-management and long-term compliance. As patients are responsible for ensuring that accurate info rmation is submitted, telehomecare requires that patients assume much greater roles in th e treatment and care of their chronic illnesses (Holman & Lorig, 2000). Although telehomecare has the potential to assi st elders in the self-management of their chronic illnesses, and in turn reduce healthcare costs, randomized controlled trials to te st this proposal are lacking. Telehomecare research studies As referrals for home health services con tinue to escalate, hea lthcare organizations are encouraged to seek more effective met hods for providing patient care and saving costs. In a landmark study of home health services by Kaiser Permanente Medical Center, positive health outcomes were reporte d in terms of quality, patient satisfaction, and cost savings (Johnston, Wheeler, Deuser, & Sousa, 2000). More than 22 percent of Kaiser’s enrollees have diagnosed chronic illnesses, and generate 47 percent of the emergency room visits and approximately 75 percent of the non-obs tetric hospital bed days of care (Bodenheimer et al., 2002a). Kaiser randomiz ed 212 patients into control and intervention groups, each receiving routine home healthcare (home visits and telephone contact). In addition, the inte rvention group was provided with access to a remote video system, which allowed nurses an d patients to interact in real time, and provided peripheral equipment for assessing v ital health information. Remote video technology in the home healthcare setting was s hown to be effective and well received by patients. Following the 18 month observationa l study, total cost savi ngs of approximately $900 per patient in the intervention group was reported, when controlling for equipment

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34 costs and depreciation. Based on these findings Kaiser Permanente is now integrating telehomecare services within its orga nization (Johnston et al., 2000). Additional smaller studies have compared conventional home h ealthcare services with the use of home-based telecommunications equipment for remote monitoring. Nakamura (1999) evaluated the effect of hom e healthcare compared to home healthcare with the addition of a videophone. The vide ophone allowed patients to receive remote medical assessments and consultation rega rding health problem s, ADLs, physical exercise and nutrition, as well as emotional suppo rt for patients and caregivers. Patients and providers responded to quest ionnaires at the end of th e study, which determined a potential benefit in the use of the vide ophone in terms of improving communication and offering better assistance. As has been noted in numerous other studies (Hebert & Korabek, 2004; Magnusson & Hanson, 2003; Nelson, Citarelli, Cook, & Shaw, 2003; Williams, May, & Esmail, 2001), both the particip ants and the home health professionals felt that services via videophone could s upplement but not repl ace all face-to-face healthcare visits. Many telehomecare applications focus on specific healthcare needs, such as individuals with congestive he art failure (CHF). CHF is one of the most common causes of hospitalization due to exacerbation of a chronic condition among adults aged 65 years and older in the U.S. (Scalvini et al 2004). Through a randomized controlled trial (RCT), the U.S. Department of Commerce is examining the benefits of using low-cost telecommunications and monitoring technologi es for homebound fra il elders needing skilled home heal thcare (Demiris et al., 2001; Finkelstein et al., 2004). The study is focusing on elders with CHF, chronic obs tructive pulmonary disease (COPD), and

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35 chronic wound-care, but final results have ye t to be published. Outcome measures will evaluate mortality and morbidity, length of time to transfer to a higher level of care (e.g., hospitalization or long-term car e facility), subject percepti on of telehomecare, subject satisfaction with care and tec hnology, quality and clinical us efulness of virtual visits, utilization of services, and cost s for both subjects and service providers. At this point in time, initial information from the study ha s shown that elderly patients can use the technology successfully, are satisfied with the ca re they receive, are confident in handling the technology, and are accepting of the underlyi ng concept of telehomecare. Roglieri and colleagues presented a multicenter, l ongitudinal comparison of a comprehensive CHF disease management program focused on patients with pure CHF and CHF-related diagnoses. The impact of telemonitori ng of CHF patients and post-hospitalization follow-up in a managed care setting was evalua ted. The researchers report significant cost savings for participants based on redu ced hospital admissions and readmission rates, length of stay, and emergency room utiliza tion (Roglieri et al., 1997). Dimmick et al. (2003) discussed the establishment of a CHF disease management telehomecare program as part of an integrated tele health network that linked three hospitals, a federally qualified healthcare clinic with six sites, a county dent al clinic, and patients from nine different counties and two states. In lieu of provi ding specific information regarding this CHF program, the authors analyzed labor and equipm ent costs and estimated cost savings on a national scale, projecting that the national co sts of care for CHF hosp italizations could be reduced from $8 billion to $4.2 billion annually. The University of California at Davis Hospital (UCDH) studied 3 groups of individua ls with the diagnosis of CHF (Jerant, Azari, & Nesbitt, 2001). All groups were pr ovided with standard healthcare, a second

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36 group also received a weekly telephone call, and the third group was provided with a videophone and remote health monitoring equi pment. Differences were not detected between the telephone and telehomecare groups, but trends were seen toward fewer CHF related and all-cause readmissi ons, and shorter mean length of stay in both the telephone and the telehomecare intervention groups compared to standard care. Although the researchers discussed charges as primary and secondary outcome s, true cost figures were not provided. This study is one that questions whether more expensive telehomecare programs offer any incremental benefit beyond telephone follow-up. Diabetes is another significant chronic illn ess, which is costly and common in the elderly. The high prevalence and complexity of diabetes poses major clinical challenges which may be attenuated by telehomecare (Shea et al 2002). An ongoing RCT is Columbia University’s Informatics for Diabetes Education and Telemedicine (IDEATel) Project. IDEATel is a f our-year demonstration projec t funded by the Centers for Medicare and Medicaid Servi ces (Shea et al., 2002). A to tal of 1,500 participants have been randomized to a telehomecare intervention (n=750) and a control group receiving standard care (n=750). IDEATel is a large, complex project desi gned to provide data relevant to policy formation for the use of telehomecare in diabetic management. Outcomes from this study should provide si gnificant information regarding the use of telehomecare for the management of diabetes in the elderly population. Integrated telehealth networks have been designed to assist diabetic pa rticipants manage their care through telehomecare support systems (Dimmick et al., 2003; Shea et al., 2002). In Dimmick et al., participants were given a bl ood glucose monitor that used telephone lines to transmit values to a hea lth clinic. Although their sample size was small (N=36),

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37 researchers reported progress in achieving better blood sugar c ontrol by participants. The researchers felt that a key outcome in this demonstration project was the ability to provide support and incrementa l education over time so that participants learned to manage their chronic health problems. The TeleHomecare Project is a partnership between the Pennsylvania State University, the Visiting Nurses Association (VNA) of greater Philadelphia, and the American Tel ecare, Inc. (ATI) (Dansky, Palmer, Shea, & Bowles, 2001). The TeleHomecare Project was designed to test the effects of telehomecare on quality and financial costs asso ciated with care for elderly diabetic patients. All costs were examined, both di rect and indirect, but the focus was costs occurring at the home health agency level. Researchers provide a specific cost breakdown for each component of the telehomecare services, including home visits, video visits, training and meeting time, and equipment. Reported outcomes focused on the percent of patients who were discharged from home healthcare (64 percent of the telehomecare group compared to 39 percent of the control group) and the percent of patients who were readmitted to the hospital (10 percent telehomecare group compared to 28 percent control group), yet th ey do not report on cost savings or the findings from the health-related quality of life measures that were used. Telerehabilitation Another application of telehe alth is Telerehabilitation. Telerehabilitation is an emerging practice that uses specialized communications technology for the remote delivery of care to patients with rehabilitati on needs. Telerehabil itation has the potential to manage multiple components of health, including functional independence, self-care and self-management of illness (Halamandaris, 2004a). The focus of telerehabilitation is

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38 to increase access to rehabilitation services, a nd to allow individuals to remain safe and independent in their homes. Over 50 million Americans today live with a functional impairment, often combined with a chronic illness, which impacts their ability to perform basic and instrumental activities of daily living (CDC, 2003b). A lthough most do not receive specialized therapy, millions require some so rt of therapeutic intervention. Typically these rehabilitative in terventions are supp lied through inpatient care, skilled nursing facilities, outpatient clinics, or home health visits. Unfortunately, as healthcare delivery is restructured in the U.S. due in part to financial considerations, rehabilitation entitlements are being reduced, resulting in shortened lengt hs of stay in acute and subacute care settings. With earlier discharg es, there is an increased need to deliver services to patients in their homes in a comprehensive yet efficient and cost effective manner. Researchers have expressed confiden ce in the “idea” of applying IT for the remote delivery of medical rehabilitation se rvices and support for independent living; they are sure that the poten tial is great (Burns et al., 1998; Kinsella, 1999; Rosen, 2004; Schopp, Hales, Brown, & Quetsch, 2003; Winter s, 2002; Winters & Winters, 2004). Telerehabilitation research studies The ability to remotely assess and monitor physical outcomes is an important area in telerehabilitation. Telereha bilitation has been used succe ssfully for administration of standardized assessment tools (N. C. Drey er, K. A. Dreyer, Shaw, & Wittman, 2001; Hauber & Jones, 2002; Russell, Jull, & Wootton, 2003b; Savard, Borstad, Tkachuck, Lauderdale, & Conroy, 2003) suggesting this is an accurate and reliable method of performing physical and cognitive assessmen ts. Additionally, televideo technology may

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39 also have potential for providing cost -effective in-home assessments for home modification services prior to a patient’s discharge (Sanford, Jones, Daviou, Grogg, & Butterfield, 2004). Findings from this small study suggest that remo te telerehabilitation assessments have the potential to enable sp ecialists to diagnose pot ential accessibility problems in home environments and prescribe appropriate modificati ons regardless of the location of the client, home, or specialist. Managing the health complications of disability is costly. A number of studies in a variety of care settings illustrate the ability to provide clinical care through telerehabilitation. Russell and colleagues used an Internet-based syst em in a replicated home environment within a clinical setting to provide rehabilitation to patients who had undergone total knee arthroplasty. Treatme nt for both the control and intervention groups included therapist guided stretchi ng and mobilizations, a tailored exercise program and education. Treatment outcome s for the telerehabilitation group were comparable to the control group. Following the treatment intervention, patients were surveyed and reported high ratings for satisf action of the telereha bilitation program, and ease of use of the technology (Russell, Buttrum, Wootton, & Jull, 2003a). Telerehabilitation may provide a way to improve care and to continue patient education following discharge from a hosp ital or inpatient se tting. In a quasiexperimental study, 35 spinal cord injury (SCI) patients were recruited for a telerehabilitation intervention in the preven tion of pressure ulcers (Phillips, Temkin, Vesmarovich, Burns, & Idleman, 1999). Pressure sores have been identified as one of the most common problems for SCI patients, and ar e also a serious problem for the elderly. Pressure ulcers can lead to expensive and dangerous complications, and treatment often

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40 requires that patients be hosp italized (Vesmarovich, Walker Hauber, Temkin, & Burns, 1999). The study’s main objectives were to dete rmine which of three approaches to care (videophone, telephone, standard care) produced the lowest inci dence of pressure ulcers, promoted the most effective care of sores that did develop, and lead to the fewest hospitalizations in newly injured patients w ith SCI after discharge. Phillips and colleagues reported that the telerehabilitation intervention was effective in ulcer tracking and management of all ulcer occurrences. Interestingly, the video group reported the greatest number of pressure ulce rs, but the investigators felt that visual contact with the nurse in the video group may have attributed to more ulcers actually being identified and reported. A large client base for rehabilitation include s adults with stroke and traumatic brain injury (TBI), yet few telerehabilitation studies have focused on these populations. Savard and colleagues reported on two c linical programs that used videoconferencing to provide rehabilitation consultation to individuals with neurologic diagnoses liv ing in remote areas (Savard et al 2003). The Minnesota Telerehabilitati on Initiative serves patients and clinicians in rural Minnesota. The Pacific Ri m Initiative serves patients and clinicians on the island of American Samoa. Both servic e areas have a scarc ity of rehabilitation clinicians. Both programs used a twomonitor system for continuous presence videoconferencing between the pa tient in their home and the rehabilitation specialist in the clinic. Their patient population included el derly individuals with diagnoses of TBI, stroke, and Parkinson’s disease. All patients reported satisfaction wi th the project, 23 patients had positive clinical outcomes, and average mileage saved was 150 miles one way. Two cases studies were presented. As th ese studies were descriptive in nature, the

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41 authors were unable to provide more than recommendations to others considering the provision of telerehabi litation services. Telerehabilitation may be a way to extend pos t-acute stroke care into a non-clinical setting, such as the community. Telerehabili tation allows providers to monitor patients’ progress, identify areas in need of improve ment, and ultimately improve function and decrease long-term disability and costs. A recent community-based study presented a model for providing telerehabilitation for st roke patients using videoconferencing (Lai, Woo, Hui, & Chan, 2004). Twenty-one stroke patients attended an 8-week intervention program at a community center for seniors. The intervention used a videoconference link and provided education, exercise, and psyc hosocial support for 1.5 hours at one session per week. Significant improvements were not ed in balance, st roke knowledge, selfesteem, and health-related quality of life. Advantages of community-based telerehabilitation includes eas e of access, enhanced learni ng and applying knowledge in a group atmosphere, increased social support, and allowance of real-time interaction between participants and the medical professionals. The au thors recommend that future studies consider investigati ng the length, duration and frequency of the intervention, as results may improve with more intense exercise a nd additional education. The development of telerehabilitation mu lti-center teams may make it possible to conduct, analyze and publish more extens ive research results in the area of telerehabilitation. In 1997, NIDRR issued a set of propos ed priorities for a new Rehabilitation Engineering Research Center (RERC) on Telerehabilitation. NIDRR’s main motivation was to explore methods to eliminate the barrier of distance in the delivery of comprehensive reha bilitation services. As well, the INTEGRIS Jim Thorpe

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42 Rehabilitation Center has teamed up with a gr oup of researchers, cl inicians, engineers, and administrators to create the Collaborativ e Alliance for Research in Telerehabilitation (CART). CART’s goal is to create a large da tabase of telerehabil itation studies through aligning standardized instruments for data gathering and developing a framework for collection of data across multiple institutions CART argues that the development of a model database linking the delivery of tele rehabilitation services reimbursement, and outcome evaluation is critical to meeting the challenge for long-term sustainability of telerehabilitation (Kaur, For ducey, & Glueckauf, 2004). The current literature also provides educa tional articles that define the basic operations of a telerehabilitation prog ram (Winters & Winters, 2004), emerging opportunities in telerehabilitation (Winters 2002), advantages and disadvantages of telerehabilitation (Torsney, 2003), and important components to consider when designing a telerehabilitation program (Schopp, Hale s, Quetsch, Hauan, & Brown, 2004). Winters (2002) reports that one of the apparent reas ons telerehabilitation isn’t thriving may be because there is not one optimal protocol fo r rehabilitation. Different problems require different technologies and pr ocedures. Based on these re ports, the deve lopment of a conceptual framework may be needed to pr ovide a foundation for clinical research in telerehabilitation. Telehealth applications within th e Veterans Health Administration The Veterans Health Administrati on (VHA) provided medical care to approximately 5.3 million veterans in 2005 (OPA, 2006). A significant portion of this medical care is provided for the management of chronic illnesses which are especially prevalent amongst the aging veteran populat ion in comparison to their community counterparts (Asch et al., 2004; Kazis et al 2004b; Rogers et al., 2004; Yu et al., 2003a).

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43 Additionally, when compared to the general U.S. population, ve teran enrollees tend to be poorer and more likely to live alone (Stineman et al 2001). Living alone may increase healthcare utilization du e to lack of available support at home, inability to rely on others for assistance, or lack of support for basic and instrumental activ ities of daily living (Guzman, Sohn, & Harada, 2004). Prior studi es have found living alone to be an independent risk factor for morbidity a nd mortality (DHHS, 2004; Lund et al., 2002). Furthermore, serious health or disabling c onditions may lead to residence in a nursing home due to the difficulties of home manageme nt. Each of these issues significantly increases the healthcare challenge and places our veterans at risk for healthcare crises. Cost effective and efficient approaches that foster the well-being and independence of our veteran enrollees must be explored. Telehe alth is viewed by ma ny individuals within VHA as an innovative means to increase acc ess and improve healthcare for veterans through telecommunications applications linking clinical car e, education, and administrative systems. In October 1999, the Veterans Health Administration (VHA) published a notice entitled, “Telemedicine Strategic Planni ng Document,” which outlined a national strategy for VHA telehealth and provided recommendations for the development, evaluation and optimization of telehealth to improve heal thcare for veterans (VHA, 1999). This planning document c oncluded the following: Telehealth has the potential to serve the healthcare needs of veterans by decreasing the barriers of distance and time. In re mote areas, travel distances represent a significant barrier for vetera ns to access timely care. Telehealth has the potential to enhance car e for veterans who may be isolated from necessary care, and to augment healthcar e services in home and community based care locations.

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44 Telehealth must be more thoroughly eval uated to demonstrate the efficacy, safety, reliability and outcomes of clinical Telehealth. Despite over three decades of telehealth ac tivities in different healthcare sectors, few clinical studies in tele health have comprehensively evaluated and documented such outcomes. To address these strategic planning initiatives, in April 2000 the VHA initiated funding of several clinical demonstra tion projects to test the integration of care coordination with communications technology fo r disease management (Meyer et al., 2002). Numerous publications have resulted fr om this initiative, but few VHS telehealth programs have existed long enough to provide convincing cost e ffectiveness results. Telehealth research studies within the VHA The use of technology to improve health behaviors and self-management in the veteran population and reduce the risk of early institutionalization is a focus of telehealth within the VHA. The Rural Home Care Pr oject (RHCP) was one of eight clinical demonstration projects within this original initia tive (Kobb et al., 2003). A prospective, quasi-experimental design with period data collection at 6-month intervals was used in one of the initial studies. The population of interest included veterans with multiple comorbidities who were high-cost medical users. The authors report that the intervention group showed greater improvement in health care resource consumption than the usual care group when comparing 6-mont h preto 6-month post-enrollment data. Patient and provider satisfaction was also reportedly high. This VHA tele health initiative included a multi-site study, which analyzed healthcare utilization and clinical impact. Three telehomecare demonstration projects from Ft. Myers, Lake City, and Miami, Florida were included (Cherry, Dryden, Kobb, Hilsen, & Nedd 2003). All participants (n=345) were elderly, had multiple chronic diseases (specifically CHF, coronary artery disease,

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45 diabetes, hypertension, and CO PD), and were high cost users of the VHA within the previous year ( $25,000). Home-based monitoring equipment allowed for daily responses to be categorized and risk prioritized to alert the care coordinators at each of the VA hospitals of the most serious outcome s first. Care coordinators contacted veterans by telephone based on the seriousness of the alerts. The intervention group was compared to themselves at 6 months preand 6 months post-enrollment. The authors report reductions in inpatient admissions, em ergency room encounters, and hospital bed days of care, as well as improve ments in medical compliance. The VA Connecticut Healthcare System us ed telehomecare, integrated with the VA’s electronic medical record system, to determine whether telehomecare could reduce healthcare costs and improve quality of life outcomes relative to standard care for chronically ill and frail elderly veterans (Noel et al., 2004). Ho me telecommunication units allowed for peripheral de vices to monitor vital signs an d provided a questionnaire to evaluate quality of life. Data was transm itted over telephone lines directly into the facility’s electronic database. In comp arison to the randomized control group, at six months the telehomecare group showed a signif icant decrease in costs in hospital bed days of care and emergency room visits, as well as a decrease in blood glucose levels. Functional level and patient-rated health stat us did not show a significant difference for either group at any period in time during the study. Most of the telehealth studies within the VHA focu s on healthcare costs and utilization, and little is known about the impact on physical and cognitive functioning. A case-control design study determined a causal relationship between the use of telehomecare and care coordination and improve ments in functional and cognitive status

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46 (Chumbler, Mann, Wu, Schmid, & Kobb, 2004). The investigators examined changes over a 12-month period and analyzed the before -after improvements in functional health and cognitive outcomes using the Functional Independence Measure and the Mini Mental Status Examination. The telehomecare group had significant improvements in all outcome measures over the 12 months. Results from an effectiveness study of a care coordination telehomecare program for veterans with diabetes determined that after two years of enro llment, a statistically significant reduction in hospita lizations was observed in the treatment group (T. E. Barnett et al 2006). An interesting phenomenon w ith many of the VHA telehealth programs is the increase in care-coordinator initiated primary care clinic visits following enrollment (Chumbler et al 2005). This increase in newly scheduled clinic visits is congruent with daily monitoring and the n ecessity to intervene quickly before a hospitalization is requir ed. In lieu of obser ving healthcare utilizati on at 12-months postenrollment, Barnett et al observed outcomes at 24 months following implementation and noted a reduction in care-coordi nator initiated clinic visits. Summary As the chronically ill and disabled elderl y populations become ever larger, there is greater urgency to find ways to provide effici ent, cost-effective care as well as improve functional performance and quality of life (Cruise & Lee, 2005). In an attempt to address this need, the provision of hea lthcare services has shifted from inpatient and outpatient settings to the home as the site of care. Allowing patients to remain within their home environments and still have direct comm unication with their healthcare providers increases access and quality of care, and may in turn reduce healthcare expenditures. Recent advances in information technology allow for the provision of such care to

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47 patients in their homes through telehealth applications. Telehealth may provide the means, yet significant research questions remain. A number of studies in a vari ety of care settings illustrate the promise of telehealth, but little systematic and cont rolled research has occurred to date. Based on the available literature, it appears that telehealth progr ams have yet to provide compelling objective documentation of successful outcomes. Because of serious limitations in experimental design, these studies are hinde red by small sample sizes, short durations, and other methodological flaws. Moreover, few studi es provide actual evidence that the interventions have resulted in clinical outcomes comparable to or better than the gold standard, conventional faceto-face care, although the technology and the technique seems to show promise in certain areas (Frantz et al., 2002). The overall methodology, quality of the evaluative studi es, and small sample sizes th at limit statistical power precludes producing convincing scientific results. These out come studies have demonstrated inconclusive medical and f unctional improvements and cost savings, and result in the lack of eviden ce-based guidelines that are imperative for the implementation of telehealth programs (Palsbo & Baue r, 2000; Whitten & Kuwahara, 2003). Such evidence-based results are essential to add to the scientific knowle dge base and ensure acceptance in the professional community. The next generation of studies needs to advance beyond efforts to replicate these earlier studies. Although larg e-scale randomized trials are important before one can argue convincingly that the me dical, psychosocial, functiona l, and fiscal outcomes of telehealth are positive, comprehensive studies evaluating current telehealth models are

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48 important and will serve as a standard for the methodology of future telehealth applications.

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49 CHAPTER 3 HEALTH RELATED COST ANALYSIS The Veteran’s Health Administration (VHA) has experienced unprecedented growth in the healthcare system workload over the past few years. During the last six years, the VHA has provided more medical se rvices to more veterans and family members than at any time during VHA’s hi story (OPA, 2006). The number of veteran enrollees receiving medical services with in the VHA increased by 22 percent from 2001 to 2005. Many veteran enrollees today are elde rly, chronically ill and disabled. Chronic illnesses account for a disproportionate amount of healthcare utilization and costs within the VHA (Yu et al., 2003a). Based on a recent study, data indicates th at 72 percent of the VHA patients have one or more chronic illn esses, and these patients account for 96.5 percent of the total VHA hea lthcare costs (Yu et al., 2004). Overcoming these challenges is a major barrier facing the VHA and healthcar e in general today. It has been proposed that telehealth can help meet these cha llenges (American Telemedicine Association [ATA], 2003; Bashshur, 2001; Brantley, La ney-Cummings, & Spivack, 2004; Cherry et al., 2003; Hibbert et al., 2004; Krupinski et al., 2002; Liss, Glueckauf, & EcklundJohnson, 2002; MacDonald-Rencz, Cradduc k, & Parker-Taillon, 2004; OAT, 2002). Telehealth used as a part of a coordinate d, comprehensive care program has demonstrated the ability to assist with the management of chronic conditions and reduce healthcare costs. Telehealth is a specific clinical applicat ion of monitoring patients in their homes from a central station usually located at a hos pital. Telehealth is viewed by the VHA as

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50 one of the more innovative advanced telecommu nication applications. Telehealth has the potential to link clinical car e, education, fiscal, and administrative systems to improve veteran’s healthcare, while at the same ti me increase veteran’s access to care. The premise is that improvements in healthcare se rvices and reductions in healthcare costs can be effected by establishing a continuum of patient care from the patient's home to service providers in the healthcare sector. Clinical effectiveness as well as the educa tional benefits of telehealth have been presented in the literature (Gamble, Savage, & Icenogle, 2004; Grigsby & Sanders, 1998; Taylor, 1998). Healthcare cost savings have been demonstrated in numerous telehealth studies (Bynum, Irwin, Cranfo rd, & Denny, 2003; Finkelstein et al., 2006; Hooper et al., 2001; Joseph, 2006; Noel et al., 2004). In a ra ndomized controlled trial, Finkelstein and colleagues demonstrated that telehealth visi ts between a skilled home healthcare nurse and chronically ill patients at home using videoconferencing technology improved patient self-care activities and lowered costs wh en compared to trad itional face-to-face home healthcare visits. Nakamara focused on ac tivities of daily living (ADLs) in his effectiveness study, and determined that th ere was not only a reduction in healthcare costs, but also significant improvement in ADLs, communication and social participation for participants in a telehealth interventi on when compared to a control group receiving traditional care (Nakamura et al., 1999). No el and colleagues (2004) determined that a home telehealth system which monitors vi tal signs and provides patient questionnaires reduced cost and improved quality of life outcomes for elderly patients with complex comorbidities In a recent report on home telehealth for diabetic patients, Dansky showed that monitoring patients in their hom es contributed substantial overall cost

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51 savings despite the additional expenses associated with the technology (Dansky et al., 2001). Meystre concluded following a literature review on the state of telehealth, that long-term disease monitoring of patients at home is the most promising application for technology for delivering cost effective qua lity care (Meystre, 2005). The use of technology combined with a chronic care model has the potential to reduce healthcare costs and lower use of healthcare services, as well as improve the management of chronic illnesses (Bodenheimer et al ., 2002a; Liss et al., 2002). In contrast, critical reviews of the cost-e ffectiveness and cost-benefit of telehealth report that current research has methodological and analytical weaknesses, and that it is premature to generalize about either the positive or negati ve effects of telehealth applications (Gamble et al., 2004; Hakanss on & Gavelin, 2000; Mair & Whitten, 2000). There continues to be a call for studies measuring the cost-effectiveness of the application of telehea lth to specific clini cal practices compared to conventional medical care (Gamble et al., 2004; Ohinmaa & Hailey, 2002). This chapter of the dissertation presents the health-related cost analyses between a telerehabilitation program (LAMP) and a matched comparison group, a telehomecare program (TCCP) and a matched comparison group, and a comparison between the telerehabilitation and telehomecare program. Me thods for obtaining the cost data and the comparison groups are presented, as well as the results and discussion from the analyses. Methods Cost Data The U.S. Department of Veterans Affair s (VA) uses the Decision Support System (DSS) to track its healthcare system workload and determine the cost of patient care. The National Data Extracts (NDEs) were created to assist VA research ers in accessing this

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52 workload and cost information. The NDEs are extracted from DSS and report total actual costs of every inpatient and outpatient en counter provided by the VA. NDEs include information based on fiscal years and report costs that incurred from the beginning of a fiscal year up to the current month. VA fiscal years run from October 1 through September 30. There are three core NDE files: inpatient discharge, inpatient treating specialty, and outpatient files. The inpatient discharg e files have one record for each hospital discharge that occurred dur ing the fiscal year. This file includes the entire cost for the hospital stay, i.e., nursing care, pharmacy, a nd laboratory testing. The inpatient treating specialty reports the type of bedsection unit where the care was provided, allowing for nursing home bed days of care (BDOC) to be distinguished from hospital bed days of care. The outpatient NDE files consist of one record for each unique clinic encounter, defined as a clinic stop. Therefore, there is a separate record for each clinic the patient visits, even if the patient visits multiple clinic s in one day. Each record contains the total cost of the encounter and information that identifies the patient, the location of the service and the date the service occurred. Outp atient visits include the costs of laboratory testing and ancillary services. Pharmacy r ecords and associated costs are stored in separate files. The NDEs are SAS files stored at the VA Au stin Automation Center (AAC). They are accessed using SAS batch programs. To access the NDE files, an account was established at the AAC in Austin, Texas. A “Time Sharing Request Form” as well as a “Privacy Act Statement” was submitted in order to work with real Social Security Numbers (SSNs) from a single Network (VIS N 8) for this project only. This was

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53 required as the local VHA facilities use real SSN s as the patient medical record number. The medical center director from the Malc om Randall VAMC in Gainesville, Florida granted approval to access real SSNs. To obt ain NDE records for our study participants, real SSNs were linked to encrypted SSNs include d in the NDEs. All data from this point on contained only data with encrypted SSNs. Linking of the Treatment Groups to the Comparison Group Pool Our matched comparison group was obtai ned from a database from the 1999 Veterans Large Health Study (LHS). The LH S was a national VA survey that established baseline health status on approximately one million veterans. The LHS was based on a random sample of all veteran enrollees in the nation. A data use agreement was submitted to the Office for Quality Performance (OQP) requesting use of the data for benchmarking of the SF-36 / SF-12 Health Related Quality of Life Survey and comparison of VA health re lated costs (hospitalizations, clinic visits, emergency room visits, nursing home B DOC). Following approval from OQP, a compact disk was provided which contained encrypted SSNs, diagnoses, age, marital status, education, and SF-36V scor es of all veterans from VISN 8 that participated in the 1999 LHS. The database consiste d of 75,715 veteran enrollees. Cleaning of the database was require d and initially included removing all individuals with missing demographic and di agnostic data, leaving the pool with 65,844 veterans. Forty-eight veterans enrolled in LA MP or TCCP also participated in the 1999 LHS; therefore, they were removed from the LHS database so that they were not double counted. As the LHS database was fro m a 1999 study, it was necessary to crossreference these individuals w ith individuals in VISN 8 w ho had received medical care during FY 2005 (at least 1 clinic visit). This ensured that the veterans used for the

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54 matched comparison group were alive and ut ilizing services dur ing the full pre-post periods. Individuals who died during the study period were not eligible for inclusion in the study. This reduced our comparison gr oup pool to 46,307. Next, individuals who were not being treated in the North Florid a/South Georgia Health Care System were deleted from the database. This reduced th e total pool to 10,120. Lastly, the comparison pool was cross-referenced with all enrollees in the CCCS database to ensure that no veterans in the comparison group had ever part icipated in a VA telehealth program. This reduced our total to 9918. Fr om the 9918, 56 individuals were then deleted from the pool due to unverifiable inpatient data, leaving 9862 individuals in our final comparison pool. These 9862 patients comprised the control pool for subsequent matching to the treatment groups. Figure 3-1 presents the initial linking procedure. Reported long-term chronic diseases The LHS database consisted of veterans who were enrolled in and receiving healthcare through the VA at th e time of the 1999 survey. Reported demographics and disease states for the LHS veterans were obt ained in 1999. To ensure comparability of our treatment and comparison groups, inpati ent and outpatient workload files with reported primary and secondary diagnoses ba sed on the Internationa l Classification of Diseases, Ninth Revision, Clinical Modificat ion (ICD-9) diagnostic codes were obtained for LAMP and TCCP from 1997-99. Detailed clinical information, including diagnoses, came from the VA National Patient Care Database (NPCD) healthcare workload/encounter files, which includes th e Patient Treatment Files (PTF) and the outpatient files. PTF and outpatient f iles for fiscal years 1997, 1998, and 1999 were explored in order to review diagnoses and ensure that each of the study arms was

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Figure 3-1. Preparation of comparison pool for final matching to LAMP and TCCP.

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56 1999 LHS Database for VISN 8 = 75,715 Veterans Missing Data = 9,871 Comparison Pool = 65,844 LAMP/TCCP LHS participants = 48 Comparison Pool = 65,796 Cross reference with 2005 outpatient data = 19,489 Comparison Pool = 46,307 NF/SG patients only = 36,187 Comparison Pool = 10,120 Non-CCCS patients = 202 Comparison Pool = 9918 Unverifiable patient data = 56 Final Comparison Pool = 9862 Matched to LAMP = 115 based on age, marital status, long-term diagnoses, number of prestudy inpatient bed days of care Matched to TCCP = 112 based on age, marital status, long-term diagnoses, number of pre-study inpatient bed days of care

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57 receiving VA care and was diagnosed with their reported chronic illness between 199799. All patients enrolled in LAMP and T CCP were identified in the 1997-99 PTF, signifying they were receiving VA care during th at time period. Only diagnoses reported in the 1997-99 NPCD files were used for matching purposes. Therefore, VA healthcare use and chronic illness diagnoses were c onsistent between our study arms. Chronic illnesses used for matching purposes for our comparison group and our treatment groups were diagnosed by 1999 or earlier. Enrollment date Specific enrollment dates were available for each member of the treatment group. These specific dates were used as a baseline to determine health related costs 12-months pre-enrollment and 12-months post-enrollme nt. Because our matched comparison group was not actually enrolled in a program, this was not possible; ther efore, an arbitrary enrollment date was required for analysis pur poses. To determine an arbitrary enrollment date, frequency of enrollment for LAMP a nd TCCP was calculated from October 1, 2002 through September 30, 2004 (the study period). Eighty-nine percen t of our treatment group was enrolled between June 2003 and Fe bruary 2004. Therefore, the median point of October 1, 2003 was chosen as an appropria te enrollment date. For our comparison group, FY 2003 served as the pre-enrollment period and FY 2004 served as the postenrollment period. Inpatient bed days of care pre-enrollment Inclusion criteria for enrollment in a VHA telehealth program includes previous use of medical services, especially hospital BDOC, and was deemed an important variable for matching purposes. Data on hospital BDOC 12 months pre-enrollment was obtained for both treatment groups and the comparison pool. The NDE files report inpatient BDOC at

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58 discharge. Our comparison pool was provided with an arbitrary enrollment date of October 1, 2003 (the first day of FY 2004) in order to determine health related costs for one-year pre and one-year post enrollment. Therefore, for our comparison pool, 12 months pre-enrollment extended from October 1, 2002 through September 30, 2003 (FY 2003). To determine pre-enrollment BDOC fo r inpatient stays that spanned more than one fiscal year (i.e., stays with admission da tes before October 1, 2002 or discharge dates after September 30, 2003), total inpatient B DOC were allocated proportional to the number of days that occurred within FY 2003. Matching The demographics of age and marital status, and diagnoses of arthritis, hypertension, congestive heart failure, chronic lung disease, diabetes and stroke, as well as number of pre-enrollment BDOC were used for matching purposes. Initially, SAS logistic regressions (stepwis e) were run for both LAMP and TCCP to determine which variables were signif icant to the treatment group at the p=.05 level Using this methodology, in the LAMP treatment group the variables of chroni c lung disease and CHF dropped out of the model. Therefore, these diagnoses were not used for matching purposes for the LAMP comparison group. Fo r TCCP, the variables of chronic lung disease and arthritis dropped out of the model and were not used for matching purposes for the TCCP comparison group. Matching was accomplished by creating a dummy string variable where the elements of the character string represented the variables that remained in each of the regression models. Initially, ma rital status and diagnoses we re dichotomized (1=yes and 2=no), and age and inpatient BDOC remained continuous variables. During the matching process, age was stratified to simplify the dum my variable. For LA MP, age stratification

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59 was 1 = ages 49-57, 2 = ages 58-66, 3 = ages 67-75, 4 = ages 76-84 and 5 = ages 85-98, which covered all age ranges. TCCP age stra tification was 1 = ages 37-57, 2 = ages 5866, 3 = ages 67-75, 4 = ages 76-84 and 5 = ages 85-90, which covered all age ranges. The number of pre-enrollment inpatient BDOC remained a continuous variable. Based on this technique, a dummy string vari able of 112121327 would represent a LAMP participant who was married a nd diagnosed with arthritis, diabetes and hypertension, within the age range of 67-75, and who had 27 inpatient BDOC pre-enrollment. Dummy string variables were created fo r all study participants in LA MP and TCCP, as well as the full comparison pool. Using the dummy string variable, 76 per cent of LAMP and 68 percent of TCCP had direct matches with a patient from th e comparison pool. Once the exact direct matches were determined, the remaining we re matched manually on age and pre-BDOC and as many of the residual dem ographic variables as possible. Telehealth vs. Standard Care The type of healthcare delivery a patient r eceived was also an independent variable in this study. The three t ypes of service delivery incl ude: VA telerehabilitation/care coordination (LAMP), VA telehomecare/care coordination (TCCP), and VA standard care. For the LAMP and TCCP cohorts, ongoing daily monitoring exists between the care coordinator and the patient through va rious types of technology. Due to daily monitoring, patients receive incr eased access to primary care, sp ecialty or rehabilitative care, and self-management support. In c ontrast, our matched comparison groups had access to all VA healthcare services, with in termittent contact with their primary care providers.

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60 Study Design A retrospective, matched comparison study design was implemented. The LAMP (telerehabilitation) program included vetera ns with functional deficits and chronic illnesses, who were at risk for multiple VA hos pital and nursing home bed days of care. Veterans were eligible for enrollment in LAMP if they presented with deficits in at least two activities of daily living (ADLs), includ ing mobility and transferring. Veterans enrolled had to live at home, have electricity and phone service, and accept remote monitoring technology into their homes. The TCCP (telehomecare) program included veterans with chronic illnesses, who were at risk for multiple VA inpatient and outpatient visits. Veterans were eligible for enrollm ent in TCCP if they were noninstitutionalized, had a history of high healthcare costs and ut ilization, had electricity and phone service, and accepted remote monitoring technology into their homes. Both treatment and comparison groups rece ived their healthcare from the North Florida/South Georgia Healthcare System. Treatment and comparison groups were matched on demographic variables of age and marital status, as well as primary diagnoses, and number of 12-month pre-study be d days of care. All groups had to be enrolled in the VA for the entire 24-month observation. Actual enrollment dates were used for our treatment groups to determine prepost costs. The arbitrary enrollment date of October 1, 2003 was used for the comparison groups to determine pre-post healthcare expenditures. Although selection criteria was stringent for matching of the comparison groups, the absence of randomization be tween the treatment and comparison groups may result in selection bias. A difference-in-differences (DiD) approach was used to allow for the control of any remaining differences be tween the treatment and comparison groups,

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61 including the differences that may not be di rectly observed. Such unobserved differences may influence both the treatment and comp arison groups, as well as the estimated treatment effect. The DiD method controls for selection bias through measuring the treatment effect while accounting for any pr etreatment differences between the groups. This method has often been used in studies of labor economics, with applications increasing in health services research (Tai-Seale, Freund, & LoSasso, 2001; Wagner, Hibbard, Greenlick, & Kunkel, 2001), as well as telehealth studies (T. E. Barnett et al., 2006; Chumbler et al., 2005). The concept of DiD observes the treatment and control group before and after the intervention. Prio r to the intervention, intrinsic differences between the groups are measured. Following th e intervention, the treatment effect plus the intrinsic differences betw een the groups are measured. The treatment itself is then calculated by subtracting the intrinsic difference between the two groups pre-intervention from the combined treatment effect plus intr insic difference post-inte rvention. Therefore, we are measuring the difference between the differences to obtain the treatment effect. Statistical Analysis The dependent variables used in this st udy were healthcare expe nditures defined as costs incurred by the VHA for inpatient BDOC (h ospitalizations), outpa tient clinic visits, emergency room visits (ER), and nursi ng home care unit (NHCU) BDOC. Costs presented exclude costs of cont ract medical services provided at non-VA facilities. Total costs were summed for the final analyses, w ith cost breakdowns presented in order to clarify final results. In an attempt to decrea se variability and skeweness in the cost data, the natural log of costs (lncosts) were initially considered for these analyses. Prior to logging, natural costs were positively skew ed. Logging costs resulted in negative skeweness, but did not decrease variab ility enough to undertake the analyses.

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62 Additionally, a linear regression model using na tural log converts to a nonlinear model, which requires complicated corrections, and is difficult to interpret. A multivariable statistical model was implemented using actual costs as the outcome, based on a difference-in-differences (DiD) approach. The DiD model was used to compare LAMP with their matched co mparison group and TCCP and their matched comparison group to determine where differenc es lie within the groups based on total healthcare costs. The statistic al model used for patient cost s in this research study was: E(Costs) = 0 + 1 (Treatment) + 2 (Time) + 3 (Treatment x Time) + X. The parameter 3 represents the DiD estimate of the treatment effect. Finally, a one-way analysis of varian ce (ANOVA) was used to compare the two telehealth programs, LAMP and TCCP, to de termine where differences lie within the treatment groups. ANOVA was used to compare the independent variables of age, marital status, diagnoses, and pre-BDOC, wh ich were the variable s used for initial matching of their comparison groups. Following ANOVA, the DiD approach was performed to determine if LAMP and TCCP di ffered in treatment effects based on costs, after accounting for the covariates determined by ANOVA. SPSS version 12.0 (SPSS, Inc., Chicago, IL) and SAS version 9.1.3 (SAS Institute, Cary, NC) were both used for these analys es, with significance le vel set at .05. All analyses were two-sided. Analyses followed in tention to treat such that all subjects who were enrolled and participated for one full year in LAMP or TCCP during October 1, 2002 through September 30, 2004 were included in the analyses regardless of study participation level.

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63 Results Descriptive baseline data including age, marital status, diagnoses and pre-BDOC for LAMP and its matched comparison group are presented in Table 3-1. Chi-square for descriptive variables an d independent samples t -tests for continuous variables were used to compare treatment and comparison groups on these baseline characteristics. Table 3-1. Baseline characteristics of te lerehabilitation group, Low ADL Monitoring Program (LAMP), and matched comparison group* Characteristics LAMP (n=115) Comparison Group (n=115) p value Age, mean, s/d .63 Marital Status .88 Arthritis .15 Hypertension .11 Diabetes .88 Stroke .20 Pre-BDOC, mean, s/d 72.4 + 9.4 (73.0) (50.4) (65.2) (24.3) (35.7) 12.6 + 26.3 71.7 + 9.6 (73.9) (60.0) (54.8) (23.5) (27.8) 12.6 + 26.2 .98 *Data are given as number (percentage) unles s otherwise indicated. BDOC indicates hospital bed days of care. LAMP and Matched Comparison Group LAMP and matched comparison group part icipants were primarily male (97 percent) with more than 70 pe rcent married. On average, st udy participants were age 72. On the average, participants reported four chronic illnesses. More than 50 percent reported they had been diagnosed with hypertension and arthritis, approximately 25 percent reported diabetes, and approximately 30 percent had suffered a stroke. The average number of hospital BDOC one year pre-enrollment was 12.6. Total summed actual costs and cost ite mization for LAMP and their matched comparison group are presented in Tables 32 and 3-3. Tables in clude one-year preenrollment costs in comparison with one-year post enrollment costs.

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64 Table 3-2. Healthcare expe nditures for LAMP (n=115) one -year pre-enrollment and oneyear post-enrollment Total Sum BDOC Clinic ER NHCU Pre-Enroll Days/Visits Percent of total $2,767,712.90 $1,494,483 1449 54.0% $1,162,211 4561 42% $23,842 116 0.86% $87,177 214 3.15% Post-Enroll Days/Visits Percent of total $2,812,250.50 $690,215 623 24.5% $2,053,015 8728 73% $24,257 108 0.86% $44,763 98 1.6% Difference in costs pre-post Difference in days/visits prepost +$44,537.60 -$804,268 -826 +$890,814 +4167 +$415 -8 -$42,414 -116 Table 3-3. Healthcare expe nditures for LAMP matched comparison group (n=115) oneyear pre-enrollment and one-year post-enrollment Total Sum BDOC Clinic ER NHCU Pre-Enroll Days/Visits Percent of total $2,055,283.60 $1,231,656 1443 60% $642,052 3088 31% $16,908 76 0.8% $164,668 404 8% Post-Enroll Days/Visits Percent of total $1,578,459.30 $553,924 699 35% $862,510 2931 55% $12,826 72 0.8% $149,198 400 9.5% Difference in costs pre-post Difference in days/visits prepost -$476,824.30 -$677,732 -744 +$220,458 -157 -$4,082 -4 -$15,470 -4 Hospital bed days of care Costs for hospital BDOC in the year pr eceding enrollment in LAMP totaled approximately $1,500,000 and consisted of 1449 da ys of care. These 1449 hospital days were for 55 patients. The average cost of a BDOC pre-enrollment in LAMP was $1,030. Total costs for hospital BDOC for LAMP decreased more than $804,000 and 826 days in

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65 the year following enrollment. This represents a 46 percent decrease in costs. The average cost of a BDOC pos t-enrollment was $1,100. Costs for hospital BDOC in the pre-enro llment year for our matched comparison group totaled approximately $1,230,000 and cons isted of 1443 days. These 1443 BDOC were for 55 patients, and the average precost of a hospital BDOC for our matched comparison group was $853. Post-costs for hos pital BDOC for the matched comparison group decreased approximately $678,000 and 744 days or 45 percent. The average postcost of a BDOC decreased by $61. Clinic visits Every LAMP participant in our study received at least one clinic visit both pre and post-enrollment. Costs for clinic visits pre-post for LAMP increased more than $890,000 following enrollment, representing an increase of 4167 clinic visits. In an attempt to determine which clinic encounter s increased, clin ic visits were calcula ted for each clinic stop code for one-year pre-enrollment in LA MP and one-year post enrollment in LAMP. Clinic visits increased specifically in the area of preventive medicine, including laboratory and x-rays, and primary and geriatri c patient care. Increa ses were also noted in physical medicine and rehabilitation, incl uding speech language services, occupational and physical therapy services, as well as pros thetics. Prosthetic devices increased from 573 pre-enrollment to 1193 post-enrollment. Th e provision of prosth etic or assistive devices, such as bathroom aids and mobility devices was a primary focus of LAMP services. Diabetes care, urol ogy care, and home health aide a ssistance were also noted to increase for the LAMP intervention group. Clinic visits also included 127 home assessments performed by LAMP and approxi mately 2605 patient contacts resulting from enrollment in LAMP.

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66 One hundred eleven of the 115 veterans in the matched comparison group received a pre-clinic visit, and 113 rece ived a post-clinic visit. Clinic visits pre-post for the matched comparison group increased by 157 visits which resulted in a cost increase of approximately $220,000. There were no significant differences in clinic stop codes pre versus post for the comparison group. In fact preventive services su ch as laboratory and x-rays, as well as primary care and geriatric care decreased by over 100 visits during the post-year. Emergency room visits ER visits for the LAMP participants re mained stable over the two-year period. LAMP ER visits increased in dollar amount by $415, but total visits decreased by 8. Fifty-one LAMP enrollees required ER serv ices pre-enrollment, and 48 LAMP enrollees required ER services post-enrollment. Ou r matched comparison group decreased their ER visits by 4, resulting in a savings of over $4,000. Thirty-s ix patients required pre-ER visits, which decreased to 32 patients in the post-period. Nursing home bed days of care A main hypothesis was that LAMP would ma intain independence over time to a greater extent than the other study arms. Wh ile it may be difficult to determine which outcomes signify that one of the study arms is more independent than another, the use of NHCU may be such an outcome. Functional decline and decreased independence in selfcare are the main reasons patients are pl aced in nursing homes (Andresen, Vahle, & Lollar, 2001; Yu, Wagner, Chen, & Barnett, 2003b). LAMP particip ants spent 214 days in NHCU pre-enrollment, which decreased to 98 days post-enrollmen t, demonstrating a decline of 116 days. This amounted to a cost reduction of over $42,000. For LAMP,

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67 NHCU BDOC averaged approximately $14,000 per person pre-enrollment, and decreased to an av erage of $8,000 per pers on post-enrollment. Our matched comparison group spent ove r 400 days in NHCU pre-study period, which decreased by 4 days post-enrollment. This amounted to a cost savings of approximately $15,000. Yet, for our matche d comparison group, NHCU costs per person increased from $18,000 pre to approximat ely $25,000 post, compared to $8,000 per person post-enrollment in LAMP. TCCP and Matched Comparison Group Descriptive baseline data including age, marital status, diagnoses and pre-BDOC for TCCP and its matched comparison group are presented in Table 3-4. Chi-square for descriptive variables an d independent samples t -tests for continuous variables were used to compare treatment and comparison groups on these baseline characteristics. Table 3-4. Baseline characteristics of telehomecare group, Technology Care Coordination Program (TCCP), and matched comparison group* Characteristics TCCP (n=112) Comparison Group (n=112) p value Age, mean, s/d .77 Marital Status .12 Hypertension .06 Diabetes .78 Stroke .60 CHF .85 Pre-BDOC. mean, s/d 70.94 + 11.2 (62.5) (68.8) (40.2) (6.3) (15.2) 10.23 + 30.1 70.5 + 10.9 (67.0) (57.4) (38.4) (8.0) (14.3) 10.8 + 34.1 .88 *Data are given as number (percentage) unl ess otherwise indicated. CHF indicates congestive heart failure, BDOC indi cates hospital bed days of care. TCCP and matched comparison group part icipants were primarily male (98 percent) with more than 60 pe rcent married. On average, st udy participants were aged 70. Participants reported approximately four chronic illnesses. Mo re than 60 percent of the groups had been diagnosed with hyperten sion, 40 percent reportedly were diabetic, 6-

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68 8 percent had suffered a stroke, and appr oximately 15 percent had congestive heart failure (CHF). Total summed costs and breakdowns for TCCP and their matched comparison group are presented in Tables 3-5 and 3-6. Tables include pre-enrollment costs in comparison with one-year post-enrollment costs. Actual enrollment dates were used for TCCP to determine pre-post costs. The arbi trary enrollment date of October 1, 2003 was used for the comparison group to determine pr e-one year and post-one year healthcare expenditures. Table 3-5. Healthcare expe nditures for TCCP (n=112) one -year pre-enrollment and oneyear post-enrollment Total Sum BDOC Clinic ER NHCU Pre-Enroll Days/Visits Percent of total $1,474,699 $801,490 1146 54.4% $557,159 3414 37.8% $10,456 94 0.7% $105,594 338 7.2% Post-Enroll Days/Visits Percent of total $2,140,111 $850,953 1055 39.8% $1,095,174 5414 51.2% $17,335 193 0.7% $176,650 541 7.2% Difference in costs pre-post Difference in days/visits prepost +$665,412 +$49,463 -91 +$538,015 +2000 +$6,879 +99 +$71,056 +203 Table 3-6. Healthcare expe nditures for matched comparison group (n=112) one-year preenrollment and one-year post-enrollment Total Sum BDOC Clinic ER NHCU Pre-Enroll Days/Visits Percent of total $1,606,664 $872,972 1216 54.3% $521,625 2616 32.5% $6254 31 0.4% $205,812 549 13.0% Post-Enroll Days/Visits Percent of total $1,362,215 $438,097 665 32.0% $763,532 2586 56.5% $9493 84 0.7% $151,094 436 11.0%

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69 Table 3-6. Continued. Total Sum BDOC Clinic ER NHCU Difference in costs pre-post Difference in days/visits prepost -$244,449 -$434,875 -551 +$241,907 -30 +$3,239 +53 -$54,718 -113 Hospital bed days of care Costs for hospital BDOC for TCCP incr eased more than $49,000 in the year following enrollment, but decreased by 91 days Hospital BDOC in the year preceding enrollment in TCCP totaled approximately $800,000 and consisted of 1146 days of care. These 1146 hospital days were for 42 patients. The average cost of a BDOC preenrollment in TCCP was $700. The average co st of a BDOC post-en rollment increased to $800. Costs for hospital BDOC for the matched comparison group decreased approximately $435,000 and 551 days. Hospital BDOC in the pre-enrollment year for our matched comparison group totaled approximately $873,000 and consisted of 1216 days. These 1216 BDOC were for 42 patients, with the average pre-cost of a hospital BDOC for our matched comparison group at $71 8. Post-costs for hospital BDOC for the matched comparison group decreased to an average cost of $658. Clinic visits Costs for clinic visits pre-post for TCCP increased more than $538,000 following enrollment. This represents an increase of 2000 clinic visits. To determine where the increase was, clinic visits were calculate d for each clinic stop code for one-year preenrollment and one-year post enrollment in TCCP Clinic visits increased in the area of preventive medicine, including laboratory a nd x-rays, and primary and geriatric patient

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70 care. Prosthetic devices increased from 321 pre-enrollment to 534 post-enrollment. Diabetes care, ophthalmology, and home health aide assistance were also noted to increase for the TCCP intervention group. C linic stops included more than 1154 patient intervention contacts resulting from enrollment in TCCP. Costs for clinic visits pre-post for the matched comparison group increased approximately $242,000, but number of clinic visi ts decreased by 30 visits. Preventive services such as laboratory and x-rays, as well as primary care and geriatric care remained stable over the year. Emergency room visits ER visits increased for both TCCP and their matched comparison group. TCCP ER visits increased by approximately 100 visi ts post-enrollment, and more than $6,800. Forty-one patients visited the ER pre-enrollme nt in TCCP, and 61 patients visited the ER post-enrollment. ER vis its for the matched comparison group increased by 53 visits and approximately $3,200, which was an increase from 21 patients to 28 patients pre-post. Nursing home bed days of care Pre-enrollment NHCU for TCCP include d 338 days at nearly $106,000. The postenrollment costs increased by $71,000, and 203 days. For the TCCP matched comparison group, we see a decline in NHCU of 113 days and $54,700. Cost Analysis: Difference-in-Differences Approach As shown in Table 3-7, multivariate resu lts determined that 1 year following enrollment in LAMP, there are no significant di fferences in total healthcare costs (costs include inpatient BDOC, clinic, ER, NHC U), between LAMP and their matched comparison group.

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71 Table 3-7. Multivariable regression analysis summary examining the relationship among LAMP and matched comparison group, pre-post interven tion, and total healthcare costs based on the DiD me thod, with the comparison group serving as the reference group. Variable B SEB Main Effects Intercept 17,872 2943 6.07*** Treatment Time (pre-post) 6,195 -4,146 4162 4162 1.49 -1.00 Interactions Time*Treatment 4,534 5886 0.77 Note. R2 = 0.02 (n=230) ***p .001 In this model, the overall regr ession equation was significant (F(3,456)= 3.09, p < .05), demonstrating the relationship between co sts and treatment, but the coefficient of determination (R2 = 0.02) represents a w eak association. The in tercept (17,872) is the mean predicted costs pre-intervention when holding treatment and time constant. The treatment coefficient (6,195) is not statisti cally significant, dem onstrating there are no baseline significant differences in the trea tment groups prior to the intervention. The slope for time (pre-post intervention = -4,146) is the predicted inte rvening time effect on costs for LAMP, which was not significant. The interaction demonstrates the treatment effect, and is the product of time and treatment on health related costs. The slope of the product of the two variables represents the chan ge in costs for LAMP as time increases. Based on the regression coefficient (4,534), we are unable to detect a statistically significant treatment effect. The R2 value is an indicator of how well the model fits the data (e.g., an R2 close to 1.0 indicates that we ha ve accounted for almost all of the variability with the variables specified in the model). The R2 of 0.02 indicates that the variables, treatment and time, account for no more than 2 percent of the va riance in costs. Table 3-8 presents the DiD results for the multivariate analysis between TCCP and their matched comparison group.

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72 Table 3-8. Multivariable regression analysis summary examining the relationship among TCCP and matched comparison group, pre-post interven tion, and total healthcare costs based on the DiD me thod, with the comparison group serving as reference group. Variable B SEB Main Effects Intercept 14,345 2540 5.65*** Treatment -1,178 3592 -0.33 Time (pre-post) -2,183 3592 -0.61 Interaction Time*Treatment 8,124 5080 1.60 Note. R2 = 0.01 (n=224) ***p .001 In this model, the overall regres sion equation was not significant (F(3,442)= 1.46, p > .05), demonstrating this linear model does not fit the data and has no predictive capability. The regression equation does not pr ovide a basis for predicting costs based on treatment and, therefore, we are unable to statis tically detect a treatm ent effect with this model. The R2 of 0.01 indicates that this group of va riables (treatment and time) account for no more than 1 percent of the variance in costs. Residual scores for our regression equation are widely dispersed around the regression line, indicating a large error component. Treatment Group Comparisons A one-way analysis of variance (ANOVA) was used to compare LAMP and TCCP to determine where mean differences lie within the groups based on the independent variables of age, marital status, diagnoses and pre-BDOC. These are the variables used for initial matching of the comparison groups. A significant difference was found between the treatment groups and the diagnoses of arthritis (F(1,225)= 51.04, p < .001), stroke(F(1,225)=33.5, p < .001), and diabetes (F(1,225)=6.65, p < .05). This analysis revealed that participants in LAMP had more incidences of arthritis and stroke than participants in TCCP. This is not surprising, as inclusion in the LAMP program focused on individuals

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73 with rehabilitative needs. Additionally, the variable arthritis was not used for matching purposes for TCCP and their comparison gr oup. Differences were also found between TCCP and LAMP in the area of diabetes, with TCCP demonstrating higher prevalence of diabetes. Following the comparison, a multivariable regression model using the DiD method was calculated to examine the effects of group assignment (LAMP/TCCP) and healthrelated costs, covarying out the effects of arthritis, stroke, and diabetes. Table 3-9 presents the results. Table 3-9. Multivariable regression analysis summary examining the relationship in healthcare costs between LAMP and T CCP, pre-post intervention, covarying out the effects of diagnoses based on the DiD method, with TCCP as the reference group. Variable B SEB Main Effects Intercept 10,999 3061 3.59** Treatment 12,060 4416 2.73** Time (pre-post) Arthritis Diabetes Stroke 5,941 -3,517 5,727 3,890 3927 3461 3004 3783 1.51 -1.02 1.91 1.03 Interactions Time*Treatment -5,553 5517 -1.01 Note. R2 = 0.04 (n=227) **p .01 In this model, the overall regr ession equation was significant (F(6,447)= 2.85, p < .01), demonstrating the relationship between cost s and treatment is not likely to be the result of chance, although the coefficient of determination (R2 = 0.04) represents a weak association. The intercept (10,999) is the m ean predicted costs for the pre-enrollment period when holding time and treatment cons tant. The slope for the treatment group (LAMP) is the predicted effect on costs of being in the treatm ent group. Based on the significance of the model and the significance of the treatment variable, there are

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74 additional cross-sectional sel ection biases evident between our two groups prior to the intervention. The slope for time (pre-pos t intervention = 5,941) is the predicted intervening time effect on costs, which was not significant. The inte raction is the product of time and treatment on health related cost s. The slope of the product of the two variables represents the change in costs for LAMP as time increases. The coefficient for this variable was negative, and may be interpreted to mean a negative effect on costs based on treatment by LAMP, but this was not significant. Additionally, we are unable to detect a statistically significan t effect on costs determined by any of the diagnoses in the model. The R2 of 0.04 indicates that these variables (treatment, time, arthritis, diabetes, stroke) account for no more than 4 percent of the variance in costs. Discussion This retrospective study ex amined the effectiveness of a VA telerehabilitation program (LAMP) and a VA telehomecare program (TCCP) for a cohort of chronically ill veterans with matched comparison groups by examining healthcare costs at 12 months following the intervention. In the absence of a randomized controlled trial, this quasiexperimental design attempted to ove rcome methodological s hortcomings by using strict matching criteria and a DiD approach to evaluate treatment effectiveness. The DiD method controls for any intrinsic differences between the groups pre-intervention, as well as intervening time factors dur ing the intervention, and pr ovides the observed treatment effect. Using the DiD approach and actual costs su mmed for these analyses, no significant differences were observed in post-enrollmen t costs between LAMP and their matched comparison group, TCCP and their matche d comparison group, or between the two treatment groups, LAMP and TCCP. The point estimate of the DiD treatment effect in

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75 each of these models was extremely large rela tive to the mean costs. Therefore, the inability to detect significance is a result of the high variability of the estimate, and does not signify that there is no treatment effect. A larger sample size may improve accuracy of prediction. Additionally, logging of the costs would reduce variability and may increase the ability to detect significance. There are numerous factors to cons ider. During the 12-months following enrollment, LAMP participants experienced a co nsiderable increase in clinic visits/stops, increasing 4,167 visits at an increase in co sts of more than $890,000. Although inpatient BDOC costs were reduced, including both i npatient BDOC and nur sing home BDOC, the increase in clinic costs in creased LAMP’s overall costs $44,538 post-enrollment. It is important to note that one of te lehealth’s primary focuses is to increase access to care; as a result, much of the increase in clinic visi ts was a product of enrollment in LAMP. The increase in LAMP clinic stops includes se rvices provided by the intervention, i.e., the initial evaluation and home assessment, ad aptive equipment provided for self-care and safety, and remote monitoring interventions. Additionally, due to the intensity of daily monitoring, patients were more apt to be brought into the clinic for ch eck-ups or more indepth evaluation in order to ensure an illness did not escalate and require hospitalization. Although the number of interventi on-related clinic stops is provided, it is difficult to determine how many of the care coordinato r-patient contacts resulted in additional primary or geriatric care visits lab and diagnostic visits, or s econdary clinic visits such as ophthalmology or audiology. This significant incr ease in care coordina tor-initiated clinic visits has been observed in other VA home telehealth studies (Chumbler et al., 2005; Kobb et al., 2003).

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76 During the 12-months following enrollment, TCCP participants also experienced a large increase in clinic vis its, increasing 2,000 visits and more than $500,000. For TCCP, while the number of inpatient BDOC decrea sed, inpatient costs increased slightly postenrollment. The combined effect of incr eased costs per BDOC and additional clinic visits increased TCCP’s costs post-enro llment by more than $665,000. The increase in TCCP clinic stops includes services provided by the intervention, such as the initial home visits and installation of remote monito ring equipment, and the follow-up monitoring interventions. TCCP’s primary goal of remotely monitoring health symptoms and providing increased access to care resulted in a significant incr ease in clinic visits. Due to the intensity of daily m onitoring, patients may receive a dditional primary or geriatric care visits, which in turn increase lab and di agnostic procedures. In comparison, TCCP’s matched cohort received 30 less c linic visits, which resulted in a savings of $242,000. It is evident that when treatmen t is decreased, costs decrease. Longer term observations are required to determine the health-related cost effects of these increases and decreases in ambulatory care. Additionally, as stringent as our matching criteria were, this was not a randomized controlled trial; matching was performed retros pectively based on the variables that were available. When we analyze the cost distri bution, LAMP enrollees ar e considerably more costly and, therefore, possibly less healt hy. The average cost of each BDOC was approximately $200-$300 higher per day for th e LAMP group pre-and post-enrollment in comparison with both their matched c ohort and the telehomecare (TCCP) group. When actual costs are observed pre a nd post-study period, we note a significant decrease in inpatient costs (BDOC) for LAMP (t(114)=3.09, p .01), and both of the

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77 comparison groups. LAMP’s matched comparison group decreased approximately 45 percent, or $678,000, based on pre-st udy costs and post-study costs (t(114)=3.09, p .01). Hospital costs for TCCP’s matched comp arison group decreased approximately 50 percent, or $435,000 within the same study period (t(111)=1.95, p .05). This phenomenon may be a result of regression to the mean. When using a pre-post design, regression to the mean may bias results in healthcare expenditures (Barnett, van der Pols, & Dobson, 2005; T. E. Barnett et al 2006; Yudkin & Stratton, 1996). Regression to the mean (RTM) is a statistical phenomenon that may occur whenever there is a nonrandom sample from a population and tw o measures that are imperfec tly correlated, such as preenrollment costs and post-enrollment costs fo llowing an intervention. Veterans in this study were enrolled into a telehealth program because of their high usage of VA medical services. Our comparison groups were matched with our treatment groups based on preBDOC and also demonstrate high levels of h ealthcare use at baseline In RTM, observed change may be most negative for those with the largest pretest values. This is often interpreted as showing the effect of the treatm ent. While the regression effect is real and complicates the study of subjec ts who are initially extreme on the outcome variable (i.e., costs), we attempted to control for it statis tically through the DiD design. Unfortunately, the uses of costs in the design, which were highly variable within and between our study populations, resulted in a high error rate for our regression analysis. The observed decrease in inpatient costs may also be explained by a system-wide secular trend within VA hospitals to decrease inpatient length of stay (BDOC) and transition to more am bulatory care (Payne et al 2005; Phibbs, Bhandari, Yu, & Barnett, 2003; Yu et al., 2003a). Additionally, all four study arms demonstrated high costs based

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78 on numerous hospitalizations in our pre-study pe riod. We would assume that if a patient had been hospitalized in our pre-study time period, followi ng a successful hospital stay they would not require hosp italization during our post-st udy period. The ability to observe these patients over a longer period of time may provide more accurate effects of treatment versus non-treatment. There are limitations in the study that n eed to be addressed. Healthcare costs included inpatient BDOC, clinic visits, em ergency room visits, and NHCU, and were summed for these analyses. Summed costs in cluded VA-incurred expenses and did not consider whether the patient utilized servic es other than the VA, which may be a key reason for the inability to detect significance. Research has determined that between 25 and 50 percent of veterans are dual users, and seek both primary and inpatient care outside of the VA (Borowsky & Cowper, 1999; Payne et al 2005; Stroupe et al 2005). This percentage increases when veterans ar e not satisfied with th eir care. LAMP and TCCP enrollees are carefully monitored and referred to VA services, whereas nontelehealth veterans may be more apt to seek medical care outside of the VA. This would likely increase costs for our comparison group. Future studies should consider the impact of differential use of VA services between the groups. More notably, skewed distri bution and heteros cedasticity problems in healthcare expenditure models have been well recognized by health se rvice researchers (Manning & Mullahy, 2001; Yu et al., 2003a). For this study, we analyzed actua l healthcare costs. Models were also analyzed using costs tran sformed by a natural logarithm function. Due to the difficulty in interpreting the logged re sults, and the large mean differences between

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79 groups in the exponentiated resi duals, logged costs were not used for the final analysis, but should be considered in future costs studies. Although patients within each telehealth program, LAMP and TCCP, and their matched comparison groups, were comparable w ith respect to age, marital status, prestudy period hospital bed days of care, and pr imary chronic illness, we did not consider additional comorbidities. Many of our part icipants had multiple comorbidites, which may result in higher healthcare expenditu res, and require more intense remote monitoring. Approximately 30-35 percent of the ma tching was performed manually. The ability to acquire a direct one-to-one match was increasi ngly difficult due to the high number of variables incorpor ated into the matching dumm y string along with the wide variability of the pre-BDOC. If pre-BDOC had been strati fied, additional matches may have been obtained, but this was not optim al. Moreover, although careful steps were taken to ensure close matching of the co mparison groups, we had limited access to such sociodemographic information as educational level, income, or the presence of a caregiver or other social support within the home. This study attempted to quantif y the effect of telerehabili tation and telehomecare in reducing healthcare costs among four groups of veterans. The analyses observed veterans enrolled in LAMP, veterans enro lled in TCCP, and corresponding matched comparison groups who have not received any ty pe of telehealth inte rvention. The initial hypothesis for this study was that veterans en rolled in LAMP, veterans enrolled in TCCP, and their corresponding matched group of ve terans who have not received telerehabilitation or telehomecare interventions will differ in their VA healthcare costs.

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80 Based on results from the multivariable regressi on analyses, we reject the hypothesis that our four study arms will differ in VA healthcare costs following one-year enrollment in a telehealth program. It should be noted that based on the variance of errors in each of the regression equations, numerous unknown or uni dentified factors must account for the remaining variance in the models. Future research should consider usin g a randomized controlled trial design, following the intervention and comparison gr oups for more than 12 months, analyzing differential use of VA services and collecting information to identify care coordinatorinitiated outpatient visits.

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81 CHAPTER 4 HEALTH STATUS AND OUTCOMES FROM THE VETERANS SHORT FORM-12 HEALTH SURVEY Development of the Veteran’s SF-36 The ability to quantify an individual’s pe rception of their illness and how their illness affects their social and functional roles is an important component when evaluating healthcare re quirements and healthcare inte rventions (IOM, 2001; Kaplan, 2002; Office of Quality Performance [OQP], 2000). Measurements of health-related quality of life (HRQoL) are increasingly used to assess th e impact of chronic disease and healthcare interventions, as phys iologic measures often corr elate poorly w ith functional ability and well-being (Andresen & Meyers, 2000; Guyatt, Feeny, & Patrick, 1993). The SF-36 Health Survey is a frequently used pa tient-derived measure of disease burden and HRQoL. The SF-36 was adapted from the Medical Outcomes Study 20-item short form health survey in an attempt to construct a more efficient scale for measuring general health (Kazis, 2000; J. E. Ware, Jr. & Sherbourne, 1992). The SF-36 includes one multiitem scale that assesses eight he alth concepts: 1) limitations in physical activities due to health problems; 2) limitations in social activ ities due to physical or emotional problems; 3) limitations in usual role ac tivities due to physical health problems; 4) bodily pain; 5) general mental health (psychol ogical distress and well-being); 6) limitations in usual role activities due to emotional problems; 7) vita lity (energy and fatigue); and 8) general health perceptions (J. E. Ware, Jr. & Sherbour ne, 1992). These eight concepts have been summarized into two summary scores: the physical component summary (PCS) and the

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82 mental component summary (MCS). The orig inal version of the SF-36 is scored using weights derived from a national probability sa mple of the U.S. population. Scores are norm-based with a mean of 50 and a standa rd deviation (SD) of 10, whereby higher scores indicate better health. Veteran’s SF-36 Health Survey The Veterans version of the SF-36 (SF36V) is a patient-based questionnaire designed specifically for use among veterans (Kazis et al 1998). In developing the SF36V, the original SF-36 was modified to add mo re precision to the assessment of role functioning (Kazis et al 2004a). These modifications in cluded changing dichotomized yes/no response choices in two of the role items (role limitations due to physical and emotional problems) to a five point ordinal scale. The SF-36V is a reliable and valid measur e of HRQoL and is widely used within the Veterans Health Administration (VHA) (Brazier et al., 1992; Kazis et al., 2004a; Kazis et al 2004b; Kazis et al 1999b; Ware, et al 1995). Items on the scale were shown to be internally consistent, with Cr onbach Apha’s ranging from 0.93 for PCS and 0.78 for MCS (OQP, 2000). The 1999 Veterans Large Health Study (LHS) used the SF-36V to establish baseline health status data on nearly one million veterans. The 1999 LHS established the VA national average for PCS as 36.9 and 45.08 for MCS (Kazis, 2000; OQP, 2000). These two summaries, PCS and MCS, are scor ed using a linear t-score transformation that was normed to a general U.S. population with a mean of 50 and a SD of 10 (Ware & Kosinski, 2001). Based on these results and re sults from past surveys, veteran enrollees report lower levels of health status reflecti ng more disease and health burden than the non-VA civilian population (Kazis, Lee, Ren, Skinner, & Roger, 1999a; Kazis et al.,

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83 1998; Kazis et al., 1999b). The l999 LHS al so reported overall PCS and MCS by the 22 established Veterans Integrated System Networks (VISNs). This study took place in VISN 8, which includes North Florida/South Georgia and is headquartered in Bay Pines, Florida. VISN 8 overall PCS is 35.99 (0.9 less than the national VA average scores), with MCS at 43.59 (1.49 less than the national VA average scores). Development of the Veteran’s SF-12 The SF-12 was developed in an attempt to shorten the SF-36 instrument and, therefore, shorten the time to take or administer the inst rument. The ability to reduce administration time makes the SF-12 an important tool for clinical practice, if the results can assist with decision-making about th e patient. The SF-12 was developed using regression methods to select items and wei ghting algorithms for reproducing the PCS and MCS scales (Ware, Kosinski, & Keller, 1996). A detailed description of the methods utilized for construction of the SF-12 ha s been fully documented (Ware, et al 1996; Ware, Kosinski, Turner-Bowker, & Gandek, 2002). An important factor in development of the SF-12 was the ability to accurately predict SF-36 scores. Based on a study from the general population (n=2,333), the SF-12 achieved multiple R squares of 0.911 and 0.918 in predicting the SF-36 PCS and MCS scores, respectively (Ware, et al 1996). Numerous studies have followed the initial development of the SF-12, and have determin ed the validity and reliability of the measurement for a variety of conditions (Cote, Gregoire, Moisan, & Chabot, 2004; Haywood, Garratt, & Fitzpatrick, 2005; King, Horowitz, Kassam, Yonas, & Roberts, 2005; Resnick & Nahm, 2001; Riddle, Lee, & Stra tford, 2001). In each of these studies, responsiveness to change was less sensitive with the SF-12 than the SF-36, but essentially parallel for patient groups of at least one hundred.

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84 The Veteran’s version of the SF-12 survey ( SF-12V) is a subset of identical items from the Veteran’s version of the SF36. The SF-12V also provides a physical component summary score (PCS-12V) and mental component summary score (MCS12V). The PCS-12V and MCS-12V scales are scored using norm-based methods transformed to have a mean of 50 and a SD of 10. Table 4-1 presents the SF-36V question and the respective SF-12V question. Table 4-1. Short Form Health Survey-36V que stions with respective Short Form Health Survey-12V questions SF-36V SF-12V Question 1 – In general, would you say your health is: Excellent Very Good Good Fair Poor Question 1 Question 2 – Does your health now limit you in these activities? If so, how much? 2b – Moderate activities, such as moving a table, pushing a vacuum cleaner, bowling, or playing golf? Yes, limited a lot Yes, limited a little No, not limited at all Question 2a 2d – Climbing several flights of stairs? Yes, limited a lot Yes, limited a little No, not limited at all Question 2b Question 3 – During the past 4 weeks, have you had any of the following problems with your work or regular daily activities as a result of your physical health? 3b – Accomplished less that you would like: No, none of the time Yes, a little of the time Yes, some of the time Yes, most of the time Yes, all of the time Question 3a

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85 Table 4-1. Continued. SF-36V SF-12V 3c – Were limited in the kind of work or other activities? No, none of the time Yes, a little of the time Yes, some of the time Yes, most of the time Yes, all of the time Question 3b Question 4c – Didn’t do work or ot her activities as carefully as usual: No, none of the time Yes, a little of the time Yes, some of the time Yes, most of the time Yes, all of the time Question 4b Question 7 – During the past 4 w eeks, how much did the pain interfere with your normal work (including both work outside the home and housework)? Not at all A little bit Moderately Quite a bit Extremely Question 5 Question 8 – These questions are about how you feel and how things have been with you during the past 4 weeks. For each question, please give the one answer that comes closest to the way you have been feeling 8d – Have you felt calm and peaceful? All of the time Most of the time A good bit of the time Some of the time A little of the time None of the time Question 6a 8e – Did you have a lot of energy? All of the time Most of the time A good bit of the time Some of the time A little of the time None of the time Question 6b

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86 Table 4-1. Continued. SF-36V SF-12V Question 8f – Have you felt downhearted and blue? All of the time Most of the time A good bit of the time Question 6c Question 9 – During the past 4 weeks, how much of the time has your physical health or emo tional problems interfered with your social activities (like visiting with friends, relatives, etc.)? All of the time Most of the time A good bit of the time Some of the time A little of the time None of the time Question 7 Methods The Veterans SF-36 and Veterans SF-12 we re fully developed and supported by the Veterans Health Study (VHS) (SDR 91006.S, prin cipal investigator Lewis Kazis), which was funded by the VA Health Service Resear ch and Development Service and the VA Center for Health Quality, Outcomes and Economic Research in Washington, DC. Permission to use the SF-36V and the SF-12V for our study was obtained by the VISN 8 Community Care Coordination Service (CCCS) from the developer, Lewis Kazis. There was no cost for use, only that the developer is made aware of any studies or publications that utilize the measurement. Design This portion of the study includes a retros pective analysis of data collected from two telehealth programs funded by the VISN 8 CCCS at the NF/SG VA. Veterans who were enrolled between October 2002 and September 2004 in the Technology Care Coordination Program (TCCP), a telehomecar e program, and the Low ADL Monitoring Program (LAMP), a telerehabilitation program, were included in our study. Please refer

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87 to Chapters 1 and 3 for in-depth informati on regarding these two telehealth programs. Our hypothesis focuses on differences in he alth status between the two telehealth programs from baseline to post 12-months en rollment. The SF-36V and SF-12V were used to measure self-reported physical a nd mental outcomes. Measurements were administered at baseline during the initial enrollment and at 12-months follow-up. All scores were input into the VI SN 8 CCCS database in Bay Pines, FL by their respective telehealth program. The CCCS provided SF36V and SF-12V data on both telehealth programs for this portion of the study. This secondary analysis was approved by the University of Florida and Veterans Admini stration Institutional Review Boards (IRB 439-2005). Participants TCCP is a VA telehomecare program that uses home-based telehealth technology in conjunction with nurse practitioners and a social worker to coordinate care for chronically ill veterans living in remote areas in NF/SG. Veterans are eligible to be enrolled in TCCP if they meet the followi ng criteria: a) past high-cost medical care needs (>$25,000) and high health care utilization (two or more hospitalizations and frequent emergency room visits), b) ha ve electricity and phone service, c) accept technology in their homes for monitoring purposes d) sign an informed consent form or have the consent form signed by a proxy. Part icipants included in this study were veterans enrolled in TCCP between October 2002 and September 2004 who had completed a full year in the program (n=112). Of the 112 enrollees participating in this study, 84 completed a self-report he alth survey both at baselin e and one year follow-up. Of the remaining 28 participants, 26 complete d baseline testing, but were unavailable for

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88 12-month testing or were unable to complete th e survey within an adequate time period. The remaining 2 participants refu sed to participate in either baseline or follow-up testing. LAMP is a VA telerehabilitation program that uses home-based telehealth technology and adaptive equipment/envir onmental modifications (AE/EM) in conjunction with occupational therapists to coordinate care for chronically ill and disabled veterans living in the NF/SG area. Veterans were eligible to be enrolled in LAMP if they met all of the following criter ia: a) lived at home, b) had a functional deficit with at least two ADLs (transferri ng and mobility are considered ADLs for the purposes of inclusion), c) had electricity and phone service, d) accepted technology in their homes for monitoring purposes, e) signe d an informed consent form or had the consent form signed by a proxy. Participants included in this study were enrolled during October 2002 through September 2004 and complete d a full year in the program (n=115). Of the 115 enrollees, 50 were administered a self-report heal th survey both at baseline and one year follow-up. Of the remaining 65 participants, 43 completed only baseline testing, 11 completed only 12-month follow-up testing, and 11 were unable to complete testing based on cognitive concerns or declined to be tested. The LAMP care coordinator reports that due to staff lim itations, manpower was not availa ble to complete the followup testing for many of their enrollees. Administration of the SF-12V Beginning in April 2000, the VISN 8 CCCS initiated funding of se veral telehealth clinical demonstration project s, all of which initially used the SF-36V as a HRQoL outcome measure. Each project administered the HRQoL assessments at baseline during enrollment and at one-year follow-up. In January 2005, the VISN 8 CCCS determined that the SF-36V was lengthy and difficult to administer, and required all telehealth

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89 programs to commence using the shorter versi on (SF-12V). Therefore, for this study’s time period (October 1, 2002 through Septem ber 30, 2004), if a TCCP/LAMP enrollee was in a program for 12 months prior to December 2004, they were administered the SF36V. If a TCCP/LAMP enrollee’s 12 mont h period occurred af ter December 2004, they were administered the SF-12V. Participan ts who were enrolled from January 2004 September 2004 received the SF-36V at base line and the SF-12V in 2005 at one-year follow up. Because the CCCS chose to switc h to the SF-12V during our study period, the SF-12V will be utilized as the primar y measurement for this study aim. Baseline data was collected in the participant’s home during the initial evaluation; twelve-month follow-up data was collected through a home visit or through telephone contact. Each telehealth program was require d to input the health survey results into a database that was managed by CCCS in Bay Pines, FL. CCCS converted all SF-36V scores to SF-12V scores. All health survey data used for this study was supplied to the principal investigat or by VISN 8 CCCS. Scoring Items on the SF-12V are scored so that hi gher scores indicate better health. Raw scores are computed for each of the eight s cales, which is a simple algebraic sum of responses for all items in th at scale. Transformation of raw scores to a 0-100 scale converts the lowest and highest possible score to zero and 10 0, respectively. A z-score for each scale is then computed. Linear tran sformation of each z-score to the norm-based (50-10) score is the final step. Norm-based scoring (NBS) allows for direct comparison and interpretation across all SF-12V scales and summary measures. Scoring algorithms have been judged to be accurate enough to warrant use of published norms for SF-36V summary measures in interpreting SF-12V summary measures (Ware, et al., 1996).

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90 Statistical Analysis Descriptive statistics were used to pres ent demographics of the two samples. TCCP and LAMP baseline and 12-months post-enrollment SF-12V scores were calculated for each of the eight summary scal es and the two component scales. Scoring software was purchased from Quality Metric Incorporated, Lincoln, RI, (2005) to score all SF-12V data in this study (War e et al., 2002). Paired samples ttests were used to evaluate the difference in baseline and 12-month follow-up scores for each of the telehealth programs; a one-way analysis of variance (ANOVA) was used to compare the PCS-V and MCS-V scores of the two teleheal th groups at baseline and one-year followup. Multiple linear regression analysis was used to determine whether SF-12V PCS scores could be predicted at baseline or 12months follow-up from the variables of age, marital status, inpatient bed days of care, or diagnoses. All statistical analyses were performed using SPSS software version 12.0 (SPSS, Inc., Chicago, IL), with significant level set at .05. Results Baseline demographics of the TCCP partic ipants (n=84) and LAMP participants (n=50) are presented in Table 4-2. The averag e age of the two samples is 71 years, with 53% married. The entire sample includes 4 females, and 90% are Caucasian. An independent samples ttest comparing TCCP and LAMP participants diagnoses found significant differences in arthritis ( t(128)= 4.66, p>.001), stroke ( t(128)= 2.66, p>.01), and amputation ( t(128)= -2 .06, p>.05), with these diagnoses si gnificantly highe r in the LAMP group.

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91 Table 4-2. Characteristics of participants LAMP (n=50) TCCP (n=84) M S/D % M S/D % Age 71.9 9.7 70.4 10.9 Married 56.0 50.0 Diagnosis Arthritis 50.0 12.5 CHF 8.0 13.8 COPD 22.0 12.5 Diabetes 24.0 40.0 Hypertension 62.0 72.5 Stroke 20.0 3.8 Amputation 8.0 0.0 Table 4-3 presents the paired samples ttest calculations comparing the mean pretest SF-12V scores to the mean post-test SF-12V scores for our telerehabilitation group (LAMP). Due to the variability in some of the summary scales, the non-parametric Wilcoxon test was also used to examine the re sults, which did not ch ange our findings. A significant increase from pre-test at baseline to post-test at 12-months was found for the Role Physical summary scale (p< .001) and the Physical Component Scale (PCS) (p< .001). A significant increase from baseline to 12-months was also determined for bodily pain (p< .001). The standard error of the measurement (SEM) has been proposed as a useful estimate for meaningful change in HR QoL measures (McHorney & Tarlov, 1995; Wyrwich & Wolinsky, 2000). To calculate the SEM, we use the reliability coefficient of the measurement and the standard deviation (SD) of the sample at baseline. The SEM was computed for the LAMP PCS scores. The reliability component of .93 was used based on results from the 1999 LHS (OQP, 2000), as well as the PCS baseline SD of 8.3. Therefore, SEM = 8.3 1-0.93 = 2.19.

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92 Cohen (1988) developed effect size benchm arks for evaluation of group change over time. The effect size is calculated using scores at baseline and subtracting scores at follow-up, then dividing by the SD at base line. Cohen’s guidelines for effect size standards suggest 0.2 for a small group change 0.5 for a moderate group effect, and 0.8 for a large group change (Cohen, 1988). The st andardized difference between the means at baseline and 12-month follow-up in LAMP PC S scores represents a small to medium effect size of .425, with no effect size in MCS scores Table 4-3. Differences between SF-12V ba seline and 12-month follow-up for LAMP (paired sample statistics) LAMP SF-12V Baseline 12-Month Differences M + S/D M + S/D t (49) Summary Scales Physical Function 24.17 + 5.9 25.03 + 1.1 -1.219 Role Physical 32.48 + 12.5 40.23 + 15.0 -2.964** Bodily Pain 38.69 + 13.9 42.97 + 14.8 -2.636** General Health 33.49 + 13.9 33.36 + 13.6 0.065 Vitality 37.08 + 11.9 37.28 + 11.5 -0.155 Social Function 41.22 + 13.7 43.84 + 13.4 -1.241 Role Emotional 45.01 + 15.4 46.91 + 14.6 -0.703 Mental Health 47.72 + 12.8 46.74 + 13.2 0.683 Component Scales Physical (PCS) 26.34 + 8.3 30.59 + 9.3 -3.619*** Mental (MCS) 50.78 + 12.5 50.32 + 10.7 0.310 *p< .05 **p< .01 ***p< .001 Table 4-4 presents the paired samples ttest calculations comparing the mean pretest SF-12V scores to the mean post-te st SF-12V scores for the telehomecare group (TCCP). Additionally, we used a non-parametr ic Wilcoxon test to examine the results, which did not change our findi ngs. A significant increase fro m pre-test at baseline to post-test at 12-months was found for the So cial Functioning summar y scale only. Based on the standardized difference between the m eans at baseline and 12-month follow-up in PCS and MCS scores, no effect size was detected (Cohen, 1988).

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93 Table 4-4. Differences between SF-12V ba seline and 12-month follow-up for TCCP (paired sample statistics) TCCP SF-12V Baseline 12-Month Differences M + S/D M + S/D t (83) Summary Scales Physical Function 30.70 + 11.5 28.96 + 9.8 3.717 Role Physical 33.70 + 10.2 35.90 + 10.7 0.363 Bodily Pain 42.40 + 12.4 41.91 + 12.3 3.142 General Health 34.99 + 10.8 36.63 + 10.5 0.706 Vitality 39.72 + 9.1 40.20 + 10.6 1.892 Social Function 34.57 + 11.1 39.98 + 12.3 -2.637*** Role Emotional 46.76 + 12.9 44.50 + 12.8 4.942 Mental Health 48.72 + 9.1 48.29 + 8.9 2.258 Component Scales Physical (PCS) 30.66 + 10.8 31.45 + 9.3 1.201 Mental (MCS) 48.83 + 10.9 49.35 + 11.1 1.565 *p< .05 **p< .01 ***p< .001 A one-way ANOVA was computed comparing the baseline SF-12V PCS scores of participants from LAMP and TCCP. A si gnificant difference was found among the two groups (F(,132)=5.86, p<.05). This analysis reveals th at LAMP participants reported lower physical functioning at baseline than TCCP participants. Follow-up PCS scores were then compared between the two groups to determine if significant differences continue d to exist following 12 mont hs of intervention. A oneway ANOVA comparing the post 12-months enro llment PCS scores of participants from LAMP and TCCP were computed. At 12-m onths, no significant differences were found (F(,132)= .268, p>.05). This analysis reveals that LAMP and TCCP participants were no longer significantly different in physical functioning following 12-months of the telehealth interventions. A multiple linear regression was calculated for LAMP participants to determine whether we could predict SF-12V PCS scores at baseline or 12month follow-up based on age, marital status, pre-enrollment inpa tient BDOC, or diagnoses. A significant

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94 regression equation was found fo r baseline SF-12V PCS scores based on age and marital status (F(10,39) = 2.512, p <.05) with an R2 of .392). Subjects’ predicted SF-12V PCS increased .240 points with each year of age, and were 2.09 points higher for our married population. Multiple regression analyses we re then computed for LAMP post SF-12V PCS scores. At 12-months post enrollment, the association between SF-12V PCS scores and age or marital status was no longer significant (F(11,38) = .1.627, p >.05 with an R2 of .294). In comparison, a multiple linear regression was calculated for TCCP participants to determine whether we could predict baseline or 12-month SF-12V PCS scores based on age, marital status, pre-enrollment inpatient BDOC, or diagnoses. No significant regression equation was found for ba seline TCCP SF-12V PCS scores (F(8,75) = .420, p >.05), or 12-months post enroll ment SF -12V PCS scores (F(8,75) = .531, p >.05). Post-hoc analyses A post-hoc analysis was performed to address the missing SF12 data for both LAMP and TCCP participan ts. As reported earlier, of the 115 LAMP participants, 43 were administered the m easurement at baseline but not at 12-month follow-up due to the limited staff available to complete the follow-up testing. Baseline scores were computed for these 43 partic ipants. Comparisons between those with baseline scores who had missing data at 12 m onths (n=43), and those with baseline and 12-month follow-up scores (n=50) are presen ted in Table 4-5. Independent samples t tests conclude that there were no signif icant differences between these two LAMP baseline samples.

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95 Table 4-5. Group differences for SF-12V baseline scores SF-12V LAMP (n=50) LAMP Baseline-only (43) Individual Differences M + S/D M + S/D t (91) Summary Scales Physical Function 24.17 + 5.9 23.31 + 3.55 0.868 Role Physical 32.48 + 12.5 32.64 + 13.02 -0.060 Bodily Pain 38.69 + 13.9 35.86 + 14.61 0.948 General Health 33.49 + 13.9 29.75 + 11.30 1.432 Vitality 37.08 + 11.9 35.11 + 9.59 0.882 Social Function 41.22 + 13.7 38.25 + 13.09 1.067 Role Emotional 45.01 + 15.4 44.16 + 15.27 0.280 Mental Health 47.72 + 12.8 47.325 + 11.74 0.185 Component Scales Physical (PCS) 26.34 + 8.3 24.44 + 8.08 1.113 Mental (MCS) 50.78 + 12.5 49.42 + 11.14 0.556 *p< .05 **p< .01 ***p< .001 Of the 115 LAMP participants, 11 particip ants received the measurement at 12month follow-up only. LAMP reports that on occasion a participant does not receive the SF-12V at baseline due to time factors, the pa rticipant has become tired, or the participant declined to be tested further. Table 4-6 presents the diffe rence in scores at 12-months between the 50 participants that received th e SF-12V at the two time periods and the 11 participants who only receive d the SF-12V at their 12-month follow-up assessment. Based on independent samples t -tests, significant differences were noted in the general health summary scale only. Table 4-6. Group differences fo r SF-12V at 12-month follow-up SF-12V LAMP (n=50) LAMP Followup only (n=11) Individual Differences M + S/D M + S/D t (59) Summary Scales Physical Function 25.03 + 1.1 22.89 + 2.59 1.599 Role Physical 40.23 + 15.0 30.79 + 13.68 2.033 Bodily Pain 42.97 + 14.8 37.98 + 17.32 0.885 General Health 33.36 + 13.6 24.75 + 5.63 3.354* Vitality 37.28 + 11.5 34.94 + 9.10 0.734

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96 Table 4-6. Continued. SF-12V LAMP (n=50) LAMP Followup only (n=11) Individual Differences M + S/D M + S/D t (59) Social Function 43.84 + 13.4 36.37 + 16.90 1.375 Role Emotional 46.91 + 14.6 38.80 + 17.59 1.426 Mental Health 46.74 + 13.2 40.71 + 13.75 1.325 Component Scales Physical (PCS) 30.59 + 9.3 25.51 + 7.54 1.919 Mental (MCS) 50.32 + 10.7 43.59 + 13.16 1.586 *p< .05 **p< .01 ***p< .001 For our telehomecare group (TCCP), of th e 112 participants, 84 completed the SF12V at baseline and 12-months, 26 comple ted the SF-12V at baseline only, and the remaining 2 declined to be tested. Table 4-7 presents a comparison between the baseline data of the two TCCP groups. Based on independent samples t -tests, no significant differences were observed between th ese two groups’ baseline scores. Table 4-7. TCCP group differe nces for SF-12V at baseline SF-12V TCCP (n=84) TCCP Baselineonly (n=26) Individual Differences M + S/D M + S/D t (108) Summary Scales Physical Function 30.70 + 11.5 27.73 + 10.01 1.278 Role Physical 33.70 + 10.2 35.92 + 7.93 -1.156 Bodily Pain 42.40 + 12.4 44.51 + 10.61 -0.850 General Health 34.99 + 10.8 36.45 + 10.70 -0.607 Vitality 39.72 + 9.1 39.62 + 11.03 0.043 Social Function 34.57 + 11.1 37.92 + 12.68 -1.214 Role Emotional 46.76 + 12.9 46.62 + 11.60 0.054 Mental Health 48.72 + 9.1 48.36 + 7.50 0.202 Component Scales Physical (PCS) 30.66 + 10.8 31.29 + 9.13 -0.293 Mental (MCS) 48.83 + 10.9 49.66 + 8.43 -0.406 *p< .05 **p< .01 ***p< .001 The 1999 Large Health Study (LHS) of Vetera n Enrollees provides the first largescale study based on approximately 43 percent of the veteran enrollee population (OQP,

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97 2000). The LHS report provides baseline norms for the SF-36V, and identifies the health status of veteran enrollees by the twenty-two VISNs, including norms based on age and primary medical condition. As a post-hoc anal ysis, cross-sectional relationships between presence of primary medical condition and PCS scores were explored at ba seline and 12months for our two telehealth groups. Thes e analyses were then compared to the VA PCS norms established from the 1999 LHS for VISN 8. For purposes of this study, both telehealth cohorts were combined. Based on the results, significant differences exist between the telehealth cohorts and the VA VISN 8 average for all primary medication conditions, with the telehealth group presenting with lower PCS scores at baseline and at 12-month follow-up. Although norms were not provided for the diagnosis of amputation, our telehealth cohort with the primary di agnosis of amputation reported significantly higher PCS scores from baseline to 12-month follow-up. Table 4-8. Cross-sectional re lationship between presence of primary medical condition, physical component summary (PCS-12) at baseline and 12 months for two telehealth cohorts (LAMP and TCCP n=229), and VA PCS norms from 1999 Large Health Study for VISN 8 (n=75,163). Percentage of enrollees with medical condition presented in parentheses. Medical Condition PCSBaseline PCS12 Months 1999 LHS Prevalence and PCS Norms Mean + Standard Deviation Arthritis (31%) 25.30 + 8.0 29.96 + 8.4 (34.1%) 38.7 + 10.3*** Hypertension (67%) 28.26 + 10.0 30.33 + 8.7 (44.2%) 44.44 + 10.2*** CHF (13%) 27.26 + 3.3 29.83 + 7.5 (5.6%) 39.16 + 10.6*** COPD (20%) 27.79 + 8.3 29.94 + 10.8 (16.1%) 38.14 + 10.8***

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98 Table 4-8. Continued. Medical Condition PCSBaseline PCS12 Months 1999 LHS Prevalence and PCS Norms Mean + Standard Deviation Diabetes (33%) 29.48 + 11.6 31.04 + 8.8 (18.1%) 41.92 + 10.7*** Stroke (21%) 28.83 + 11.5 30.30 + 10.3 (5.9%) N/A Amputation (4%) 32.56 + 16.1 39.15 + 11.4** N/A Total Sample Mean 28.50 31.51 35.99** *p< .05 **p< .01 ***p< .001 Discussion The SF-12 Short Form Health Survey wa s developed to describe mental and physical health status of adu lts and to measure the outcomes of healthcare services. The SF-12 has been deemed a reliable and valid meas ure of health status and has been used as both a predictor and an outcome measure (R esnick & Nahm, 2001). Responsiveness to change has been measured with the SF-12 in patients with cerebral aneurysms (MullerNordhorn et al 2005; Pickard, Johnson, Penn, Lau, & Noseworthy, 1999), spinal cord injuries (Andresen, Fouts, & Romeis, 1999), lo w back pain (Deyo et al., 1998; Riddle et al., 2001), hypertension (Cote et al., 2004), and chronic illnesses ( Haywood et al., 2005; Resnick & Nahm, 2001; J. Ware, Jr. et al 1996). The 1998 National Survey of Veterans in Ambulatory Care utilized the veteran’s version of the SF-36 and developed norms for veteran enrollees, which were determined to be significantly lower than the genera l non-VA population (Kazis et al., 1998; Kazis et al., 1999b). In 1999, the Large Health Study of Veteran Enrollees (LHS) (OQP, 2000)

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99 established updated baseline health status da ta on approximately one million veteran enrollees. Based on the 1999 LHS, the VA national average for PCS is 36.9, which falls approximately 1.3 standard deviations be low the U.S. population. The VA national average for MCS is 45.1, which is approximate ly 0.50 of one standard deviation below the U.S. population. The LHS also stratified data in order to provide informati on pertaining to the twenty-two VA VISNs based on age groups and primary diagnosis. For all age groups (18-49, 50-64, and 65-98), average PCS and MCS scores for veteran enrollees in VISN 8 (n=75,763) fall below the VA national average. VISN 8 veteran enrollees aged 65 to 98 exhibit lower physical health scores (PCS) when compared to younger VISN 8 veterans aged 18-49 and 50-64 years. For all age gr oups, VISN 8 average scores for PCS were 36.0, with individuals aged 65-98 scoring 34.09. PCS scores for th is age group (65-98) are approximately 1.6 standard deviations below the US general population, and 0.2 standard deviations below the VISN-8 averag e. The l999 LHS reported that most VISNs in the south regions exhibit lower PCS scor es, indicating greater disease burden. Between October 2002 and September 2004, 115 veterans were enrolled in the LAMP telerehabilitation program, and 112 veterans were enrolled in the TCCP telehomecare program. Forty-three percent of LAMP enrollees (n=50) and 63 percent of TCCP enrollees (n=84) completed the SF-36V or SF-12V health survey at baseline and 12-months. The mean age of the LAMP partic ipants (n=50) was approximately 72 years; the mean age of the TCCP patients (n=84) was 70 years. Fifty percent or more of the 50 LAMP and TCCP patients reported they we re married, which may signify they had a caregiver at home able to provi de assistance if needed.

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100 The SF-12V PCS scores include questions co ncerning one’s ability to participate in daily physical activities, such as climbing stairs, how energeti c or vital one fees, as well how their physical abilities affect their social and work roles. Based on results from the LAMP and TCCP cohorts, the veteran enroll ees served by these telehealth programs report significantly wors e health in the physical domain (PCS) than that of the general population, the overall veteran enrollee popul ation, the overall VISN 8 veteran population, and the VISN 8 vete ran population aged 65-98. This demonstrates a highly skewed population, which is not symmetrically distributed about the population mean. Based on baseline and one year follow-up, the SF-12V PCS scores for our veteran telehealth participants fall at least 2 standa rd deviations below the mean of the general population. There was a significant increase reported in SF-12V PCS scores for the LAMP participants from baseline to 12-month follow-up. It is difficult to determine whether this increase in scores is clinically relevant or individually meaningful. Stadnyk and colleagues tested the measurement properties of the SF-36 in a frail elderly cohort, and determined that this measurement may not be su itable to detect clinical change in this population (Stadnyk, Calder, & Rockwood, 1998). Additional studies report that the SF36 may be insensitive to important clinical change since it contains items that are not clinically relevant or typically focused on during treatment (McHorney, 1996; Wright & Young, 1997). In an attempt to discuss and ev aluate meaningful change in scores, we estimated the standard error of measurem ent (SEM) (Wolinsky, Wan, & Tierney, 1998; Wyrwich, Tierney, & Wolinsky, 1999b) and the effect size (Cohen, 1988) in PCS scores for each of our telehealth cohorts.

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101 The standard error of measurement (SEM ) was calculated to explore clinical significance in LAMP baseline and 12-mont h PCS scores, due to the significant difference shown between the pre and post in tervention scores. The SEM value can be used as an estimate of significance for a group. The SEM is computed by using the reliability coefficient of the measure, which takes into consideration the possibility that some of the change from baseline to follo w-up may be due to random measurement error (Wyrwich et al 1999b; Wyrwich & Wolinsky, 2000). The SEM is considered sample independent and remains relatively constant across the population te sted. McHorney & Tarlov used a value of 1.96 SEM’s, reflecti ng a 95% confidence interval, as the minimal amount of change needed to demonstrate true change (McHorney & Tarlov, 1995). More modest SEM change thresholds have also been used (Wolinsky et al., 1998; Wyrwich, Nienaber, Tierney, & Wolinsky, 1999a), but to date SEM-based criterion for clinically relevant HRQoL change, specifically th e SF-36, has not been established (Samsa et al 1999; Ware, Jr. & Gandek, 1998). Using McHorney & Tarlov’s more restrictive threshold, the increase in PCS scores from baseline to 12-month on our LAMP population (+4.25) falls within of the 95% ra nge (2 SEM = 4.38), and therefore does not represent a clinically si gnificant change. It should be noted that some researchers feel that 1 SEM is acceptable to determine clin ically significant change (Wyrwich et al 1999b; Wyrwich & Wolinsky, 2000). Ferguson a nd colleagues report that the SEM alone does not indicate clinical significance and that any post-intervention score must fall within the range of normativ e values (Ferguson, Robinson, & Splaine, 2002). We do not meet this criterion as LAMP pre and post PCS scores fall at least two SD below the general norms and approximately one SD below the general veteran norms. Additionally,

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102 we would assume that we would see regres sion to the mean for responses which are clustered at such extreme values. The use of effect size sta tistics (Cohen, 1988) has been questioned when evaluating clinical significance in health-related quality of life (HRQoL) meas urements, as Cohen’s original benchmarks were not derived from any HRQoL or health status measurements (Kazis, Anderson, & Meenan, 1989; Wyrwic h & Wolinsky, 2000). Additionally, since effect size statistics use the average change divided by the baseline st andard deviation of the sample, effect sizes can vary among samp les taken from the same population (Samsa et al., 1999). When evaluati ng effect size in both LAMP and TCCP PCS scores from baseline to one-year follow-up, the LAMP population had an eff ect size that was considered small to moderate by Cohen’s definition (.425). Unfortunately, we do not have power to justify this effect, as our sample size was 50 subj ects and 100 subjects would be required for an effect size of .425 with power at .80. A one-way ANOVA was computed compar ing the baseline PCS scores of participants from LAMP and TCCP. A si gnificant difference was found among the two groups (F(,132)=5.86, p<.05). This analysis reveals th at LAMP participants report lower physical functioning at baseline th an TCCP participants. This ma y be due to that fact that functional disabilities, which may be experienced in individuals with arthritis, stroke and amputations are higher in the LAMP populat ion. LAMP patients also report a higher level of pain, which has been shown to co rrelate with lower PCS scores (Wright & Young, 1997). This significant difference in SF-12V PCS scores between the two groups was no longer visible at 12 months post-intervention, as LAMP scores increased significantly and TCCP scores remained stab le during the 12 months of treatment.

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103 Regression analyses indicate whether or not a significant prediction regarding the variable can be made, as well as the directi on of the relationship. Numerous studies have shown correlations between the SF-36 and/ or the SF-12 and sociodemographics and morbidities (Cote et al., 2004; Kazis et al., 2004b; Kazis et al., 1999b; King et al., 2005; Weeks et al., 2004). A multiple linear regression was calculated for LAMP participants in an attempt to predict baseline SF-12V PCS sc ores. The variables of age, marital status, pre-enrollment inpatient BDOC, and diagnoses were used in the model. A significant regression equation was found for LAMP base line SF-12V PCS scores based on age and marital status, but diagnoses and hospital bed days of care were not significant. At 12months post enrollment in LAMP, the asso ciation between SF-12V PCS scores and age or marital status is no longer significant. In comparison, multiple linear regressions were calculated for TCCP participants at baseline and post-12 months’ enrollment to determine whether we could predict SF-12V PCS scor es based on age, marital status, preenrollment inpatient BDOC, and diagnoses No significant regression equation was found for baseline or 12-months post enrollment TCCP SF-12V PCS scores. It may be that our samples are too small to detect signif icant relationships between these variables. As a post-hoc analysis, we analyzed th e missing data for LAMP and TCCP to determine if there were significant differen ces between the two samples. Of the 115 LAMP participants, we were provided with pa ired samples on only 50 of the participants. An additional 43 participants received baseline testing, but were not tested at 12-month follow-up, and 11 participants received the measurement at 12-month follow-up, but do not have baseline scores. There were no signi ficant differences not ed in the SF-12V PCS scores between the baseline pa ired samples (n=50) and the baseline only samples (n=43),

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104 or the 12-month paired samples (n=50) a nd the 12-month only samples (n=11). Combining these samples would no longer allow us to use a repeated measures or paired samples design. Yet, the fact that we do not see significant differences in the samples may strengthen our final results, and allow us to uphold our conclu sion that there was a significant increase in LAMP SF-12V PCS scores from baseline to12-months, even when considering the large amount of missing data For our telehomecare group (TCCP), of the 112 participants, 84 completed the SF-12V at baseline and 12-months, 26 completed the SF-12V at baseline only, and the remaining 2 declined to be tested. No significant differences were observed between these tw o groups’ baseline scores, allowing us to conclude that the missing data woul d not have altered our final outcome. There are some limitations that need to be addressed. Health surveys, such as the SF-12V, can provide information on quality of car e and clinical effectiveness. Yet, the use of the SF-12V as a physical measurement to determine an individual’s ability to function within the home has not been validated The results from this study do not allow us to make clinical judgments about these patient s or the effects of eith er of the telehealth interventions without eviden ce from a matched comparison group or further long-term follow-up study. As our intervention groups were not random ly assigned, this is not a true pretestposttest control group design. Therefore, we cannot determine a cause-and-effect relationship between the intervention a nd physical functioning based on the SF-12V alone. Some experts have questioned whether th e SF-36/SF-12 is appropriate for a frail elderly population (McHor ney, 1996; Stadnyk et al 1998). We also have to question

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105 whether the use of this health outcomes instrument is a ppropriate for our population of interest. Results from their SF-12V demonstr ate that this population is skewed toward illness; therefore it is very difficult to show change. It is important to note that as this is a frail population, decline would be expected, especially within a 12 month time period. As the SF-12V demonstrates, these two groups were able to at least mainta in their scores over time. For this population, staying the same may be seen as a sign of a successful intervention.

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106 CHAPTER 5 PERSONAL INTERVIEWS FROM TELEHEALTH PARTICIPANTS Exact sciences give correct answers to certain aspects of life problems, but very incomplete answers. It is important of course to count a nd measure what is countable and measurable, but the most precious values in human life are aspirations which laboratory experiment s cannot yet reproduce (Cousins & Dubos, 1979) [page 279]. Qualitative Research and Healthcare The application of qualitative studies within healthcare outcomes research is novel, but growing in degree and importance. Qua litative research provides a unique means for assessing a healthcare program or interv ention, and has been deemed useful in illuminating the findings of healthcare out comes studies (Hatch, 2002; Pope & Mays, 1995; Pope, van Royen, & Baker, 2002). There is value in the use of qualitative studies in determining clinical interventions and s ubsequently assessing the effects of these interventions (Pope et al 2002; Shortell, 1999). Qualitative research involves the collecti on, analysis, and interpretation of personal data which is not easily reduced to number s (Creswell, 2003). Qualitative research in healthcare is largely concerned with th e people who participate in healthcare interventions; the recipients of healthcare se rvices. Interviews are most often used in healthcare research to determine how consum ers evaluate their services, including the strengths and weaknesses of an intervention, as well as what personal attitudes motivate consumers to comply with interventi on guidelines (Murphy, Dingwall, Greatbatch, Parker, & Watson, 1998). Pope et al. states th at, “quality of serv ices can no longer be

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107 confined to simply monitoring such asp ects as waiting time, but requires an understanding of the patient’s experien ce of waiting for care” [page 148]. Typically, the evaluation of healthcare programs concentrates on cost analyses, yet it is important that program evaluations ar e not based on cost calculations alone. The larger more complex issue of the patient’s perceived value and be nefit of the program should also be included. Qualitative rese arch can contribute significantly to our understanding of a patients' e xperience of chronic illness an d disability, and their views on health education and healthcare delivery. Nowhere is this more important than in homecare or interventions that are delivered to the home. As here, control ultimately rests with the patient (Magnusson & Hanson, 2003). Despite numerous studies reporting on cost savings for telehealth programs, few studies have investigated part icipant perspectives regardi ng the home-based intervention or the use of technology to remotely connect with healthcare provi ders (Dhurjaty, 2004; Hebert & Korabek, 2004; May et al ., 2002; Nodhturft et al., 2000) Hebert and Korabek conducted focus groups and personal interviews with frail elders who were currently receiving telehealth services to obtain th eir initial reactions on the use of telehomecare equipment. Themes included payment for t echnology, criteria for client selection, and most importantly, the potential loss of human touch, which was s een as essential for care. Most clients felt that telehomecare would not be adequate without the addition of visits by home healthcare staff. Magnusson a nd Hanson (2003) provided an overview of ethical issues, which arose during the field-st udy of a telehomecare project. A majority of the families involved reported that they f ound the technology easy to use and of direct benefit to them in their daily life. Issues of confidentiality and privacy were raised with

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108 the use of videophones. As with Hebert’s study, fears that the t echnology would replace healthcare staff were initially raised, but once the positive effect was observed, these opinions changed. Dhurjaty (2004) focused on telerehabilitation and associated costs to patients, providers, payers and corporations Patients reported that collaboration with their therapists was a positive experience and that telerehabilitation reduced travel time and associated costs. Yet, his focus was on the telehealth systems manufacturers and making a business case for telerehabilitation. Mann et al. (2001) examined frail elders’ acceptance of the concept of home monitori ng devices. Results suggested the strong acceptance of home health monitoring and the monitoring devices. Further research was suggested regarding patients’ actual per ceptions of using home monitoring devices. Additional studies focused on evaluating stakeh older readiness and assessing the needs of potential users (Hebert & Korabek, 2004; Jennett et al 2005). Given that the VA recognizes the need to have veterans become more actively involved in their healthcare (N odhturft et al., 2000), the aim of this qualitative study is to explore veterans’ satisfaction with health care at a distance through enrollment in a telehealth program. To supplement quantitat ive findings, information was obtained from individuals who have experienced the application of teleheal th and have personally used the technology. Personal feeli ngs regarding use of technolog y in this population affects the individuals themselves and has implicat ions on the success or failure of the intervention. Role of the principal investigator. I cannot state that I’m impartial to the outcomes of this study, or that I care not what the in terviewees have experien ced. I believe in the concept of telehealth, and the direct benef it that the VA population may receive from the

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109 provision of telehealth servi ces into their homes. This partiality stems from my background as an occupational therapist, a nd my direct connection with the VA and telehealth. In the summer of 2002, I was accepted to gr aduate school to pursue a doctoral degree in rehabilitation science (RSD) at the University of Florida (UF). In the fall of 2002, Dr. William Mann, chairperso n of the UF RSD program, was awarded a grant from the VA to conduct a 2-year telerehabilitation clinical demonstration project focused on the use of assistive techno logy / adaptive equipment (AT/ AE) and home monitoring to reduce healthcare costs and increase functional independence for chroni cally ill veterans. The project was titled the Low ADL Monito ring Project (LAMP). I worked as an occupational therapist and care coordinato r for LAMP through the grant funding cycle from October 1, 2002 to September 30, 2004. Ha ving been an intrinsic part of LAMP from its inception is the most obviou s bias in this qualitative study. I, as well as the other LAMP team memb ers, worked very hard to ensure the success of LAMP. It was my job. I worked cl osely with the veterans I traveled to their homes, I was their care coordi nator, and I assisted them in obtaining the resources they needed to manage their illness, to maintain independence, and to live safely in their home. The rehabilitation aspect of LAMP was the most important to me. The LAMP model allowed for a complete evaluation of a patient within their home environment, provision of AT/AE, training on the equipment, and monitoring or self-care and health related needs. The ability to work one-on-one with each patient, to problem solve and individualize solutions was of ultimate benefit to me, as well as the patient. I believe fully in the LAMP model, as well as the vision of telehealth.

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110 I understand that not every elderly veteran n eeds a grab bar, or a ramp, or a reacher; many veterans are functionally independent and may only require symptom tracking and medical interventions when their symptoms ha ve progressed. Yet during my interviews, I met veterans enrolled in the telehomecare program (TCCP) who voiced a need for adaptive equipment due to safety issues with in their home environments. I believe it’s important that these concerns are monitored. That’s the personal bias I bring to this project. I did not interview any LAMP participan ts for whom I served as their care coordinator. I did not discuss my service to the VA or LAMP with any interviewee, although a few of the LAMP interviewees kne w of my association with LAMP, which may have biased their answers to my ques tions. Through my personal discussions and the signed informed consent, I acknowledged to all participants that nothing they said during the interviews would affect their healthcare or their participation in the telehealth program. I discussed with all participants th e importance of hearing their personal stories related to their experience with telehealth. My focus was to learn of the strengths and weaknesses of the telehealth programs; theref ore, everything that was said during the interviews, positive or negative, was considered valuable to ensure these telehealth programs were the best they could be. Methods Selection of Subjects To ensure that the sample was consistent with the intention of the inquiry, a purposeful selection of subject s was made. All veterans in cluded in this study met the initial program inclusion criteria, were curren tly enrolled, and had participated in LAMP or TCCP for at least one year.

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111 Care Coordinators (CC) from LAMP and TCCP initially screened enrollees during regularly scheduled contacts to determine th eir interest in participating in personal interviews in their homes. If the veteran enro llee verbally agreed to be contacted for a personal interview, a telephone consent form was read, completed and signed by the CC. Following signed and dated telephone consent, I (the principal investigator) then telephoned the veteran to further discuss the purpose of the study and to schedule a convenient time for a personal interview in the veteran’s ho me. Prior to the scheduled interview, I telephoned the ve teran a second time to confirm the appointment and to obtain directions to their home. Table 5-1 presents demographic informa tion from the TCCP and LAMP sample. Aliases have been used in place of real names. Additional information provided includes age, diagnoses, marital status, and type of technology used for remote monitoring. Table 5-1. TCCP and LAMP sample demographics Name Age Program Diagnoses Marital Status Technology Jim 69 TCCP Arthritis Hypertension Diabetes Married Health Buddy Jeff 61 TCCP CHF Diabetes Married Health Buddy Joseph 83 TCCP Hypertension Married Health Buddy Jack 63 TCCP Hypertension COPD Not Married Health Buddy Jessie 80 TCCP Hypertension CHF Diabetes Married Videophone James 57 TCCP Diabetes Not Married Videophone John 78 TCCP COPD Not Married Health Buddy Mary 79 LAMP Arthritis Hypertension Married Smartphone Mark 77 LAMP Arthritis Hypertension Married Computer

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112 Table 5-1. Continued. Name Age Program Diagnoses Marital Status Technology Mitchell 81 LAMP Arthritis Hypertension Married Computer Mike 70 LAMP Arthritis Hypertension Diabetes Married Health Buddy Martin 74 LAMP Hypertension CHF Post-Stroke Not Married Health Buddy Mick 79 LAMP CHF Married Computer Merle 73 LAMP Arthritis Hypertension Married Health Buddy Mack 53 LAMP Muscular Dystrophy Married Health Buddy Milton 70 LAMP Arthritis Post-Stroke Married Computer Data Collection Semi-structured interviews were conducted with 16 veterans who had been enrolled in one of the telehealth progr ams for at least one year. Seven veterans enrolled in the telehomecare program (TCCP) and 9 veterans en rolled in the telerehabilitation program (LAMP) were interviewed in their homes. Pa rticipants signed an informed consent form prior to the initiation of the interview. A ny questions they expressed were answered in full to their satisfaction before they were given the opportunity to sign the informed consent form and be included in the study. If a witness was available, they were asked to verify each subject’s signature. The interv iewees were given a copy for their personal use, and the principal investigator retained the original signed c opy. Prospective data was obtained through single, face-to-face in terviews within the veteran’s home. Interviews were semi-structured in nature a nd were digitally record ed. Veterans were contacted by telephone within two months following the in terview to ensure that interpretation of their comments was as accurate as possible.

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113 Appendix A presents the Interview Guide, which was approved by the University of Florida Institutional Review Board and the VA Subcommittee for Clinical Investigation. Interview questi ons were viewed as a list of information to obtain from the interviewees; the particular wording or orde r in which the questions arose was adapted for each individual interview. Five to 10 minutes was typically spent with each participant prior to beginni ng the interview in order to develop rapport and, therefore, attempt to solicit more accurate and descriptiv e answers. During the interview process, answers to certain questions were often acquired through stories or conversations regarding other inte rview topics. Coding Process Data were collected through semi-structured interviews with 7 veterans enrolled in TCCP and 9 veterans enrolled in LAMP (n=16) Personal interviews were held in the participant’s homes. Interviews were record ed through a digital recorder, and data were transcribed into Word documents, read thoroughly, and then coded. Codes and coded data were analyzed and interpreted using c ontent analysis. Conten t analysis has been defined as a systematic, replicable techni que for compressing many words of text into fewer content categories based on explicit rules of coding (Cre swell, 2003; General Accounting Office [GAO], 1996; Krippendorff, 1980; Weber, 1990). Content analysis is a research tool used to determine the presence of certain words or concepts within texts or sets of texts, such as interviews. Weber (1990) states that cont ent analysis can be a useful technique for discoveri ng and describing the focus of an individual or group, and can be an important component for a program evaluation. Moreover, as these interviews are only one aspect of the telehealth pr ogram evaluation, content analysis allows

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114 inferences to be made, which can then be supported using other methods of data collection. To conduct the content analysis, text from each interview was coded into categories on a variety of levels, such as a word, phrase, or senten ce. Weber (1990) defines a category as, "a group of words with similar meaning or connotations" [p. 37]. The text was then examined using a basic content an alysis method: conceptual analysis. Conceptual analysis establishes the existen ce and frequency of concepts – most often represented by words or phrases – in a text. The technique of conceptual analysis extends far beyond simple word frequency counts. In co nceptual analysis, a concept is chosen for examination, such as connectedness with the ca re coordinator, and the analysis involves quantifying its presence (Palmquist, Carley, & Dale, 1997). The focus was to search for the occurrence of selected terms, either implicit or explicit, within the interview. The use of emergent coding allowed for categories to be established following preliminary examination of the data, but specific a priori questions guided the organization and coding of the data (see Interv iew Guide, Appendix A). Overall, the goal was to gain information regard ing the veteran’s perception of the telehealth program they were enrolled in, their thought s on the technology used fo r remote monitoring, their thoughts on any assistive devices they had received, their und erstanding of the role of their VA care coordinator, and in general how satisfied they were with the telehealth program. In order to obtain a general sense of th e information and to reflect on the overall meaning of the interviews, I read through the data three times, on varying dates. On the initial reading, notes were written in the ma rgins of the transcri bed data and general

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115 thoughts about the data were recorded at this stage. General thoughts included both conceptual and concrete ideas of communi cation, connection, security, independence, satisfaction, education, assistive devices and technology, and role of the care coordinator. A detailed coding analysis was then begun to organize the interviews into sections or coding units, label these units with a term, and provide narr ative passages to convey the findings. Reliability and Validity Weber (1990) notes: "To make valid inferen ces from the text, it is important that the classification procedure be reliable in th e sense of being consistent: different people should code the same text in the same way" [p. 12]. To validate the coding scheme and accuracy of the findings, two sub-investigator s for this project coded a random sampling of 10 interviews each. The appropriate size of the sample used for validation reportedly depends on may factors but should not be less than 10 percent of the full sample, and will rarely need to be greater th an 50 percent (Neuendorf, 2002). Therefore, validation coding for this study consisted of approximate ly 10 (62 percent) of the interviews. Based on the information available, there is no consensus as to the best index of intercoder reliability. Several recommendati ons for Cohen's kappa argued that kappa should be "the measure of choice" and this index appears to be commonly used in research (Weber, 1990). Cohen’s kappa of .80 or greater was consider ed to be acceptable for this exploratory study. Subcoder training was performed with one personal interview, which was then excluded from the sampling of interviews pr ovided to the subcoders. Training required approximately 2 hours with each subcoder. Subcoder training included discussing the main objectives of the project reading through the interview together, discussing initial

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116 thoughts on themes or categories within the interview, and indivi dually re-reading and finalizing the coding of the interview. Subc oders were provided with the initial themes and were asked to determine whether the theme was present in the interview. A dichotomous rating scale of 1=present and 2=not present was used for coding each interview. Because of its simplicity and wide spread use, percent agreement was used for initial intercoder reliability during this training process. Following subcoder training, percent agreement reached 95 percent be tween subcoder #1 and myself, 92 percent between subcoder #1 and subcoder #2, and 94 percent agreement between subcoder #2 and myself. Results Description of Sample The mean age of our total interview samp le (n=16) is 71.7 years old. Ninety-four percent of our sample are male and 75 percen t are married. The mean number of chronic diseases is 1.88, with hypertension as the most prevalent chronic c ondition. Nine of our participants use the Health Buddy (HB) (56.25 percent), 4 individuals use a computer (25 percent), 2 use videophones (12.5 percent), and 1 uses the smartphone (6.25 percent) to remotely connect to their respec tive telehealth programs. Descriptions and Themes Characterizations and quotes from intervie wees were used to identify eight main recurrent themes that were refined during th e analysis. The initial coded themes were care coordination, connection, communication, e ducation, security, technology, adaptive equipment, and satisfaction with telehealth se rvices. When interviews were re-read, it was observed that many of these themes were closely connected with each other and should not be placed into separate coding categor ies. Therefore, four primary themes or

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117 codes were established, each with secondary th emes or subcodes. The coding structure is presented in Table 5-2. A secondary them e may be included under more than one primary theme and a phrase or sentence coul d be coded under more than one primary or secondary theme. Conceptual analysis was completed to id entify phrases and sentences under this coding structure. Table 5-2. Coding structure fo r qualitative interviews. Primary Theme/Code Secondary Theme/Subcode Care Coordination Connectedness Education Personal Security Technology Connection Convenience Daily Routine Education Frustration Personal Security Adaptive Equipment Security Satisfaction Overall (Telehealth Program) Interpretation / meaning of the data Care coordination Care coordination (CC), which includes care coordinators who manage patients through remote technology, emerged as a prin cipal theme in the interviews. CC was broken down into 3 subcodes to increase accu racy and detail of the analysis. The 3 subcodes include connectedness with the VA, education provided by the CC, and personal security through having access to a CC. Connectedness. Connectedness with the VA was an important theme that fell under the scope of care coordination. C onnectedness is defined as the relationship or

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118 attachment veterans feel they have with the VA through their care coordination office. Eighty-one percent (13) of our interviewees felt that bei ng enrolled in a telehealth program increased their connectedness with the VA. For our veteran interview ees, CC meant many different things. Most of our veterans felt that CC was supportive and able to fill in for direct contact with their VA primary care provider (PCP). Joe reports that his CC has, “been very helpful and sometimes more so than my primary care perso n.” CC allowed our participants to stay in close contact with the VA and was viewed as thei r direct access to care In fact, a few of our veterans felt that their CC was more pe rsonable than their PCP and felt secure because “they’re there and will take care of things.” Our veteran interviewees report that conn ection and staying in touch with their doctor were important aspects of the CC relationship. This requires that care coordinators follow up on phone calls, or com puter or Health Buddy flags. Seventy percent of the veterans interv iewed remarked that they re ceive immediate response from their CC. Martin is homebound and requires as sistance to get outside of his home. He discussed the fact that his CC telephones hi m because “she wants to know if I need anything or if things are all ri ght.” Martin reported that participating in a telehealth program also connected him with other serv ices, such as meals-on-wheels, which has been essential as he has difficulty cooki ng for himself secondary to post-stroke hemiplegia. Jim remarks, “the best thing is you stay in touch with your doctor, your healthcare provider; that’s the best part.” Jack also feels that’s important and states, “she’s [CC] the only way I can get to my doctor.” Mark stated that the computer format allows him to ask his PCP or his CC questions and that the response is always quick and

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119 helpful. For Mark, if there is anything th at he needs from the VA, “I’ll go through my CC and ask them if they could help, and it ’s taken care of.” Merle discussed the connection he has with the VA through his te lehealth program and stated, “to have somebody at the VA so to speak going to bat for you and doing things for you, it sure has taken a load off me.” Merle fe els “it’s been one of the best things for me because it gave me a connection with the VA that I wouldn’t have otherwise had.” For Merle, having this connection to the VA was “part of the h ealing process.” Milton uses his telehealth CC for any question or concern he has about his VA healthcare Milton stated that, “I don’t try to get a hold of anybody now, I just ca ll LAMP and I’m sure they recognize my voice by now, you know, if I need a change of anything, they take care of it for me.” James speaks with his CC weekly through a vide ophone, but also reported that he is able to call her anytime and she’s ava ilable for him, “I can call he r if I really need something and she’s my direct connection to my physicia n.” James appreciates that his CC is in also in direct contact with his PCP; he refe rs to this as, “a loop between the piece of equipment, my CC, my physician and then back to me.” Although quick response and follow-up regard ing healthcare needs is an important aspect to care coordination, it appears to be inconsistent. In c ontrast, a few of our veterans felt frustrated because of the lack of attention from their CC or the sense that the CC was not backing up their HB responses. Nine teen percent (3) of our HB users stated that they’ve never received any calls from their CC regarding responses they’ve input into the HB. Jessie was unaware of who his CC was and remarked that their office never calls him; a screen on the HB advises Jessie to call his CC or the VA hospital if he needs assistance. Jeff reported that no matter how high he reports his blood pressure or blood

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120 glucose levels are, he does not receive a follow-up telephone call from his CC. His analogy was, “it’s kinda like doing a repor t card everyday but there is really nobody backing it, you know.” Jeff stated that the HB tells him to call his CC and provides the phone number, but “there isn’t much communication I don’t think, personal communication.” John was initially a HB user and reported that they “never did, never did” call him in response to his HB answer s. John now uses a videophone, which has increased communication and his i ssues are resolved quickly. Delays in responding to telephone calls or not enough personal communication appeared to be a concern. Although Joe was very enthusiastic about the program and his CC, he also reported that, “they don’t seem to have the time to do the effort that they used to put into me when I called.” In fact, a few of our vetera ns were concerned that their telehealth office was too busy and didn’t have the personnel necessary to attend to everyone’s needs. The need for more reso urces was one of the weaknesses that was expressed during the interviews. Education. Telehealth CCs were noted to be important educational resources for many of our veteran interviewees. Seventy-five percent (12) of our interviewees stated they use their CC for information about the VA and as a health education resource. Mary reported that when she has a clinic visit, her PCP is very impersonal, and she doesn’t always understand what he tells her. But, when she talks with her CC, “it’s very personable, you know, they can sit and talk wi th me on the phone as a human being and still look up all my vitals on the screen and explain it to me in layman’s terms.” James reports that his CC is “a very good instructor, she’s the education, she is very good in

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121 listening to me, getting a good understanding of where I’m coming from and finding the right place to go or the right informa tion and letting me know that.” Security. Eighty-eight percent of our telehea lth interviewees found security and trust in their CC. Jim remarked, “it makes me feel good because if something is wrong, I can get a hold of somebody. In fact, I can call like this morning, a nd this afternoon I’ll see the doctor no doubt. It’s that fast.” Having a “shortcut” to the doctor was important and provided security for Jim. “If you ca ll them you’re gonna hear from somebody. That makes me feel good and secure.” Merle was a supporter of telehealth because of the security he felt having someone available to him when he telephoned. It appeared to be important to him that “they are there for you and answer the phone just because you made a call.” John stated he uses his CC, “when I get in trouble, if I need something, she can get connected with my doctor and I can’t, so I go through her.” Mark’s story was special, and may demonstrate the impact telehealth has had on his home situation. Mark felt that his CC and the telehealth program “was a godsen d” as his wife was planning to retire in order to spend more time at home caring for Mar k. Because he enrolled in telehealth and received the adaptive equipment he needed to increase his safety at home, his wife no longer feels retirement is n ecessary at this time. Technology Connection. All of our veteran interviewees viewed their home-based technology as a connection to the VA and their CC office. Our only female veteran interviewee, Mary, uses the smartphone for remote monito ring to LAMP. Mary reports that anytime “I punch in do we need to contact you, LAMP calls, if not two hours later, then the next day.” In fact, she reports that even when she doesn’t specifically state that she needs someone to contact her or contact her docto r, they do. Mary uses LAMP and the CC

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122 services for all her VA needs, “I don’t call anybody but LAMP .” Merle felt that having the technology in his home, which connected him to the VA and his CC, “has been a great help for me because I wouldn’t bother to call if I didn’t have it. I wouldn’t bother somebody and call them on the phone and say he y I fell today, what do you think I ought to do? But with the HB it asks you, and you’re not going to lie about it, if you fell, you fell.” James calls his connection with th e VA through technology “a lifeline.” For James, the security he has being connected di rectly to his PCP and the VA hospital is one of the most important aspects of telehealth. “That piece of equipment is a big piece of security, I have a lot less st ress and a lot less worry.” Convenience. Eighty-one percent of our technol ogy users felt the equipment was easy to use and was not viewed as intrusive. Mitchell st ates, “It’s a pleasure doing it because I have a lot of fun on the computer.” Mitchell f ound that he could use the computer to type in a request or a question and he receives a quick response so he does not need to wait or refer to a nother office to find the answer. One important component of telehealth is increased access to services. For our veteran interviewees, many of whom live in rural areas, limiting the drive to the hospital appeared to be an important aspect of the te chnology. It “helps more than driving all the way to Gainesville and have them say, we ll you look okay.” Jack reports that it’s decreased his travel time to Gainesville because of the quick follow-up for an intervention, “That way I don’t have to ma ke three trips to the VA you know; I have somebody to communicate with without getting dressed and going to th e doctors again.” Mick agrees and views his technology conn ection with the tele health program as replacing a visit to the VA. Mick is hom ebound and states, “instead of calling and

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123 making an appointment and going in and seeing somebody and having them go through a lot of paperwork and so on, this does it.” John also reports that his technology has “saved me trips to Lake City.” Jack feels the technology is beneficial because all he needs to do is input his information everyday and if his CC deems that something needs corrected, “they can do it and call me and tell me what to do; so it’s convenient for me.” Milton was initially given a computer for remote monitoring, but was having difficulty learning to use the computer and, therefore, was not submitti ng his personalized dialogue form daily. Milton transitioned to a HB and states, “I think it’s great because I don’t like the computer, and this thing here I can sit and pump that while I’m eating. It’s easier and doesn’t take as much time.” Mick likes using the computer for his personalized daily dialogue with his CC because of the quick re sponse, “I just leave a little note and someone sees it right there and gets back to me; they’re usually very quick at responding.” Daily routine. Interestingly, 8 of our veterans (50 percent) described their part in providing answers to the HB or the computer as their personal role. Reportedly they felt guilty if it was not completed; it appeared to be a part of their daily routine. Although Jeff reported that he “hates doi ng it every morning.” He also stated “it keeps me I guess more alert on what I’m doing.” Requiring da ily completion of his HB dialogue increased his self-awareness; he reporte d that it “helps you watch your sugar better knowing you gotta do it; it keeps you doing that.” Jessie reports that, “I do it every morning, that’s the first thing.” Mark has integr ated his computerized responses into his morning routine. Mark reports, “

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124 I think it’s great. I get up in the mornings and do my breakfast, and of course it depends on if it’s a day to do my blood sugar, then I do that, then I have breakfast, then after I have my breakfast and se ttle down a little bit then I do my blood pressure and then I go in and run it off. Usually I try to get it to them by 11:00. Martin stated that there’s no pattern to when he answers hi s HB, “I might answer it at one, two o’clock in the afternoon, some nights I lay in bed and reach over and get it and do it then”. But Martin also stated that the daily reminders to take his medication, blood pressure and weight were im portant. Milton has memory di fficulties, and views his HB as a difference in life/death. He reports, “I f it was up to me, hell, I’d forget to call you. I would, I would just ignore it and probably die from it.” Education. The HB provides education to the ve teran through a br anching system that responds to their input. Should a vetera n report that his blood pressure is high, the HB responds, tells the veteran he is outside of his parameters, explains the physiologic process of blood pressure, and provides reas ons why his blood pressure may be high. Eighty-eight percent of our HB users found the education provided by the HB to be beneficial. Joe states that he ’s, “written things down what they look for to be a good read out on my vitals” and reports that this is important for him; he clearly follows these parameters. Jim states that he’s, “learned you know about the walls of the blood vessels whatever. It explains a lot of stuff that the doctor don’t explain to you.” Jesse reports that the education he receives fr om the HB is “a big help to me.” In fact, Jesse states that he now knows more about his he alth and knows “what to expect that’s coming up” if his blood pressure is high. Merle is “fascinated” by the inform ation he receives from the HB; “this is information I wouldn’t have and it fascinates me that when I put something in like, like you said my blood pressure for in stance, I mean it automatically comes back and tells me about my blood pressure, telli ng me what it amounts to and it gives me

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125 information that I wouldn’t otherwise have. I’ m fascinated that that little box can send so much information back to you and its all on the basis of what you put in there, the answers that you give it.” Mark states that he learns something everyday from the HB, especially regarding his diet, and “even the flu”. Some of the veterans were indifferent regarding the educati on provided through the HB. Mike stated, “it might contribute some but if there’s a majo r concern I don’t think so.” Martin stated that th e HB didn’t give him any inform ation that he didn’t already know, but stated, “I just don’t follow it.” Veterans who are using a computer, sm artphone or videophone are not directly provided with this type of information, yet thes e veterans felt it wasn ’t a concern as they usually received the information they needed directly from their CC or their PCP. Mitchell initially used a smartphone and was re cently transitioned to the computer. He felt that he had learned some things from hi s CC that he wasn’t aware of prior to being enrolled in the program, “there ’s just no way about it, it has helped me tremendously.” Frustration. Half of our veteran interviewees voi ced some type of frustration with the technology. The HB requires that you answ er questions daily, but provides only oneway communication. Joe states that, “I get a little frustrat ed with the Health Buddy and, let’s see how I can say this, I feel frustra ted because I can’t talk to anybody.” Jeff was disturbed because he felt like he had taken the time each morning to input information into the HB, yet if he calls hi s CC office, they ask him the same questions, “they’ll ask me what was my sugar yesterday, we ll I just gave it to them, they got it on the computer, why do they gotta ask me, you know?” Jack reports that due to the repetition of the questions, and some of the responses given by the HB “they must think

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126 we don’t have any smarts at all.” Jack reports that when he doesn’t fe el very well, “it just irritates me sometimes.” Additionally, it wa s noted that HB questions do not always apply to individuals and that having more individualized or specific questions would be preferable. One of our veterans reported he was having difficulty with the HB because of his visual impairment, “sometimes I have di fficulty seeing and I can’t read it.” Martin was frustrated by the fact that he may put the wrong information into the HB, and has no way to back-up and correct it. He felt bad that his CC “calls me even if I make a mistake. I just can’t go back so I can’t fix it.” J ohn had recently transitioned from the HB to the videophone, which he finds more personable and prefers over the HB dialogue. He remarked that the HB asked him too many questions that were difficult for him to answer and had nothing to do with his condition. John remarked, “I don’t like to answer stuff that I don’t know nothing about. She asks about me, that’s all she asks me about. I think it’s wonderful cause I can talk direct to her instead of that buddy boy.” Personal security. Eighty-one percent (13) of the interviewees found security in the use of technology to connect them with their healthcare provi der. Joe felt that the HB provided him a sense of well-being. When as ked about the technology, Joe responded, “I do feel a certain amount of help with that thing just sitting there. I think it’s wonderful. It gives me a sense of security.” Although Je ff reports that he doesn’t know how well the HB is working “on their end”, he states that he “likes it” and “I feel more secure having it.” Mary feels secure because she knows, “if we call, they will take care of it.” Mitchell uses the smartphone and finds that it provides him security when he’s away from home, “I’m not afraid to go out by myself anymore. If an emergency comes up, I can call even after hours.” For Mitchell, th e connection with the VA “makes me feel more secure”,

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127 because he has a place to go and get the information or assistance he needs. Mack’s wife works and he’s home alone all day; he feels the HB is “there to keep tabs on me.” Mack knows that if he answers the HB in a way wh ich creates a flag that his CC will “call me up right away, she calls me and asks what’s going on.” Mack also knows there is always a voice on the other end of the phone if he need s to call. Mack stated he called his CC one day because, “I just felt I needed to ta lk to somebody, not that I needed anything, just to call her up. But she didn’t say I can’t ta lk to you, I’m busy, she wouldn’t do that.” Adaptive equipment Personal security. By nature of the enrollment cr iteria for the telerehabilitation program, LAMP enrollees are more functionall y impaired. LAMP’s goals are to provide the adaptive equipment and monitoring to in crease independence and safety within the home environment. Therefore, discussions about adaptive equipment were more relevant to LAMP interviewees. All of the LAMP interviewees (9) voiced the need and importance of having a rehabilitation compone nt to their telehealth program. Mark received equipment specific to mobility, bathing and toileting and reports that, “the fact that LAMP is doing what it’s doing is keepi ng a lot of us out of nursing homes.” He reports that he’s able to go places he never co uld go before with his scooter. This is also true for Martin, who suffered a stroke a nd has basically been homebound until enrolling in his telehealth program. Martin reports that he’s able to get outside “everyday in the morning or afternoon” because he received a ramp from the VA, which was initiated through LAMP. For Mitchell, adaptive equipm ent is a major element of telehealth. Mitchell received equipment for mobility a nd transferring (rollator, grab bars), for dressing, and a reacher to help him “pick thi ngs up off the floor.” He reports that the

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128 adaptive equipment he received “amazes me” and “has helped me so much.” Merle is enrolled in the telerehabilitation prog ram, and spoke about the home evaluation. They [LAMP] checked everything out as far as my mobility to get in and out of the house and everything. They went to the bathroom to check and see what the situation was there as far as any assistance th at I would need and in turn they pretty well covered from one end of the house to the other plus the out side even, and in turn they provided me with everything th at I needed to make everything more accessible for me. I mean this, at the time I weighed over 300 pounds and there’s no way that my wife could do much to help me, so like I said if it wasn’t for that I couldn’t have gotten around. Falls are a major concern and one of the focuses of LAMP. When discussing the adaptive equipment that Martin received, he stat ed that he uses it every day, and that “I haven’t falled in a long time, I can just re ach up and grab those bars and they stop me from falling.” Mike reports that his equipm ent has “been very helpful”, and that he’s only fallen once in many months which was due to exhaustion from a recent trip to South Florida. Mack has muscular dystrophy and ma ny of his difficulties focus on mobility and access within and outside of his home environment. CCs for Mack assisted in his obtaining a ramp, which Mack feels is the “most important” piece of equipment he received. Mack was evaluated for and received other mobility equipment such as a hoyer lift, walker, and chair lift. Milton has a histor y of falls, but after the provision of adaptive equipment, he states he “feels safer in the ba throom.” Mary received numerous pieces of adaptive equipment focusing on mobility and transferring (rollator, quad cane), bed mobility (bed cane), and safety in the bathroom (grab bars, toilet riser and safety frame, bathtub assist, shower chair, hand-held show er), and reports that the adaptive equipment provided to her has been, “a life save r, an absolute life saver.” No TCCP interviewees received any adap tive equipment as a result of being enrolled in their telehealth program, but ha d received some equipment from the VA.

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129 Equipment most often provided was rollators and scooters When asked about additional needs, five of the seven TCCP interview ees responded that they did not need any additional equipment. During my conversation with Jessie, he reported that he, “couldn’t get up and down in the bathroom. I don’t have a shower chair, but I need one. I’m afraid I’ll fall trying to bathe my legs to reach my feet.” Both Jessie and Jack appeared unaware that their CC could assist them with the provision of adaptive equipment to increase safety. Satisfaction with telehealth During the interview process, satisfaction wa s not directly defined for interviewees. In general, they were asked whether they were satisfied with the services they’ve received from the VA and their respective telehealth program. Beyond any frustration with the technology, or lack of follow-up from the care coordination office, when questioned whether they were satisfied with the services they have received as a part of their telehealth program, all of our veteran interviewees stat ed they were satisfied. Joe says, “thank God I have them, and they have helped me so many times, so I’m 100 percent satisfied, anything I can do to keep them operating I certainly would like to do.” Jim remarked that the telehealth program was “fantastic as far as I’m concerned; I think everybody should have one, period. ” Mitchell replied, “I ne ver knew what it was but once I’ve got it I can’t do without it.” Martin stated th at it’s, “been beneficial, I can’t think of a thing that would be detrimental. I got a wheelchair in there, I got all the things here, that table I use al l day long, and this hemi walker is ideal, and the reacher. I’m very well satisfied with everything they’ve done.” Telehealth is viewed as a system of care; our veterans view it as providing them better care. Mark stated, “I th ink that care coordinators and nurse practitioners are one of

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130 the greatest things that happened to the VA.” Martin stated that, “I’m very well satisfied with it and I hope they work forever.” Me rle responded, “the VA is a huge operation and it would take me a half a dozen phone calls to try to find something and sometimes I still wouldn’t find it. I don’t have to do that, I call my CC and my CC handles it for me. It sure does make it nice.” Mark was “over-satis fied” with being a pa rt of a telehealth program and felt, “it’s about the best thing the VA has ever done.” Milton reported that, “I wouldn’t get any respect if it wasn’t fo r LAMP.” John completed his interview by stating that, “The main thing is that I’m satisfi ed with that and with the service I get at the VA, the whole works.” Mary summed up telehea lth for most of our veteran interviewees, “It works, it works.” Table 5-3 presents the coding structure and the final coding results based on personal in-home interviews with 16 telehealth participants. Table 5-3. Coding results from qualitative interviewees Primary Theme/Code Secondary Theme/Subcode Percent Care Coordination Connectedness Lack of Connectedness Education Personal Security 81% 19% 75% 88% Technology Connection Convenience Daily Routine Education Frustration Security 100% 81% 50% 88% 50% 81% Adaptive Equipment Personal Security 100% Satisfaction 100%

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131 Reliability and validity One method to measure reliability is to measure the percent of agreement between raters. This involves simply adding up the number of cases that were coded the same way by the two raters and dividing by the to tal number of cases. This was performed during the initial training process with the subcoders, whereby agreement reached between all coders was above 92 percent. The problem with a percent agreement approach, however, is that it does not account fo r the fact that raters are expected to agree with each other a certain percentage of the time simply based on chance (Cohen, 1988). In our study, it may also be based on the fact th at all coders were invo lved with telehealth at the VHA in one way or another. In order to circumvent this shor tfall, reliability was calculated by using Cohen's Kappa, which appr oaches 1 as coding is perfectly reliable and 0 when there is no agreement other than what would be expected by chance. All interviews were coded by the pr incipal investigator (PI); a percentage of the full sample (62 percent) was coded by each of the subcoders. The sample used for subcoder training was not included in the final sample. Follo wing subcoder training, coding was performed independently and without guidance by the princi pal investigator. A ll reliability coders evaluated the same set of units. Kappa wa s determined by be .90 between subcoder 1 and the PI, .88 between subcoder 2 and the PI, a nd .84 between subcode r 1 and subcoder 2. Values of kappa above 80 percent re present excellent ag reement (Cohen, 1988). Member checking Member checking for qualitative studies requ ires that the analysis be presented to the research subjects for feedback on validity of conclusions. Within 2 months following all in-home interviews, participants received a telephone call from the PI to discuss the interview and ensure that the information provided was valid and acceptable. One

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132 hundred percent of our interviewees (n=16) accepted the final re sults presented by the interviewer/PI. No interviewees felt the need to retract any statemen ts or add additional information to their interviews. Comparison with Quantitative Analysis Qualitative and quantitative methods used t ogether can be seen as complementary and mutually reinforcing (Creswell, 2003). Sh apiro & Markoff (1997) assert that content analysis itself is only valid and meaningful to the extent that the results are related to other measures. The interviewees were comp ared with their general health information obtained through costs analysis and evaluati on of SF-12V pre-post surveys. For this group alone (n=16), hospital bed days of care were reduced by 96 days at a total cost reduction of $89,936. The SF-12V scores for this group demonstrated no significant change within their one-year enrollment peri od. When LAMP interviewees are separated from the TCCP interviewees, the LAMP interviewees’ SF-12V physical component scores (PCS) increased significantly from baseline to post-one year enrollment ( t(8)=-2.62, p <.05). Discussion Despite an increased interest in the use of technology for healthcare delivery, it is surprising that little empirica l research has been conducted on the topic. Even more surprising is the limited amount of literature that describes the patient’s perspective on the use of technology for delivery of healthcar e. The primary goal of this portion of our study was to answer the research question, “H ow do veterans enrolled in a telehealth program describe their experi ences with the VA healthcare system”. The specific aim was to evaluate the effect of a telerehabil itation and a telehomecare intervention on health and satisfaction with VA services. Semi-struc tured interviews were used to obtain data

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133 on the thoughts and experiences of 7 veterans enrolled in TCCP and 9 veterans enrolled in LAMP. These interviews included discus sions on how using technology to connect to their healthcare provider affect ed their health in general. Prospective data was obtained through one-time only, face-to-face interviews within the participant’s home. An interview guide consisting of open-ended que stions was used for all interviews. Care coordinators emerged as a major theme in our interviews, most notably the connectedness with the VA that resulted from having a care coordinator available. Magnusson and Hanson (2003) also reported th at a reciprocal re lationship developed between telehealth patients and healthcare pr oviders, which was viewed as essential to the project. The provision of a care coordinator, located in a VA central office available via a telephone call or an office visit, app eared to increase the sense of support and personal security patient’s the interviewees experienced through telehealth. Care coordinator’s provide the patient with a prim ary VA contact, are aware of their patient’s needs, and are able to assist with their h ealthcare requests. Additionally, a number of articles regarding the use of te lehealth technology have raised the concern about fear that technology would be considered impersonal and would replace face to face meetings with healthcare staff (Demiris et al., 2004; Hebert and Korabek, 2004; Frantz, 2003; Magnusson & Hanson, 2003). This was not a con cern that arose in any of the personal interviews. Success appears to require the availabili ty of a healthcare provider to respond to the data generated by the technology; this is an inte gral component of LAMP and TCCP. Hebert and Korabek (2004) focused on th e importance of “fit between type of service needed and the technol ogy, rather than diagnosis or service type” [page 86].

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134 Jennett and colleagues (2005) also found that technology should be efficient and appropriate to the patient’s n eeds. In our study we found a number of veterans who were frustrated with the technology. One veteran had recently switched from a computer to the HB, and another from the HB to the vi deophone. Although care coordinators assess a patients needs prior to implementation, it may be important to spend more time targeting the technology to each patient rather th an providing a general approach to implementation. Although there were frustrations noted in using the technology, a majority of the interviewees viewed the technol ogy, such as the HB or the co mputer, as a tool to help access information and support their everyday he althcare needs. Most veterans who participated in the telehealth programs e xplained that they were happy to use the technology because they saw the direct benef it to themselves. Additionally, because the technology was easy and quick to use, it was not considered a burden or an intrusion into their daily lives. Education was a major theme that evolved from these personal interviews. The VA views telehealth as integral to the delivery of health education, as well as healthcare services. The HB provides daily reminders and education, which may increase patient compliance, as patients are more aware of their vital parameters (blood pressure, blood glucose levels, body weight and temperature) and able to be come actively involved in the process of managing their care and treatment interventions. Each of the veterans interviewed who used the HB for remote monitoring reported that the information provided was important to them, although a few st ated that they didn’ t always adhere to the information. Participants who were using the computer, videophone or smartphone

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135 did not receive education directly from th e technology, but found the education provided by their CC to be more individualized, and met their educational needs. Another main objective of tele health is to remove the ba rriers of distance, time and travel from healthcare. Dhurjaty (2004) repor ted that patients view telehealth positively, especially because of the reduction in travel time and associated costs. Eighty-one percent of our veteran interv iewees remarked how convenien t and beneficial it was to have a CC and the technology in their homes, as it often saved them from driving to the VA hospital for their healthcare needs. Mo reover, responses are typically quick and address health concerns in a timely and efficient manner. Limitations. There are typically inherent biases in qualitative research, particularly in interviewing (Murphy et al 1998). We have dealt with a number of threats to validity of interviews through member checking and the use of additional subcoders. As well, the interview process may be critic ized based on the fact that an individual’s answer to a question is highly dependent on the context in wh ich they are presented. In this study, all personal interviews were held in the interviewee’s home, which provided a more comfortable and natural context. The home wa s felt to be an appropriate environment for answering questions regarding telehealth sin ce the interviewees ar e using the technology and involved with telehealth appl ications within their homes. Moreover, possible flaws may be present in the process of cont ent analysis that could diminish its value. Such flaws include faulty definitions of categories or categories that are too restrictive or t oo far-reaching. Yet, when used properly content analysis is a powerful data reduction technique. Its major be nefit is the fact that it is a systematic, replicable technique for compressing text in to content categories based on rules of

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136 coding. To enhance the efficacy and reliabi lity of the analysis, member checking was carried out with each of the interviewees, and subcoders provided validity to the findings. Care coordinators from LAMP and TCCP provided a list of possible interviewees for the researcher to contact. As our samp le was not randomly selected, selection bias may be present. Although we felt it was impor tant to take cases from where we could learn the most, it may be that the list of interviewees provided would be more apt to report positive comments. Of the 11 potenti al TCCP participants, 3 never returned telephone calls and 1 was hospita lized in ICU when initially contacted. The researcher was told to contact him again in a few m onths, but this did not occur because of numerous issues within the VA that began to limit direct contact with veterans and personal interviews. The LAMP care c oordinator provided a list of 12 possible participants. Upon telephone contact, 1 poten tial interviewee wit hdrew his consent and declined to be interviewed because of health issues, and the remaining 2 LAMP enrollees asked to be contacted at a much la ter time for a possible interview. These qualitative interviews have provided essential information and are deemed an important component to the ev aluation of LAMP a nd TCCP. The generalizability of our research findings to a larger population is not the intention of our study. Rather, the aim of this study is to provide additional information on teleh ealth as a healthcare delivery model using a sample from two teleh ealth programs and qualitative methods.

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137 CHAPTER 6 DISCUSSION Telehealth offers opportunities and cha llenges to the traditional practice of medicine and to the organization of hea lthcare. Advances in technology and data transmission networks make the delivery of healthcare to the home feasible and accepted, but not without continued efforts and possi ble economic costs. Along with studies examining the efficacy of telehealth app lications, additional studies of the costeffectiveness and impact on the patient are needed. The clinical effectiveness and educationa l benefits of telehealth have been acknowledged in the literature (Bynum et al., 2003; Finkelste in et al., 2006; Grigsby & Sanders, 1998; P. A. Jennett et al., 2004; K obb et al., 2003; Noel et al., 2004; Taylor, 1998). Yet, controversy continues regardi ng measuring the costs of these efforts (Bashshur, 2001; Hakansson & Gavelin, 2000; Ohinmaa & Hailey, 2002; Whitten, Kingsley, & Grigsby, 2000). The main ch allenges to the econo mic evaluation of telehealth continue to in clude new and constantly ch anging technology, limited large scale randomized controlled trials and the ability to accurate ly evaluate health and nonhealth outcomes. Further complications arise when healthcare costs and the benefit to the patient or the healthcare provi der are considered together (Agha, Schapira, & Maker, 2002; Bashshur, 2001; Hakansson & Gavelin, 2000; Ohinmaa & Hailey, 2002). Despite these challenges, studies measuring the cost -effectiveness and patient perspectives of telehealth applications compared with standard medical practices are needed.

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138 The purpose of this mixed methods study wa s to obtain quantitative results from a sample of veterans enrolled in a telereha bilitation program and a telehomecare program, and follow-up with personal interv iews to explore the patient’s perspective on telehealth. The Low Activities of Daily Living Monitori ng Program (LAMP) is a telerehabilitation program that included veterans with functiona l deficits and chronic illnesses who were at risk for multiple VA hospital and nursing home bed days of care (BDOC). Veterans were eligible for enrollment in LAMP if they pr esented with impairments in at least two activities of daily living (ADL s), including mobility and transferring. Veterans enrolled had to live at home, have electricity a nd phone service, and accept remote monitoring technology into their homes. The LAMP mode l draws on the experience of occupational therapists to coordinate care through remote monitoring in conjunction with environmental modifications and assistive de vices to improve function and decrease the impact of chronic illnesses. The Technol ogy Care Coordination Program (TCCP) is a telehomecare program that included veterans with chronic illnesses, who were at risk for multiple VA inpatient and outpatient visits. Veterans were eligible for enrollment in TCCP if they were non-institutionalized, had a history of high healthcare costs and utilization, had electricity and phone servic e, and accepted remote monitoring technology into their homes. The TCCP model uses tele health technology in conjunction with nurse practitioners to coordinate medical care for chronically ill veterans. Cost Analysis Using retrospective data from veterans enro lled for at least one year in TCCP or LAMP, this longitudinal study examined heal thcare costs 12-months pre and 12-months post-intervention. Healthcare co sts included expenditures for hospital, clinic, emergency room, and nursing home BDOC, and were summed for these analyses. Matched

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139 comparison groups were obtained from a databa se of veterans who participated in the Veterans Administration’s 1999 Large Health Study (LHS), which surveyed over one million veterans on their health and well-be ing. Using the LHS cohort allowed us to compare the telehealth participants with vete rans who were instrumental in establishing baseline health status data for all ve terans. Comparison groups were matched on geographic location, age, marita l status, chronic illnesses, and number of hospital BDOC 12-months pre-study period. Matching wa s accomplished by creating a dummy string variable for every participant, whereby the el ements of the character string represented the matching variables. Using the dummy string variable, 76 per cent of LAMP and 68 percent of TCCP had direct matches with a patient from the comparison pool; the remaining were matched manually on age and pre-BDOC and as many of the diagnostic variables as possible. Following matc hing, analyses determined no significant differences between LAMP and their matc hed comparison group or TCCP and their matched comparison group. Both treatment and comparison groups rece ived their healthcare from the North Florida/South Georgia VA Healthcare System All groups were enrolled and using services in the VA for the entire 24-month observation. Actual telehealth enrollment dates were used for our treatment groups to determine pre-post costs. An arbitrary enrollment date of October 1, 2003 was used for the comparison groups to determine prepost healthcare expenditures. Although selection criteria were stringen t for matching of the comparison groups, a difference-in-differences (DiD) approach was us ed in the cost analysis to allow for the control of any remaining differences, which ma y result in selection bias and influence the

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140 treatment effect. The DiD method has been used in health services research (Tai-Seale et al., 2001; T. H. Wagner et al., 2001 ), as well as in telehealth studies (T. E. Barnett et al., 2006; Chumbler et al., 2005). Using the DiD a pproach and actual costs summed for these analyses, we were unable to detect sign ificance between LAMP and their matched comparison group, TCCP and their matche d comparison group, or between the two treatment groups, LAMP and TCCP. The point estimate of the DiD treatment effect was extremely large relative to the mean costs, therefore the inability to detect significance was a result of the high variability of the estimate. Itemized costs revealed that LAMP pa rticipants experienced a considerable increase in clinic visits post-intervention. Although i npatient costs were reduced, including both inpatient BD OC and nursing home BDOC, the increase in clinic costs increased LAMP’s overall post-enrollment cost s. For LAMP participants, the initial enrollment evaluation and home assessment, adaptive equipment provided for self-care and safety, and remote monitoring interven tions were considered clinic visits. Approximately 3,300 clinic visits were the direct product of enrollment in LAMP. A primary goal of this telerehabilitation program was to keep veterans out of the hospital and nursing home and at home safe. For th e LAMP cohort, this goal was met. An outcome of meeting this goal was increased outpatient visits which benefited many more veterans. During the 12-months following enrollment for TCCP participants, the number of hospital BDOC decreased, but total inpatient costs (hospital and NHCU) increased slightly post-enrollment. As with LAMP, TCCP’s primary goal of remotely monitoring health symptoms and providing increased access to care resulted in a significant increase in clinic visits. The combined effect of higher costs per BDOC and additional clinic

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141 visits negatively effected TCCP’s post-enrollm ent costs. An important factor to consider is that telehealth’s primary focus is to increa se access to care. Consequently, much of the increase in clinic visits was a result of enroll ment in a telehealth program, as the increase in clinic visits included services provided by the intervention. Mo reover, increases in care coordinator-initiated clinic visits, su ch as primary and geriatric care, lab and diagnostic visits, and secondary clinic vi sits such as ophthalmology or audiology are evident, and have been observed in other VA home telehealth studies (Chumbler et al., 2005; Kobb et al., 2003). In comparison, clinic visits for both matc hed cohorts decreased in the post-study period, demonstrating that wh en treatment declines, costs decline. Longer observation times would allow us to weigh the impact of this decline in care against the impact of the increase in car e provided by the telehealth programs. A phenomenon that was observed in this study was the significant decrease in hospital costs for both of the comparison gr oups. This phenomenon may be the result of regression to the mean, which can occur with a nonrandomized sample and two measures that are weakly correlated (T. E. Barnett et al., 2006; Yudkin & Stratton, 1996), such as pre and post-healthcare costs. Our compar ison groups were closely matched with our treatment groups and demonstrat ed high levels of healthcare use at baseline. In regression to the mean, change is often nega tively correlated with higher values. This may be why we observe a significant decline in hospitalizations post-study period. While the regression effect complicated this study, we attempted to control for it statistically through the DiD design. Unfortunately, the us es of costs in the design, which were highly variable within and be tween our study populations, resu lted in a high error rate for our regression analyses. The observed decrease in inpatient costs ma y also be explained

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142 by a system-wide secular trend within VA hosp itals to decrease inpatient length of stay (BDOC) and transition to more ambulatory care (Payne et al 2005; Phibbs, Bhandari, Yu, & Barnett, 2003; Yu et al 2003a). The ability to observe patients over a longer period of time may provide more accurate effects of treatment vs. non-treatment. This segment of the dissertation a ttempted to quantify the effect of telerehabilitation and telehomecare in reducing healthcare costs. The analyses observed veterans enrolled in LAMP, veterans enro lled in TCCP, and corresponding matched comparison groups who had never received a te lehealth interventi on. The hypothesis for this study was that veterans enrolled in LAMP, veterans enrolled in TCCP, and their matched comparison group will differ in their VA healthcare costs. Based on results from the DiD analyses using summed healthcar e costs, we reject th e hypothesis that our four study arms will differ in VA healthcar e costs following one-year enrollment in a telehealth program. Although we were unable to detect significance, the high variability of the estimate reduced the ability to obser ve a treatment effect. The multivariate analysis determined a large variance of errors in each of the regression equations, therefore, numerous unknown or unidentif ied factors may account for the remaining variance in the models. Future studies shoul d consider using larger sample sizes or logged costs to reduce the variance in the models. Health-Related Quality of Life The second hypothesis in this study was th at veterans enrolled in LAMP would demonstrate less decline in physical functi oning over 12 months of intervention due to the framework of the telerehabilitation pr ogram. The Veteran’s version of the SF-12 health survey (SF-12V) was used to measur e self-reported physical outcomes. The SF12V is a subset of identical items from the Veteran’s version of the SF-36 (SF-36V), and

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143 is a patient-based health-related quality of life questionnai re (HRQoL) designed specifically for use among vete rans (Kazis et al., 1998; Re snick & Nahm, 2001; Riddle et al., 2001; Ware et al., 1996). The SF-12V pr ovides a physical component summary score (PCS-12V) and mental component summary score (MCS-12V), but physical outcomes were the primary focus of this study. The SF-12V PCS scores include questions concerning one’s ability to participate in dail y physical activities, such as climbing stairs, how energetic or vital one fees, as well how their physical abilities affect their social and work roles. Measurements were administered to LAMP and TCCP enrollees at baseline during the initial en rollment and at 12-months follow-up. The analysis focused on differences in health status between the two programs from base line to post 12-months enrollment based on results from the SF-12V. Forty-three percent of LAMP enrollees (n =50) and 63 percent of TCCP enrollees (n=84) completed the SF-12V health survey at baseline and 12-mont hs. Based on results from the LAMP and TCCP cohorts, the vete ran enrollees served by these telehealth programs report significantly wors e health in the physical doma in (PCS) than that of the general population, as well as the overall veteran enrollee po pulation (Kazis et al., 1999b; OQP, 2000). The SF-12V PCS scores for our vetera n telehealth particip ants fell at least 2 standard deviations below the mean of th e general population and 1 standard deviation below the mean of the veteran population. Dependent samples t -tests demonstrate significant increases reported in SF-12V PCS scores for the LAMP participants from baseline to 12-month follow-up, with no significant differences observed in the TCCP co hort. In an attempt to determine whether the increase in PCS-12 scores for LAMP were clinically relevant, th e standard error of

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144 measurement (SEM) (Wolinsky et al., 1998; Wy rwich et al., 1999b) as well as the effect size (Cohen, 1988) was estimated. To date, SE M-based criterion for clinically relevant HRQoL change, specifically the SF-36 or SF-12, has not been established (Samsa et al., 1999; Ware, Jr. & Gandek, 1998). Using the thres hold of 2 SEMs to demonstrate clinical significance (McHorney & Tarlov, 1995), the increase in PCS12V scores from baseline to 12-month on our LAMP population (+4.25) falls within of the 95% range (2 SEM = 4.38), and therefore does not represent a clinic ally significant change. If 1 SEM were used to determine clinically significant cha nge, as some researchers suggest (Wyrwich et al., 1999b; Wyrwich & Wolinsky, 2000), then the increase in LA MP SF-12V PCS may be considered clinically signi ficant. Some researchers repor t that the SEM alone does not indicate clinical significan ce and that any post-intervention score must fall within the normal range (Ferguson et al 2002). LAMP participants do no t meet this criterion as pre and post PCS scores fall at least 2 SD belo w the general norms and approximately 1 SD below the general veteran norms. When eval uating effect size in both LAMP and TCCP PCS-12V scores from baseline to one-year fo llow-up, the LAMP population had an effect size that was considered small to moderate by Cohen’s definition (.425). Unfortunately, we do not have power to justify this effect as our sample size was 50 subjects and 100 subjects would be required for an ef fect size of .425 with power at .80. Comparisons between LAMP and TCCP were computed through a one-way ANOVA. A significant difference was found be tween the two groups at baseline, with LAMP participants reporting lower physic al functioning at baseline than TCCP participants. As LAMP participants are, by nature of enrollment in the program, more functionally impaired, this is not a surpri sing finding. Yet, following the 12-month

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145 intervention, the sign ificant difference in SF-12V PCS scores between the two groups was no longer visible, as LAMP scores increas ed significantly and T CCP scores declined, although not significantly, during th e 12 months of treatment. A post-hoc analysis was performed in an attempt to increase the power of this study. Missing data for LAMP and TCCP were examined to determine if there were significant differences between their corre sponding samples at baseline or 12-month follow-up. In the LAMP cohort, 43 veterans completed the SF-12V at baseline only, and 11 completed the survey at 12-months only. In the TCCP cohort, 26 participants completed the SF-12V at baseline only. Thes e groups were matched with the parallel baseline and 12-month data. There were no significant differences noted in the SF-12V PCS baseline or 12-month scores, allowing us to conclude that the missing data would not have altered the final outcome. Health surveys, such as the SF-12V, can provide information on quality of care and clinical effectiveness. Results from the SF-12V demonstrate that this population is skewed toward illness. Moreove r, it is important to note th at because this is a frail, chronically ill population, decline would be expected especially over a 12 month time period. Yet based on the SF-12V PCS sc ores, physical functioning increased significantly for the telerehabilitation particip ants, demonstrating that the addition of a rehabilitation component, which focused on independence and safety within the home environment, is beneficial. The TCCP particip ants were able to at least maintain their scores over time. For this population, remaini ng stable is also important and may be seen as a positive outcome of the telehealth inte rvention. Although results from this study are

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146 noteworthy, we are limited without evidence from a matched comparison group or further long-term follow-up study. Personal Interviews In the third study aim, qualitative inte rviews were used to probe patient’s perspectives of telehealth and the use of technology for remote monitoring and healthcare delivery. The primary goal of this portion of the dissertation was to answer the research question, “How do veterans enrolled in a tele health program describe their experiences with the VA healthcare system.” The specifi c aim was to evaluate veteran’s personal feelings about being enrolled in LAMP or TC CP, and how telehealth impacts their health and satisfaction with VA services. Single, f ace-to-face, semi-structured interviews were used to obtain information from 9 veterans en rolled in LAMP and 7 veterans enrolled in TCCP. Based on these personal inte rviews, a majority of vete rans reported an increased sense of connectedness with the VA following en rollment in telehealth, and viewed their care coordinators as integral to the success of telehealth. In contrast, a few of the interviewees felt frustrated because of the lack of attention and limited follow-up they received from their CC. Interviewees c onsidered the technology, such as the Health Buddy or the computer, a tool to help access information and support their everyday healthcare needs. Most vetera ns who participated in the telehealth programs explained that the technology was beneficial and easy to use, although frustration with the HB was voiced by 50 percent of our interviewees. The HB only provides one-way communication, and questions are repetitive a nd not individualized to each person’s healthcare concerns. Other themes that aros e during the personal in terviews included the importance of health-related education. Inte rviewees reported that daily reminders and

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147 education received through th e technology or directly fr om the care coordinators improved their ability to self-manage their illnesses. Many of our veteran interviewees remarked how secure they felt having a car e coordinator and the technology in their homes. Telehealth was also viewed as in creasing access to timely and efficient care. Veterans reported they were more aware of their vital parameters (blood pressure, blood glucose levels, body weight and temperatur e) and able to more actively involve themselves in managing their care and treatmen t interventions. As one of the veterans explained, “it’s a loop between the piece of equipment, my CC, my physician and then back to me.” Specific to LAMP enrollees was the provision of adaptive equipment and environmental modifications for self-care a nd safety. All of th e LAMP interviewees voiced the need and importance of having a re habilitation component to their telehealth program and appreciated the ad aptive equipment provided. One hundred percent of our interviewees re ported that they liked being involved in telehealth and were satisf ied with the services they receive through the VA and telehealth. Summary The complex health problems of our veterans require complex medical and restorative regimes. The demands associated with the care of individuals with chronic illnesses and disabilities pose a considerable challenge. Chronically ill and aging veterans often require multiple hospital admissions, as well as numerous clinic and urgent care visits. Moreover, costly long-term care provided through nursing home and home healthcare is often necessary. It has been proposed that these costs would begin to decrease if interventions were focused on deliv ery of medical and reha bilitative care to

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148 help elderly live at home safely and independe ntly. Health promotion in older adults should include prevention of di sability, maintenance of capac ity in those with frailties and disabilities, and enhancement of quali ty of life (CDC, 2003b). In order to accomplish this, better methods to deliver care and monitor health outcomes related to older adults functioning and quali ty of life are essential. The VA has acknowledged through the wide us e of telehealth th at the development of programs that provide coor dination of complex care remo tely and extend healthcare services into the home to assist veterans in managing their chronic diseases is essential. Telehealth interventions thr ough the VA are not only designed to reduce costs, but to increase service connection and access to ca re for veterans and decrease reliance on hospital and nursing home care. In creased patient satisfaction w ith healthcare is also an important aspect of telehealt h. Creative models of care, su ch as telehealth, can support veterans in acquiring self-management skil ls and maximizing health potential and outcomes (Nodhturft et al 2000). This study targeted community-dwelling ve terans with chronic illnesses. Two models of care delivery were explored. TCCP is a telehomecare program, which employs a medical model of care. Care coor dinators for TCCP are nurses skilled in the management of chronic illnesses through di agnosis, medical intervention and patient education. Interventions provi ded through TCCP are typicall y disease-specific and focus on the monitoring of physiologic parameters. The medical model of care places emphasis on diagnosing and successfully treating a dis ease, with functioning and health viewed primarily as a consequence of a disease. In comparison, the telerehabilitation model (LAMP) views function as not only an outco me, but also an important component of

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149 assessment, intervention, and quality of care (Cieza & Stucki, 2005). Therefore, the severity of an illness can be reduced through the provision of environmental modifications and adaptive devices that rem ove the limitations that alter functioning. LAMP care coordinators are occupational therap ists that focus on the patient’s functional difficulties, which represent a major threat to the quality of life in older adults and, therefore, should be addressed concomitantly with disease treatment. LAMP uses the rehabilitation model to coordi nate care for chronically il l individuals through assessing personal and environmental factors in orde r to provide the appropriate technology for remote monitoring, as well as modifying the immediate home environment through the addition of adaptive equipment. From the LA MP perspective, provision of resources and remote monitoring of health, self-care and safety within the home environment assists patients to cope with the impact of their chronic illness. This study illustrates that te lehealth applications do not decrease overall healthcare costs, but may change the configuration of ca re. For our telehealth participants, costly hospitalizations declined, but clinic visits in creased significantly as patients became more aware and compliant with their healthcare treatm ent plans. Newly scheduled clinic visits enabled more veterans to be treated in an appropriate and timely manner. Clinic visits became an alternative level of care, and the home the alterna tive place of care. Although we hypothesized cost savings, due to the co mplex chronic illnesses of our veteran enrollees, frequent follow-up clin ic visits were scheduled to ensure there was no decline in condition or to check on progress of an intervention or treatment. Due to these safeguards, clinic visits incr eased significantly in the tele health group, increasing overall costs. The short interven tion period of one-year may ha ve limited our ability to

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150 demonstrate cost savings. Clinic visits have been noted to decline within the second year of a telehealth intervention (T.E. Barnett et al., 2006). Jennett and colleagues (2005) report that institutions should not expect shor t-term results in cost savings, and should move away from cost-benefit analysis in tele health to viewing telehealth as a long-term venture with patient utilizati on considered as success. Success may also be measured by the increase in clinic visits, as patients ar e receiving access to th e intense care their chronic illnesses require. This study indicates the f easibility of delivering h ealthcare and rehabilitative services through a telehealth model. Ca re coordination, combined with technology, allows for the provision of comp lex care regimens remotely. This is not meant to replace the relationship with the primar y care provider, but to extend it into the home. In spite of some limitations, this study and others point to the potential of integrated systems to reduce hospital and nursing home utili zation, while increasing satisfaction among patients. Clearly, the evaluation of telehealth applic ations is challenging. Future research should consider using a randomized controll ed trial design, following the intervention and comparison groups for more than 12 months considering the impact of differential use of VA services, and collecting adequate information to identify care coordinatorinitiated outpatient visits.

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151 APPENDIX INTERVIEW GUIDE FOR PARTICIPANTS Below is an outline of topics or issues to be covered. The interviewer is free to vary the wording and order of the questions, keep ing the tone of the interview fairly conversational and informal. 1. How did you learn about the LAMP / TCCP program? 2. How do you feel about the LAMP / TCCP program? 3. How has LAMP / TCCP helped you? 4. What do you think about the technology? 5. What do you think about the assistive devices? 6. Was the LAMP / TCCP program what you expected? 7. What do you like most / least about the LAMP / TCCP program? 8. What do you remember most about LAMP / TCCP? 9. What kinds of new information have you gotten from LAMP / TCCP? 10. Where do you go if you need assistance re garding the LAMP / TCCP program? 11. In general, are you satisfied with the services received from LAMP/TCCP? 12. Is there anything else you want to tell me about being in the LAMP / TCCP program? 13. Is there anything you can think of that w ould make the LAMP / TCCP program better?

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171 BIOGRAPHICAL SKETCH Roxanna Bendixen is a doctor al candidate in the Rehab ilitation Science Doctoral Program at the University of Florida. Sh e earned her bachelors de gree in Occupational Therapy (OT) in 1997. Her clinical experien ce is in the area of pediatrics, where she focused on OT services for infants, early in tervention and young adults especially in the area of assistive technology. She subsequently obtained her Masters in Health Science in 2001. She received the Lena Llorens Award fo r Academic Excellence in Research for her thesis on aging and gender differences fo r vestibular activities During her doctoral studies, she worked as a research assist ant for the Rehabilitation and Engineering Research Center for Successful Aging, Nati onal Older Drivers Res earch and Training Center, and the Rehabilitation Research and Training Center on Inde pendent Living. She has published in Topics in Geriatric Rehabilitation, Physical and Occupational Therapy in Geriatrics, and Clinical Reviews in Bone and Mineral Metabolism. Her current research focuses on the promotion of indepe ndence and quality of life for older people with disabilities through the us e of technology and assistive de vices that can make daily tasks easier and safer. To augment her re search, her dissertation focuses on the use of communications technology for remote monitori ng of frail elders in their homes.


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Permanent Link: http://ufdc.ufl.edu/UFE0017537/00001

Material Information

Title: Assessment of a Telerehabilitation and a Telehomecare Program for Chronically Ill Veterans
Physical Description: Mixed Material
Copyright Date: 2008

Record Information

Source Institution: University of Florida
Holding Location: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
System ID: UFE0017537:00001

Permanent Link: http://ufdc.ufl.edu/UFE0017537/00001

Material Information

Title: Assessment of a Telerehabilitation and a Telehomecare Program for Chronically Ill Veterans
Physical Description: Mixed Material
Copyright Date: 2008

Record Information

Source Institution: University of Florida
Holding Location: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
System ID: UFE0017537:00001


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ASSESSMENT OF A TELEREHABILITATION AND A TELEHOMECARE
PROGRAM FOR VETERANS WITH CHRONIC ILLNESSES














By

ROXANNA M. BENDIXEN


A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA


2006


































Copyright 2006

by

Roxanna M. Bendixen


































To my loving husband; he is the reason.
















ACKNOWLEDGMENTS

I would first like to express my appreciation to the Veterans Administration Office

of Academic Affairs, Pre-Doctoral Associate Health Rehabilitation Research Fellowship

and the VA Rehabilitation Outcomes Research Center for funding of this dissertation.

Additionally, I'd like to thank the VA Community Care Coordination Services for their

support and assistance with the data necessary to complete this study.

I wish to convey my gratitude to a number of individuals who have guided and

supported me throughout my doctoral studies. First and foremost, I wish to recognize my

doctoral committee. I especially thank my committee chairperson, Dr. William Mann,

for trusting in me to work on this proj ect and believing in me to make it a success. You

have always supported and inspired me and I thank you. Dr. Charles Levy, my writing

and brainstorming partner, thank you for always being available for me and for keeping

me laughing. Dr. Craig Velozo, I appreciate your guidance and support at the RORC and

your heartfelt advice. Dr. Bruce Vogel, thank you for your contributions on

methodological issues and assistance with statistical matters. I may not have fully

understood many of our conversations, but you always made me reach higher and try

harder.

Special thanks are given to Mr. Steve Olive for your invaluable help with VA

databases, your assistance with SAS programming issues, and your friendship...all of

which were essential for this dissertation. I am more than grateful to you for the long

talks, sparring conversations, and using all my cell phone minutes.










I am also very fortunate to have my dear friends and colleagues in Occupational

Therapy and the Rehabilitation Science Doctoral Program, Megan, Jessica, Patricia, Rick,

Cristina, Eric, Bhagwant, Leigh, Sande, Inga, Pey-Shan, Jai Wa, as well as those who

have gone before me, Arlene, Michael, Dennis and Michelle. I must also mention and

thank Elena, Joanne, Emily, Sandy, Orit and Sherrilene. You have made this the greatest

experience I've ever had. I will be connected to you always.

Special thanks to my fellow LAMPees, Kathy, Steve and Wendy; I wouldn't be

here without your assistance, hard work, and friendship. Also thanks to TCCP, especially

Joanne, for assistance with data collection and coding.

I thank my family for their love and encouragement, but mostly for understanding

that it just takes some people a little longer. And most notably my husband, John, whose

idea it was for me to pursue a PhD. My true love, your support and sacrifices have made

this pursuit possible. We actually did it baby.




















TABLE OF CONTENTS


page

ACKNOWLEDGMENT S .............. .................... iv


LI ST OF T ABLE S ........._..... .............._ ix...___. ....


LI ST OF FIGURE S .............. .................... xi


AB STRAC T ................ .............. xii


CHAPTER


1 INTRODUCTION ................. ...............1.......... ......


Challenges in Healthcare .............. ...............2.....
The Veterans Healthcare System ................. ............ ...... ... ....... ........ 5
The impact of aging and chronic illness in the VA ................. ............ .........6
VA telehealth applications .............. ...............6.....
Models of VA telehealth care ................. ...............7............ ...
Technology Care Coordination Program ................. .......... ... ............... 8 ....
The Low Activities of Daily Living (ADL) Monitoring Program ................... ..........1 1
Daily Remote Monitoring by LAMP and TCCP ................. ................. ........ 14
Theoretical M odel ................... .... ... ...... .. ...... .... ............ ............1
The International Classification of Functioning, Disability and Health model...15
Telehealth / ICF framework .............. ...............16....

Specific Aims............... ...............18..
Summary ............ ..... ._ ...............20....

2 REVIEW OF THE LITERATURE .............. ...............22....


Aging, Chronic Illness and Disability ................... ...............22..
Environmental contributors to functional decline ......____ ...... ....__..........23
Access to healthcare services .............. ...............25....
Information technology .............. ...............26....
Benefits to the use of IT .............. ...............28....
Barriers to the use of IT ................. ......... ...............29..
Telehealth Applications ............ _...... ._ ...............31....
Telehomecare .............. ...............32....
Telerehabilitation.......... ... ...... .. .........__ .. .......... ...............37
Telehealth applications within the Veterans Health Administration...................42












Summary ................. ...............46.................


3 HEALTH RELATED COST ANALYSIS .............. ...............49....


M ethods .............. ...............51....
Cost Data .................. ........... .... ....... .... ......... .. .......5

Linking of the Treatment Groups to the Comparison Group Pool ................... ...53
Reported long-term chronic diseases .............. ...............54....
Enrollment date .............. .. .. ...............57

Inpatient bed days of care pre-enrollment ................. .........................57
M watching ................... ... ......... ...............58.......
Telehealth vs. Standard Care ................. ...............59................

Study Design .............. ...............60....
Statistical Analysis .............. ...............61....
R e sults................... ......... ...... ........... .............6
LAMP and Matched Comparison Group .............. ...............63....

Hospital bed days of care .............. ...............64....
Clinic visits................ ...............65

Emergency room visits ................. ...............66........... ....
Nursing home bed days of care ................ ...............66...............
TCCP and Matched Comparison Group ................. .............. ......... .....67
Hospital bed days of care .............. ...............69....
Clinic visits................ ...............69

Emergency room visits ................. ...............70........... ....
Nursing home bed days of care ................ ........... ............... 70. ...
Cost Analysis: Difference-in-Differences Approach .............. ....................7
Treatment Group Comparisons .............. ...............72....
Discussion ................. ...............74.................


4 HEALTH STATUS AND OUTCOMES FROM THE VETERANS SHORT
FORM-12 HEALTH SURVEY............... ...............81.


Development of the Veteran' s SF-36 ................. ......... ......... ...........8
Veteran' s SF-3 6 Health Survey ................. ...............82...............
Development of the Veteran' s SF-12 ................ ...............83........... ..
M ethods .............. ...............86....

Design ................. ...............86.................
Participants ............... ....... ..... .............8
Administration of the SF-12V ................. ...............88........... ..
Scoring ................. ...............89........... ....
Statistical Analysis .............. ...............90....
Re sults ................ ...............90.................
Discussion ................. ...............98.................


5 PERSONAL INTERVIEWS FROM TELEHEALTH PARTICIPANTS ................106


Qualitative Research and Healthcare ................. ...............106...............












M ethods .................... ........... ...............110......
Selection of Subj ects ........._.__........_. ...............110...
Data Collection ........._.__....... .__ ...............112...
Coding Process ........._.__........_. ............... 113...
Reliability and Validity ........._.__......__........_. ... .. ......15
Re sults.........._.... .. .... _. ...............116....

Description of S ample ........._.__........_. ............... 116...
Descriptions and Themes ........._.__......__......._. .. ..... ......1
Interpretation / meaning of the data. ....._.__._ ...... ._._. ... .._._. ........17
Care coordination ........._.__......__........_. ... ........1

Technology ........._.__....... .__ ...............121...
Adaptive equipment .............. ...............127....
Satisfaction with telehealth .............. ...............129....
Reliability and validity ........._.__........_. .....__ ........ ....1
Member checking ........._._.... ......_ __ .....__._ .. ........3
Comparison with Quantitative Analysis............... ...............13
Discussion ........._.__....... .__ ...............132...


6 DI SCUS SSION ........._.__....... .__ ............... 137..


Cost Analy si s .........._.... ........._ __ ............... 13 8...
Health-Related Quality of Life ........._.__......__......... .. ..... ......4
Personal Interviews............... ..............14
Summary ........._.__....... .__ ...............147...


APPENDIX: INTERVIEW GUIDE FOR PARTICIPANTS AND/OR
CAREGIVERS .............. ...............151....


LIST OF REFERENCES ............. ...... ._ ...............152..


BIOGRAPHICAL SKETCH ............. ...... __ ...............171...





























V111

















LIST OF TABLES


Table pg

2-1 Health-related applications for information technology .............. ....................2

3-1 Baseline characteristics of telerehabilitation group, Low ADL Monitoring
Program (LAMP), and matched comparison group ................ .......................63

3-2 Healthcare expenditures for LAMP (n=1 15) one-year pre-enrollment and one-
year post-enrollment............... ............6

3-3 Healthcare expenditures for LAMP matched comparison group (n=1 15) one-
year pre-enrollment and one-year post-enrollment ................ ................ ...._.64

3-4 Baseline characteristics of telehomecare group, Technology Care Coordination
Program (TCCP), and matched comparison group .............. ....................6

3-5 Healthcare expenditures for TCCP (n=1 12) one-year pre-enrollment and one-
year post-enrollment............... ............6

3-6 Healthcare expenditures for matched comparison group (n=1 12) one-year pre-
enrollment and one-year post-enrollment ................. ............. ......... .......68

3-7 Multivariable regression analysis summary examining the relationship among
LAMP and matched comparison group. ............. ...............71.....

3-8 Multivariable regression analysis summary examining the relationship among
TCCP and matched comparison group............... ...............72.

3-9 Multivariable regression analysis summary examining the relationship in
healthcare costs between LAMP and TCCP. ............. ...............73.....

4-1 Short Form Health Survey-36V questions with respective Short Form Health
Survey-12V questions .............. ...............84....

4-2 Characteristic s of participants ................. ...................... ..................91

4-3 Differences between SF-12V baseline and 12-month follow-up for LAMP
(paired sample statistics) .............. ...............92....

4-4 Differences between SF-12V baseline and 12-month follow-up for TCCP
(paired sample statistics) .............. ...............93....










4-5 Group differences for SF-12V baseline scores .............. ...............95....

4-6 Group differences for SF-12V at 12-month follow-up .............. ....................9

4-7 TCCP group differences for SF-12V at baseline .............. ..... ............... 9

4-8 Cross-sectional relationship between presence of primary medical condition,
physical component summary (PCS-12) at baseline and 12 months for two
telehealth cohorts (LAMP and TCCP, n=229), and VA PCS norms.. .....................97

5-1 TCCP and LAMP sample demographics ........... ..... ._ .........__........1

5-2 Coding structure for qualitative interviews ................. .............................117

5-3 Coding results from qualitative interviewees............... .............13
















LIST OF FIGURES

Figure pg

1-1 The International Classification of Functioning, Disability and Health (ICF)
comparison of LAMP and TCCP ..........__.......__ ....__ ...........1

3-1 Preparation of comparison pool for final matching to LAMP and TCCP. .............55











Abstract of Dissertation Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Doctor of Philosophy

ASSESSMENT OF A TELEREHABILITATION AND A TELEHOMECARE
PROGRAM FOR VETERANS WITH CHRONIC ILLNESSES

By

Roxanna M. Bendixen

December, 2006

Chair: William C. Mann
Major Department: Rehabilitation Science

In the United States today, over 100 million individuals suffer from chronic

illnesses. Each year chronic illnesses account for approximately 70 percent of all U.S.

deaths and 75 percent of all healthcare costs. Chronic conditions often lead to

disabilities, which result in functional limitations and loss of independence, thereby

increasing medical expenditures. The elderly population is at a higher risk for developing

chronic conditions such as diabetes, heart disease, or arthritis, increasing their risk for

disabilities. The disability rate of the population over age 65 is at least three times higher

than the general population. Given the rapid growth of the aging population, and the

chronic illnesses, disabilities, and loss of functional independence endemic to elders,

novel methods of rehabilitation and care management are urgently needed. Telehealth

models that combine care coordination with communications technology offer a means

for decreasing healthcare costs and increasing patient satisfaction, and have been shown

to be an important component in the management of chronic illnesses.

This dissertation examined the effects of a Veterans Administration (VA)

telerehabilitation program (Low ADL Monitoring Program LAMP) and a VA










telehomecare program (Technology Care Coordination Program TCCP) on healthcare

costs, as well as patient reported health-related quality of life measures. Additionally, a

qualitative study utilizing a random sampling of veterans enrolled in LAMP and TCCP

provided patients' perceptions on telehealth interventions, the technology used for home-

based remote monitoring, and satisfaction with VA healthcare services.

TCCP is based on a medical model of care. LAMP is based on a rehabilitative

model of care. LAMP patients received adaptive equipment and environmental

modifications, which focused on self-care and safety within the home. Care-coordinators

for LAMP and TCCP remotely monitored their patient' s vital signs, such as blood

pressure and weight, and provided education and self-management strategies for

decreasing the effects of chronic illnesses. Healthcare costs post-enrollment were

examined through a difference-in-differences multivariable model. Results determined

that there were no significant differences between LAMP and their matched comparison

group, TCCP and their matched comparison group, or LAMP and TCCP, following the

12-month intervention. For TCCP patients, daily remote monitoring resulted in increases

in all healthcare costs. For LAMP patients, the provision of adaptive equipment and

environmental modifications, plus intensive in-home monitoring of patients, lead to

significant increases in clinic visits post-intervention, but decreases in hospital and

nursing home stays. LAMP patients also increased in physical functioning based on self-

report from the Veterans Quality of Life SF-12V. Through personal interviews, veterans

reported increased connectedness with the VA, found the technology easy to use, were

satisfied with the services, and would recommend telehealth to their peers.















CHAPTER 1
INTTRODUCTION

The declining health of our elders is one of the greatest medical problems and

greatest economic burdens facing the U.S. today (Fries, 2002). Approximately 70

percent of healthcare spending in the U.S. is focused on the health of our elder population

(Centers for Disease Control [CDC], 2003a). In 2005, this amounted to more than 1.3

trillion dollars, and is expected to rise to 2.5 trillion dollars by 2014, totaling more than

13 percent of the gross domestic product (Heffler et al., 2005). Of particular concern is

the increase in chronic illness and disability in our aging population, which is proj ected to

rise sharply through 2030 as the baby boom generation enters old age (Department of

Health & Human Services [DHHS], 2004). Chronic illnesses contribute to disability,

diminish quality of life, and increase health and long-term care costs (CDC, 2003b;

Ostchega, Harris, Hirsch, Parsons, & Kington, 2000). In fact, chronic illnesses are

among the leading causes of death and functional disability in older adults (Freedman,

Martin, & Schoeni, 2002; Murray & Lopez, 1996). The aging population, especially

those who are chronically ill and disabled, place a strain on healthcare resources and

challenge healthcare providers. Healthcare programs that could assist elderly patients in

the self-management of their chronic illnesses and limit hospital and emergency room

visits could potentially reduce the overall economic burden of these diseases.

The purpose of this dissertation is to examine the effectiveness of two telehealth

programs within the Veterans Health Administration (VHA) that were designed to serve

at-risk elders. A retrospective, concurrent matched cohort study design was employed to









determine healthcare costs and functional health status from a telehomecare program and

a telerehabilitation program. Additionally, the telehealth participants' personal

experiences were investigated through qualitative interviews in their homes. This study

provides valuable information regarding telehealth models of care that may assist in

managing chronic illness and disability in our elderly population, therefore reducing

health-related costs and increasing safety and independence within the home

environment.

Challenges in Healthcare

Tending to the multiple disease processes that often coincide in chronically ill

elders can be quite challenging to healthcare providers. Primary care providers are often

called to concurrently manage a variety of illnesses in the same patient, requiring

increasingly complex medical regimens (E. H. Wagner, 2001). The best possible

outcomes depend on the delivery of a multitude of services, including preventive care,

disease management, and rehabilitation. Such coordinated services hold the greatest

promise for improving the health of our elderly. Yet the provision of evidence-based,

comprehensive care is exceedingly difficult and numerous barriers exist (Grumbach &

Bodenheimer, 2002).

Preventive care measures include regular health maintenance evaluations,

immunizations and vaccines, and laboratory testing (Godfrey, 2001). Screening for

additional chronic and/or life-threatening diseases or exacerbation of an illness is also

important. An essential preventive measure for individuals with chronic illnesses is to be

knowledgeable of their healthcare regimens and be in regular contact with their

healthcare provider. Yet it is difficult for primary care providers and patients to maintain

contact and keep track of necessary screenings, laboratory tests and immunizations.









Additionally, elders often have functional disabilities and numerous comorbid conditions,

reducing their ability to manage their chronic diseases (E. H. Wagner, 2001). Moreover,

studies have shown that preventive care has had limited success in decreasing the

incidence of chronic illness and disability (Godfrey, 1999; Tulloch, 2005; Walker &

Jamrozik, 2005). The reasons preventive care is not always successful vary. Many

conditions may be overlooked by conventional care, such as urinary tract infections,

diabetes and anemia, as well as depression and dementia (Tulloch, 2005). Therefore,

individuals who have not been adequately diagnosed will not receive the necessary

preventive measures for self-management of their disease. Furthermore, there is limited

training of nurses and physicians in preventive care for elders, as well as inadequate

health education for elders themselves (Williams, Ricketts, & Thompson, 1998). Other

barriers associated with receipt of preventive services include provider continuity and site

continuity, as well as inadequate health insurance coverage and difficulty traveling to

visit specialists (Doescher, Saver, Fiscella, & Franks, 2004).

Disease management "is a system of coordinated healthcare interventions and

communications for populations with conditions where patient self-care efforts are a

significant factor in supporting the physician/patient relationship and their plan of care"

(Disease Management Association of America [DMAA], 2006). Disease management

programs for patients with chronic illnesses, such as diabetes, have become increasingly

common in recent years as a mechanism to help educate patients on how to self-manage

their disease (Congressional Budget Office [CBO], 2004; New et al., 2003; Stille, Jerant,

Bell, Meltzer, & Elmore, 2005). Disease management programs typically include clinical

guidelines for disease phases, patient education for self-management, aggressive









screening for complications, and coordination of care among numerous healthcare

providers (CBO, 2004; Gamm, Bolin, & Kash, 2005). Yet, under a system designed for

acute and episodic care, healthcare providers, as well as patients themselves, are not

always focused on disease management (Bodenheimer, Wagner & Grumbach, 2002a).

Additionally, the impact of disease management programs is mixed. Disease

management programs are difficult to efficiently provide because they require ongoing

collaboration, patient self-management education, compliance, routine reporting and

outcomes measurement (CBO, 2004; Leider & Krizan, 2004; Roglieri et al., 1997; E. H.

Wagner, 2000).

Lastly, understanding the longer-term consequences of chronic diseases is as

important as the immediate management of the disease, and deserves attention.

Rehabilitative interventions are aimed at reducing disability and improving independence

and function (Godfrey, 2001). In rehabilitation, a multidisciplinary team works

cohesively with patients to carefully assess their strengths, deficits, and personal desires

for achieving their highest functioning level and living an independent life.

Rehabilitation is a creative and individualized process of preparing an individual with a

disability to preserve or regain optimal functional independence and adapt to physical

limitations and architectural barriers (Godfrey, 2001; Hochstenbach, 2000). However,

obstacles exist that make it difficult for the elderly to receive adequate and timely

rehabilitative services. Such obstacles include availability of specialists, appropriate

assessments and recommendations of services and assistive devices, and traveling to

access services. Additionally, when rehabilitation is received strictly in a clinical setting,

carryover into the home may be sub-optimal. Delivery of care within the home is able to










target key areas to stem this, yet home rehabilitative services are rarely provided and

when provided are often of an inadequate duration and intensity.

Other factors also impact the ability to receive adequate and timely healthcare.

There are notable healthcare disparities for individuals who live in rural areas, including

problems of management and provision of services due to difficulties with access and

transportation outside the home (Eldar, 2001; Freedman et al., 2002). These challenges

are further compounded by clinic and healthcare facilities that have a limited number of

physical locations from which they can provide patient treatment. Difficulties in access

also occur due to problems with recruitment and retention of practitioners in rural areas.

To date, little progress has been made toward restructuring healthcare systems to

address these concerns. Recent reports in healthcare trends urgently recommend an

overhaul of American' s healthcare system (Bodenheimer et al., 2002a; Institute of

Medicine [IOM], 2001; E.H. Wagner, 2004). Given the rapid growth of the aging

population, and the chronic illnesses, disabilities, and loss of functional independence

endemic to elders, novel methods of care management and care delivery are urgently

needed.

The Veterans Healthcare System

The Department of Veterans Affairs (VA) is responsible for operating nationwide

programs for healthcare, financial assistance and burial benefits to veterans and their

families. The most visible of the VA systems is healthcare. The Veterans Health

Administration (VHA) is the largest integrated healthcare system in the U.S., providing a

multitude of services to over 5 million veterans in fiscal year 2005 (Office of Public

Affairs [OPA], 2006). Because the VHA provides a uniform and comprehensive set of









healthcare benefits for their patients, it is a useful system to explore resource use and

patient outcomes.

The impact of aging and chronic illness in the VA

In fiscal year 2005, the VHA provided medical care to over 5.3 million veterans at

a cost of $3 1.5 billion (OPA, 2006). Much of the VHA' s medical care is focused on a

rapidly aging and chronically ill veteran population. The number of veterans over the age

of 85 is increasing by a mean rate of 11 percent a year, and is proj ected to reach

approximately 1.3 million by the year 2010 (Yu, Ravelo, Wagner, & Barnett, 2004).

Although the increasingly aging veteran population has amplified the demand for

healthcare services, studies have show that the presence of chronic illnesses combined

with aging has a more significant effect on healthcare costs than age alone (Asch et al.,

2004; Yu, 2004; Yu, et al., 2003a). Veteran's enrolled in the VHA report a higher

prevalence of recent or long term chronic disease than their community counterparts

(Asch et al., 2004; Kazis et al., 2004b; Rogers et al., 2004). In a recent study of

prevalence and costs of chronic conditions in the VHA, Yu and colleagues (2004),

reported that among the VA patients aged 65 and older, 85 percent had one or more

chronic conditions, with 40 percent having three or more. Chronic illnesses, which are

the main reason veterans seek care through the VHA, accounted for 96 percent of the

total VA healthcare expenditures in 2000.

VA telehealth applications

As more veterans are facing debilitating chronic diseases, there is a need to ensure

timely access to preventive care, disease management, and rehabilitative care. Beginning

in April 2000, the VHA initiated funding of several clinical demonstration proj ects

related to telehealth to test the integration of care coordination with communications









technology for home-based disease management (Meyer, Kobb & Ryan, 2002). The

complexity of our veteran' s healthcare needs places greater demand on coordination of

care. In the past, care or case management was defined by an episode of care, either in

the clinic or hospital, typically with a set number of phone calls to follow-up on a patient

after discharge. The VA care coordination model combines the role of a care coordinator

with home telehealth technologies that allow for consistent follow-up that transcends

clinical programs and physical settings. The Care Coordinator is responsible for being a

team member, providing a clinical thread between therapists, specialists and general care,

and providing consistent information on the veteran's response to treatment at home.

Telehealth models which combine care coordination with communications technology

offer a means for decreasing healthcare costs and increasing patient satisfaction, and have

been shown to be an important component in the management of chronic illnesses

(Bennett, Fosbinder, & Williams, 1997; Hooper, Yellowlees, Marwick, Currie, &

Bidstrup, 2001; Joseph, 2006; Kobb, Hoffman, Lodge, & Kline, 2003; Noel, Vogel,

Erdos, Cornwall, & Levin, 2004). Today, telehealth and the use of telecommunications

technology is widely used by the VA, which views telehealth as integral to the delivery of

health services and education within their systems. Thus, it is not surprising that the VA

has placed a maj or emphasis on the development of various in-home telehealth models of

care, such as telehomecare and telerehabilitation.

Models of VA telehealth care

Telehomecare. Telehomecare (THC) uses technology to enable the

communication and transfer of information between the healthcare provider at a clinical

site and the patient in the home (Finkelstein et al., 2004). A typical application of THC is

the use of telehealth technology with oversight by nurse practitioners who provide









medical care for chronically ill individuals within their homes (Celler, Lovell, &

Basilakis, 2003; Finkelstein, Speedie, & Potthoff, 2006; Kobb et al., 2003; Noel et al.,

2004). Using telehealth technology, home-based video visits and monitoring of vital

signs can be accomplished electronically, medication compliance can be verified, and

patient education can be enhanced. Within the VHA, the THC model is based on the

traditional medical model of care. Professionals working in the field of THC are skilled

in the management of chronic illnesses through diagnosis, medical intervention and

patient education. THC interventions are typically disease-specific and focus on the

monitoring of physiologic parameters. A very important clinical goal in THC is to

minimize the impact of the condition, often through symptom tracking, which results in a

medical intervention.

Technology Care Coordination Program

The Technology Care Coordination Program (TCCP) is a VA telehomecare

program that uses telehealth technology in conjunction with nurse practitioners and a

social worker to coordinate care for chronically ill veterans living in remote areas in

North Florida/South Georgia. During our study period, veterans were eligible to be

enrolled in TCCP if they met the following criteria:

1. had past high-cost medical care needs (>$25,000) and high healthcare utilization
(two or more hospitalizations and frequent emergency room visits),
2. had electricity and phone service,
3. accepted technology in their homes for monitoring purposes, and
4. signed an informed consent form or had the consent form signed by a proxy.

The TCCP targeted veterans with multiple co-morbidities such as congestive health

failure (CHF), diabetes, hypertension, and chronic obstructive pulmonary disease

(COPD). The care coordination (CC) team consisted of two nurse practitioners, a social

worker, and a program support assistant for office support. Veterans were identified as










high-facility use, high-cost by the VHA's computerized cost allocation system. Veterans

were then contacted by telephone to determine their interest in participating in TCCP.

Following initial contact, an enrollment appointment was made to visit the home, explain

the program, and assign and install the technology for remote monitoring. TCCP

participant' s health-related quality of life was measured by the Veteran' s Quality of Life

SF-36V Health Survey Form (SF-36V) and the shorter version, the SF-12V (Brazier et

al., 1992) at baseline and every 6-months thereafter.

Veterans enrolled in TCCP were risk-stratified into three levels based on severity

of disease, functional and cognitive status, living situation, and type of residence and

provided with different remote monitoring devices based on the stratification. (1)

Veterans with stable chronic illnesses and psychosocial issues impacting health received

a videophone. The videophone is a stand-alone device that connects to a regular

telephone line and allows video and audio input between the veteran and the CC. (2)

Veterans with frequent hospitalizations, who lived in a private residence, and were able

to read, received a Health Buddy (HB) (Health Hero Network, Inc., Redwood City,

California). The HB is an in-home messaging device that serves as the interface between

patients at home and CCs located at the VA. The HB presents veterans with a list of

questions they answer by selecting one of four options to help monitor and assess a

patient's clinical condition, and provides education for the patient based on their answers.

An example of a question includes, "How do you feel today?" with answers excellent,

good, fair, or poor; with a follow-up response based on the answer. Another question

may be, "Have you fallen?" with answers yes or no, and a follow-up question if the

answer is yes, "Do you need medical attention?" with answers yes or no. Should the










patient require medical attention, the HB will provide the patient with the phone number

for the VA and alert the patient's care coordinator for follow-up. Patient data is sent over

a telephone line through a secure data center where the data is then available for review

on the Health Buddy@ Desktop. Patient responses are color-coded by risk level as High

(red), Moderate (yellow) and Low (green) based on symptoms, patient behaviors and

self-care knowledge. (3) Veterans with frequent hospitalizations, who lived in a

congregate or private setting, were able to handle peripherals, and had a diagnosis such as

heart failure or emphysema, received an Aviva (Centralia, WA). The AVIVA Home

Telecare System consists of a central station, which connects via ordinary telephone lines

to the patient station, which is placed in the patient's residence. The Aviva functions with

a PC-based monitoring station and two-way video to allow care coordinators to visually

monitor the patient remotely. The Aviva program provides live audio and video

communication with a CC.

Telerehabilitation. Telerehabilitation (TRH) is an emerging practice defined as

the remote delivery of rehabilitation services through compensatory strategies, training

and education, monitoring, and long-term care of individuals with disabilities using

assistive technology (Office for the Advancement of Telehealth [OAT], 2002). The focus

of TRH is to increase access to rehabilitation services, and to allow individuals to achieve

and maintain safe and independent lives in their own homes. TRH has the potential to

manage multiple components of health, including functional independence, self-care and

self-management of illness (Burns, Crislip, Daviou, Temkin, & Vesmarovich, 1998;

Cruise & Lee, 2005; Halamandaris, 2004b; Winters, 2002). TRH is a rehabilitative

model of care, which views health as more than the absence of disease. As health is









intimately related to and influenced by the environment and the person's characteristics

(Brandt & Pope, 1997; Dahl, 2002), many TRH programs emphasize the whole person

and focus on decreasing the impact of chronic illnesses, thereby improving health and

functional outcomes. TRH assesses the immediate environment (home) and provides

interventions such as education and training, therapeutic exercises, adaptive devices, and

simple home modifications in an attempt to improve daily function (Cieza & Stucki,

2005).

The Low Activities of Daily Living (ADL) Monitoring Program

The Low ADL Monitoring Program (LAMP) is a VA telerehabilitation program

designed to promote independence and reduce healthcare costs. LAMP services are

home-based and use a combination of traditional and advanced technologies to promote

independence and the maintenance of skills necessary to remain living at home.

Occupational therapists (OT) serve as care coordinators for veterans, and work

collaboratively with healthcare providers, rehabilitation specialists and other clinicians,

as well as with families and caregivers. LAMP interventions range from the provision

and installation of assistive technology/adaptive equipment (AT/AE) and modifications

of the home environment to daily therapeutic regimens, and on-going support for self-

care needs. LAMP staff also provides hands-on and remote training in the use of AT/AE.

For our study period, participants were eligible to be enrolled in LAMP if they met

all of the following criteria:

1. lived at home,
2. had a functional deficit with at least two ADL's, (transferring and mobility are
considered ADL's for the purpose of inclusion),
3. had electricity and phone service,
4. accepted technology in their homes for monitoring purposes, and
5. signed a consent form or had the consent form signed by a proxy.









The LAMP target population included veterans with multiple co-morbidities such

as arthritis, diabetes, hypertension, and stroke. The LAMP CC team consisted of two

licensed OTs, a technology expert also assisted with technology installation and training,

and a program support assistant provided office support. Following eligibility

determination, a licensed OT conducted a physical/functional, cognitive, and home

assessment in each of the study participants' homes. The assessment included

instruments that measured functional independence, cognition and quality of life. Two

instruments were used to measure functional status: the Older Americans Research and

Service Center Instrumental Activities of Daily Living (IADL) (Fillenbaum, 1988), and

the motor subscale of the Functional Independence Measure (FIM) (Fricke, Unsworth, &

Worrell, 1993; Pollak, Rheult, & Stoecker, 1996). Mental status was evaluated through

the Mini Mental Status Examination (M. Folstein, S. E. Folstein, & McHugh, 1988) and

the cognitive subscale of the FIM. Health-related quality of life was measured by the

Veteran' s Quality of Life SF-36V Health Survey Form (SF-36V) and the shorter version,

the SF-12V (Brazier et al., 1992). Veterans enrolled in LAMP received functional,

cognitive, and health-related quality of life measurements in their home at baseline and

12-month follow-up. A comprehensive home assessment was conducted and included

evaluation of the home' s exterior and interior, focusing on accessibility and safety.

Subsequently, care plans were developed based on information obtained from these

assessments. Care plans included the type of adaptive equipment needed to increase

safety and independence within the home, the type of technology to be used for remote

monitoring, and health-related diagnostic parameters. An additional home visit for

installation and training on each piece of equipment was required.









Three different communications systems were used for LAMP remote monitoring:

(1) a basic computer with internet capability, (2) a smartphone (cell phone) with internet

capability, and (3) the HB. Veterans who met criteria for computers or smartphones

demonstrated either past computer knowledge, or the cognitive and physical abilities

necessary for computer or smartphone use. Motivation to learn and use the computer or

smartphone was also considered. Veterans who did not meet criteria for computers or

smartphones received a HB. The HB was installed during the initial evaluation, whereas

additional home visits were required for installation and training on the use of the

computer or smartphone. Veterans who required more than 3 home visits for computer

or smartphone training were switched to a HB.

LAMP was based on preliminary work performed by Mann and colleagues (Mann,

Hurren, Tomita & Charvat, 1995; Mann, Marchant, Tomita, Fraas, & Stanton, 2001;

Mann, Ottenbacher, Fraas, Tomita & Granger, 1999) which showed that functional

decline may be attenuated through the provision of AT/AE. LAMP services were based

on the experience of Mann' s 3-year National Institute on Disability and Rehabilitation

Research (NIDRR) funded study where frail elders were provided adaptive equipment

and monitored for self-care needs using computers with video-teleconferencing

capability. Results from their study demonstrated that frail elders experienced functional

decline over time, but indicated that compared to a control group the rate of decline could

be slowed, and institutional and certain in-home personnel costs reduced, through a

systematic approach to providing AT/AE and home modifications. Other studies have

also demonstrated that the use of AT/AE can provide assistance for individuals with

disabilities (Berry & Ignash, 2003; L.M. Verbrugge & Sevak, 2002; Gitlin, et al, 2006).










Daily Remote Monitoring by LAMP and TCCP

Daily remote monitoring comprises a multi-component, chronic disease

management model through the review of personal health dialogues. TCCP and LAMP

daily remote monitoring included patient assessment based on a variety of health-related

diagnostic parameters, such as blood pressure or blood sugar readings. Disease-specific

education was provided based on individual healthcare needs. Patient adherence to

medication and treatment plans was also addressed. Maintaining daily contact with

telehealth patients allowed for comprehensive patient-provider communication, and

follow-up support. LAMP patients were assessed daily on the same health-related

diagnostic parameters as the TCCP patients, but were also monitored on self-care

parameters and the promotion of therapeutic lifestyle changes. LAMP daily self-care

reports included information on falls, self-care and mobility throughout the home

environment, as well as the ability to get outside of the home and participate in leisure

and social activities. Communications technology provided both LAMP and TCCP Care

Coordinators (CC) with the necessary information to evaluate health status and provide

immediate intervention and ongoing care management through the VA. Care

management is important for accessible, coordinated, and continuous healthcare across

all settings, especially the home.

Theoretical Model

In all areas of healthcare, theoretical models and frameworks are important for

clinical practice, research and education. The World Health Organization (WHO)

International Classification of Functioning, Disability, and Health (ICF) is a framework

designed to classify health and health-related states (World Health Organization [WHO],

2001). The ICF has broad applications to a variety of areas in medicine and









rehabilitation, and provides the basis for understanding the interrelationships between the

person, the environment, health, and function. The ICF allows us to illustrate the

instrumental similarities and differences between THC and TRH.

The International Classification of Functioning, Disability and Health model

The ICF is considered a biopsychosocial model, as it integrates the medical model,

the disability model, and the social model to view health as being influence by the

condition, the person, and the environment (WHO, 2001, 2002).

The ICF has shifted the focus from health as a "consequence of disease" to a

"components of health" classification (2001). The ICF provides a scientific basis for

viewing and studying all health conditions, allowing them to be compared using a

common measure of function and disability (Dahl, 2002). Part I of the ICF framework

focuses on Functioning and Disability; Part II of the ICF relates to Contextual Factors.

Both Part I and Part II have interrelating components, and functioning and disability are

seen as outcomes of interactions between both parts. The two interrelating components

within Functioning and Disability are "body functions and structures", and "activity and

participation." Interrelating components within Part II, Contextual Factors, are

comprised of "environmental factors" and "personal factors." To classify each of these

components, the ICF uses qualifiers. Qualifiers allow one to measure the presence or

severity in the level of functioning within the body, person and society. Therefore,

function is not limited to a single domain but is a dynamic blending of components across

domains. This indicates that the true marker of success for any individual undergoing the

process of rehabilitation is not only regaining physical and cognitive function, but by

participation in life activities (Fougeyrollas & Beauregard, 2001).









The ICF allows for the measurement and reporting of health at an individual and

population level, and has been used in the evaluation of numerous healthcare systems and

the study of healthcare interventions (Arthanat, Nochaj ski, & Stone, 2004; Bilbao et al.,

2003; Haglund & Henriksson, 2003; Lomax, Brown, & Howard, 2004; Mayo et al., 2004;

Stamm, Cieza, Machold, Smolen, & Stucki, 2004; Stucki et al., 2002a; Stucki, Ewert, &

Cieza, 2002b; Weigl et al., 2003). The ICF is an important component for healthcare

policy design and implementation. The implementation of new strategies in healthcare

requires coordinated efforts and significant investment in research. This is especially true

of applications in telehealth.

Telehealth / ICF framework

Chronic illnesses and their impact on veterans and the VA healthcare system are

the focus of this study. Chronic illnesses relate to the ICF component of Functioning and

Disability. Chronic illnesses involve domains within body functions and body structures

such as the cardiovascular and respiratory systems, as in hypertension or chronic lung

disease, neuromusculoskeletal and movement systems involved with osteoarthritis, and

the metabolic system related to diabetes mellitus. The ICF component of Activities and

Participation are likely to be negatively affected through impairments within the body

functions and structures, which often accompany chronic illnesses. Such impairments

may limit one's ability to independently perform self-care, engage in work, or spend time

with family and friends. Contextual Factors includes environmental factors and personal

factors. Environmental factors include interventions within the home environment,

assistive devices and technology, and support and relationships that may be enhanced

through care coordination. Personal factors vary based on one's particular background

and are not a feature of a chronic or disabling illness. In this study personal factors









include age, marital status, and disease state. The ICF model demonstrates that chronic

illness related impairments, combined with environmental and personal factors, may

decrease one's ability to function within the home.

TCCP uses the medical model in conjunction with communications technology to

coordinate care for chronically ill individuals. The medical model of care places an

emphasis on diagnosing and successfully treating a disease. Functioning and health are

seen primarily as a consequence of a disease. This disease-specific model dominates the

healthcare system (Kaplan, 2002). TCCP focuses on minimizing the impact of the

condition through symptom tracking and the provision of a medical intervention.

However, symptom tracking alone may not give us an accurate picture of how a chronic

illness actually affects a person's everyday life (Kaplan, 2002; Ustun, Chatterji,

Bickenbach, Kostanj sek, & Schneider, 2003). Chronic conditions are usually not cured,

and require ongoing disease management, patient education, and provision of resources

to assist patients to cope with the impact of the illness.

The rehabilitation model of care is a continuous process that ranges from

identifying difficulties and needs, relating the difficulties to impaired body functions and

structures, targeting the person and the environment through interventions, and managing

the interventions (Stucki et al., 2002b). Therefore, the severity of an illness may be

reduced through the provision of environmental modifications and adaptive devices to

remove the limitations that alter functioning. LAMP uses the rehabilitation model to

coordinate care for chronically ill individuals through assessing personal and

environmental factors in order to provide the appropriate technology for remote

monitoring, as well as modifying the immediate home environment through the addition











of grab bars to a shower stall, recessed doorways for accessibility, or ramps for entrance

into a home. From the LAMP perspective, function is not only an outcome, but also an


important component of assessment, intervention, and quality of care (Cieza & Stucki,

2005). These factors can be put into perspective through the use of the ICF model.


Health Condition
(Disorder/Disease)
Chronic Elness and Disability





Body Stnicture and Activities Participation
Body Functions LAMP LAMP
LAMP and TCCP MY ADL's and IADL's MY Self-care, Social
Cardiovascular, Neuromuscular, including Bathing, Participation, Work
Metabolic Mobility, Getting Outside
of the Home







Environmental Factors Personal Factors
LAMP and TCCP LAMP and TCCP
Adaptive Equipment, Assistive Age, Marital Status,
Technology, Healthcare Support Diagnoses

Figure 1-1. The International Classification of Functioning, Disability and Health (ICF)
comparison of LAMP and TCCP

Specific Aims

The proposed study will evaluate a telehomecare and telerehabilitation model of

care for chronically ill veterans using both quantitative and qualitative methods.

Telehomecare and telerehabilitation strategies will be assessed from multiple


perspectives including cost effectiveness, functional/health status, and patient satisfaction

with telehomecare and telerehabilitation models.


The purpose of this dissertation is to explore differences in health-related outcomes

and costs between (1) veterans enrolled in a telerehabilitation intervention (LAMP); (2)









veterans enrolled in a telehomecare intervention (TCCP); and (3) veterans who receive

VA standard care without a telehomecare or telerehabilitation intervention. In addition,

we will qualitatively explore the "experience" of a telerehabilitation and a telehomecare

intervention through personal stories from veteran telehealth enrollees.

By targeting veterans with chronic illnesses and disabilities, we anticipate that the

provision of compensatory strategies (adaptive equipment) and home monitoring through

communications technology will proactively manage the consequences of chronic

illnesses, increase safety and independence, and thereby enhance functional

independence and reduce institutional care and other healthcare costs. This study is an

important addition to the limited research available, as it combines both a cross-sectional

qualitative analysis with retrospective quantitative analyses utilizing longitudinal (1-12

months) data.

For these hypotheses, VA services are defined as costs for hospital bed days of

care, emergency room visits, nursing home bed days of care, and clinic visits. Four

groups of veterans will be compared: (1) veterans receiving the LAMP intervention

(telerehabilitation); (2) veterans receiving the TCCP intervention (telehomecare); (3) a

comparison group of veterans matched to LAMP and TCCP based on primary diagnoses,

number of hospital bed days of care 12-months pre-study, and demographic variables of

age and marital status.

Specific Aim 1: To quantify the effect of telerehabilitation and telehomecare in reducing

healthcare costs among the four groups of veterans.

Hypothesis 1: Veterans enrolled in LAMP, veterans enrolled in TCCP, and their

corresponding matched group of veterans who have not received telerehabilitation









or telehomecare interventions will differ in their use of VA services and healthcare

costs.

Specific Aim 2: To define the effect that telerehabilitation exerts in promoting functional

independence by comparing functional health status measurements within and between

the two telehealth groups.

Hypothesis 2: Veterans enrolled in LAMP and veterans enrolled in TCCP will

differ in functional health status following a 12-month enrollment period.

Specific Aim 3: To evaluate the effect of telerehabilitation and telehomecare

interventions on satisfaction with VA services.

Research Question (Qualitative Data): How do veterans and their caregivers

describe their experiences with telerehabilitation and telehomecare interventions?

This study utilized existing quantitative data sets comprised of patient' s medical

history, clinical assessments, and VA health-related expenditures for chronically ill and

disabled veterans enrolled in telerehabilitation, telehomecare or receiving standard VA

care. We examined relationships between the four groups of veterans based on costs of

healthcare services, as well as LAMP and TCCP patient reported health-related quality of

life measures. Additionally, as patient's perceptions of disease, illness and health have

been deemed critically important by the VA (Kazis et al., 2004b), a qualitative review

was initiated utilizing a sampling of veterans enrolled in a telerehabilitation (LAMP) and

telehomecare (TCCP) intervention.

Summary

The special care needs required for individuals with chronic illness and disabilities,

coupled with the VA integrated healthcare system, make it an excellent model for

studying care delivery innovations. Thoughtful evaluation of telehealth models will help









to clarify potential roles of telehomecare and telerehabilitation interventions to reduce

chronic illnesses and disabilities and enhance safety and independence in the home.

Outcomes from this study will allow assessment of the impact of veteran' s illnesses

and physical impairments on system utilization. The cost analysis permits identification

of the benefits of the telehomecare and telerehabilitation systems compared to usual care.

Results from this study will help advance knowledge and promote innovations that will

contribute to optimal care of chronically ill and disabled veterans who are living at home.

















CHAPTER 2
REVIEW OF THE LITERATURE

Aging, Chronic Illness and Disability

According to the 2000 U.S. Census, more than 35 million people in the United

States are aged 65 and older (Gist & Hetzel, 2004). This constitutes approximately 12.4

percent of the total U.S. population. The number of elders aged 85 or older is the fastest

growing cohort. By the year 2010, the 85+ population is expected to reach 6.1 million

and account for approximately 1.2 percent of the total U.S. population (DHHS, 2004).

Paralleling this population increase is the proj ected increase in the numbers of elderly

with poor health (DHHS, 2004). Illnesses affecting the elderly impact life expectancy

and healthcare costs considerably, placing more and more demands on the public health

system and on medical resources (Joyce, Keeler, Shang, & Goldman, 2005). The

maj ority of healthcare resources for the elderly are now devoted to the treatment of

chronic conditions (CDC, 2003b). Total healthcare costs for individuals with chronic

conditions are more than five times higher than healthy individuals (Partnership for

Solutions [PFS], 2004).

The elderly population is at risk for developing chronic conditions such as diabetes,

heart disease, and arthritis. Chronic conditions are the leading cause of death and

disability in the elderly, accounting for approximately 70 percent of all deaths and 75

percent of all healthcare costs (CDC, 2003a). The disability rate of the population over

age 65 is at least three times higher than the general population (Chan et al., 2002; Gist &









Hetzel, 2004). Disability causes functional limitations in activities of daily living (ADL),

such as walking, transferring, bathing and toileting. Approximately 43 percent of those

individuals over age 65 report difficulties with self-care and mobility activities within the

home (Gist & Hetzel, 2004). A systematic review of literature by Freedman and

colleagues determined a correlation between aging, chronic illnesses and disabilities and

the need for personal assistance with daily living tasks (Freedman et al., 2002).

Difficulties performing ADLs, such as bathing or ambulating, generate the need for

personal assistance or placement in a residential facility and significantly increase

medical expenditures (Gill & Kurland, 2003; Naik, Concato, & Gill, 2004; Ostchega et

al., 2000). Chan, et al. (2002) reviewed disability and healthcare costs and determined

that functional limitations in ADLs may be an independent risk factor for increases in

healthcare expenditures. The authors reported that the total mean healthcare costs for the

most disabled (i.e., those reporting 5-6 ADL limitations) was more than seven times

higher than for individuals without functional limitations.

Environmental contributors to functional decline

Functional limitations imposed by chronic conditions threaten an elders' quality of

life and the ability to age safely and independently. It is well-known that elders and those

with disabilities prefer to remain in their homes and live autonomously (Bayer & Harper,

2000; Tang & Venables, 2000). Research has shown that the provision of adaptive

equipment and home modifications may allow elders to perform self-care tasks at close to

their highest ability and decrease the need for personal assistance (Gitlin et al., 2006).

The use of adaptive equipment and home modifications that target environmental

contributors to disability and functional decline have been shown to compensate for

declining abilities for elders (Kraskowsky & Finlayson, 2001). Although there is no










single approach that can address all functional limitations, numerous studies have shown

the positive effect of adaptive equipment and home modifications when focused on areas

that therapists and elders together identify as problematic (Cumming et al., 1999; Gitlin

et al., 2006; Hoenig, Taylor, & Sloan, 2003; Mann et al., 1999; Tinker & Lansley, 2005;

Verbrugge, Rennert, & Madans, 1997). An increasing percentage of elderly manage their

ADL difficulties with the use of adaptive equipment, especially in the areas of bathing,

toileting and mobility (Spillman, 2004). Environmental modifications, such as the

addition of grab bars in the bathroom, increase safety and decrease the risk of falls.

Assistive devices and environmental modifications have been found to help conserve

energy and time, and provide a sense of security (Kraskowsky & Finlayson, 2001; Tinker

& Lansley, 2005). Moreover, the use of adaptive equipment and environmental

modifications enable elders to remain in their own homes longer.

Functional difficulties within the home environment deserve attention from the

medical community (Gitlin et al., 2006). Rehabilitation specialists, such as occupational

therapists (OT), recognize the importance of ameliorating functional difficulties that may

result from a mismatch between the elderly person and their home environment, resulting

in the risk of accidents, such as falls. Functional difficulties serve as eligibility criteria

for home-based OT services, yet such services are seldom provided unless an acute

medical episode or hospital stay triggers a referral. Additionally, services are often short-

term and focus on acute care goals in lieu of the long-term needs imposed by chronic

illnesses. Such issues challenge rehabilitation efforts and increase individuals' health

risks and access to healthcare services (Demiris et al., 2004). Improvements in quality of

care should be aimed at an elders' desire to remain independent and live at home, as well










as control healthcare costs. By focusing attention on chronic conditions, functional

limitations, and access to healthcare services, sizeable improvements in the quality of

care should be achievable (Bodenheimer et al., 2002a; IOM, 2001).

Access to healthcare services

Elders with chronic illnesses and disabilities strain healthcare resources and

healthcare providers. This economic strain and profit-driven healthcare systems have

lead to cost containment efforts, limiting access to services and compromising quality of

care. The majority of elderly patients with chronic illnesses present with difficulties

accessing care in a timely manner which increases their risk for disabilities

(Bodenheimer, Wagner, & Grumbach, 2002b; PFS, 2004). Access to healthcare may also

be due to the problems of transportation and distance, as well as understaffed clinics and

rehabilitation facilities. The Institute of Medicine (IOM) defines quality of care as being

contingent on access to healthcare in a timely and equitable manner (Hawkins &

Rosenbaum, 2005; IOM, 2001). The failure to receive timely and ongoing care for

chronic conditions can lead to serious health consequences and result in higher healthcare

expenditures.

Numerous factors exist which limit access to healthcare. Many elderly reside in

rural communities with limited availability of adult specialty services, such as psychiatry,

neurology, comprehensive wound care, and rehabilitation. Rural areas frequently require

long-distance travel by patients and their home healthcare providers. A recent study

determined that the health of individuals who live in rural areas is worse than those who

live elsewhere, even after adjusting for socioeconomic factors (Weeks et al., 2004).

Barriers to healthcare for rural-dwelling patients include geographic isolation, functional









isolation, economic barriers, a scarcity of health professions, or a combination of these

factors.

Evidence has shown that responding to a patient's needs in a timely fashion can

improve the management and quality of their care (Balas et al., 2000; Goldsmith, 2000).

Timely and equitable access to care may require that we view the delivery of healthcare

in a different way. One method to increase availability of specialty healthcare services

and provide timely access to healthcare is to expand the capacity of healthcare centers

through the use of information technology.

Information technology

Information technology (IT) uses technology applications to manage and process

information. Historically, the healthcare sector has used IT for administrative tasks, such

as billing and inventory, but its use in the area of clinical care has been limited. IT can

play a critical role in the effective and efficient delivery of clinical care. IT allows

healthcare providers to systematically gather, process, analyze, communicate, and

manage patients and patient data (Kelley, Moy, Stryer, Burstin, & Clancy, 2005).

Telecommunication is defined as the use of technology to transfer information over

a distance. Telecommunication has been used to quickly provide essential patient data to

healthcare providers at a distance. Both patients and healthcare providers benefit through

the use of telecommunications for immediate access to automated clinical information,

diagnostic tests, and treatment results. Telecommunication has also been used to assist

healthcare specialists in educating and training new practitioners who may be in a

different room of a building, a different state, or even a different country.











Table 2-1. Health-related applications for information technology*
Health-related Areas Applications for Information Technology
Financial & Enrollment of patients
Admini strative Scheduling of appointments
Billing for services
Payment of providers
Clinical Care Access to information for diagnoses
Care delivery
Reminders and alerts (re: vaccines, etc.)
Video-based medical consultation
Consultation with specialists
Patient monitoring: in-home (monitoring vital signs, etc.)
Disease Management
Patient education
Transfer of medical records/images
Professional Education Medical literature searches
Accessing reference material
Distance education
Consultations
Credentialing
Consumer Information & Online searches for health information
Health Searches for doctors or health plans
Health insurance benefits information
Participation in support and chat groups
Self-monitoring
Access to personal health records
Purchase products
Medical consults (2nd opinions)
Email between patient and provider
Clinical trial information
Public Health & Incident reporting
Homeland Security Integration of data sources
Videoconferencing among public health officials
Surveillance (diseases or epidemics)
Delivery of health alerts
Response to bioterrorist attacks
Research Enrolling patients in trials
Collection of data
Collaboration with colleagues
Transfer of large data sets
Searches of large databases
Literature searches
Outcomes measurement
*Adapted from the National Research Council, 2005









Numerous IT applications are currently available for healthcare providers. Table 2-

1 provides a listing of health-related applications for IT (National Research Council,

[NRC], 2005a).

Benefits to the use oflIT

Despite the fact that learning to live with and manage a chronic disease or disabling

condition is an important aspect of aging, current medical care and health education does

not adequately address this issue (Nodhturft et al., 2000). IT can support self-

management of chronic illnesses through education and collaboration from healthcare

providers. IT has the potential to assist patients to learn the skills needed to manage

illness, making healthcare more patient-centered. Numerous IT applications are currently

being used to bring healthcare into the home and reduce the need for clinic care and

inpatient services. Whereas clinical instruction and intervention traditionally occur in

hospitals and clinics, a growing range of information technologies are being utilized,

including remote monitoring, interactive videoconferencing and web-based e-Health

applications. IT devices are increasingly employed to help gather, send and manage large

amounts of health information needed to assist both caregivers and patients in their self-

care efforts.

The ability to remotely and timely monitor patients' physiological parameters,

provide patient education and intervene quickly is essential for quality of care. Home-

based monitoring can provide healthcare providers with daily information about their

patient' s health, allowing for quick response to healthcare needs. Home-based

monitoring can provide patients with customized health education, access to providers,

and support for their healthcare needs. Potential advantages of using technology to

deliver patient education include its immediate availability, consistency of instructional









content, increased accessibility, a private learning environment within the home, and the

ease of reinforcement of learning (Dang, Ma, Nedd, Aguilar, & Roos, 2006; Lewis,

1999). Additionally, monitoring healthcare needs and trends over time allows healthcare

providers to determine the programs that are more effective and cost efficient. Home

monitoring by the healthcare team can detect and remediate functional and health

problems before they spiral out of control, improving access, effectiveness, and

efficiency of healthcare services. Although the potential for the use of IT in the

healthcare industry is tremendous, barriers continue to exist.

Barriers to the use of IT

Currently, the internet offers enormous potential to make the delivery of healthcare

more timely and patient centered. Yet many healthcare settings lack basic computer

systems or support the use of the internet for information or decision making (NRC,

2005a). To date, email is only used sporadically between patients and healthcare

providers, but the interest is growing. Moreover, clinicians and patients have varied

experience and comfort with IT and both may be wary of adopting the use of IT for

healthcare delivery.

A maj or impediment is both patients' and healthcare providers' concerns about

privacy and confidentiality of data. The U.S. has issued neither national standards

regarding the protection of health data, nor policies for the collection, storage and

processing of health data through communications technology. Although this is viewed as

a barrier to the use of IT, proponents of IT fear that enactment of stringent privacy rules

and regulations may impede the integration and success of IT applications addressed to

meet the quality needs of the current healthcare system (Detmer, 2000; DHHS, 2000).









IT proponents also believe expansion of IT for healthcare delivery is impeded by

reimbursement policies of the federal government and private insurers. Healthcare

payers, government and private, are reluctant to cover IT services as a part of health

insurance because of the uncertainty about efficacy and cost (Hersh et al., 2001). The

demands for immediate financial returns by private industry and sponsoring organizations

have precluded large-scale and long-term coordinated research efforts (Krupinski et al.,

2002).

The IOM (2001) reports that a challenging barrier to the establishment of IT

applications in healthcare "relates to human factors" [pg. 174]. Widespread adoption to

the use of IT for healthcare delivery may require behavioral adaptations on the part of

both the patient and the healthcare provider. Many of the concerns voiced by both

clinicians and patients focus on the loss of face-to-face interactions and the demise of the

patient-clinician relationship. Mangusson and Hanson (2003) view the debate as a moral

and social one, stating that analysis should qualitatively evaluate complex issues relating

to quality of life, as well as job satisfaction for the healthcare professions. Research has

shown positive views from patients and their healthcare providers. Hebert and Korabek

(2004) determined through focus groups and interviews that patients were positive about

the potential of technology to support their independence, increase self-control over their

care, and provide access to services. Nurses in their study felt improved outcomes would

result from the provision of disease-management education, and frequent monitoring and

timely interventions would result in health improvements. Physicians were more reticent

about the reliability and accuracy of the technology for assessing patients, and were

concerned about reimbursement, liability and training (Hebert & Korabek, 2004).









A further impediment to the use of IT in healthcare is the paucity of reliable

information on the costs and benefits. The idea that the use of IT can improve care and

lower costs through fewer office visits and timely medical interventions has yet to be

fully tested in rigorous settings. Before IT can become widespread in healthcare,

research on technologies and the evaluation of health applications must be achieved.

Although funding has been limited for large scale studies, "IT demonstration proj ects can

serve as venues for continued identification of technology needs" [page 257] and develop

standards for the provision of healthcare services (NRC, 2005b).

Telehealth Applications

Telehealth, an approach that connects individuals with their healthcare providers

through the use of telecommunications technology, addresses many of the above-

mentioned aims. The 2001 Report to Congress on Telehealth defines telehealth as the

"use of electronic information and telecommunications technologies to support long-

distance clinical health care, patient and professional health-related education, public

health, and health administration" (OAT, 2002) [page 1]. Specialized medical devices,

video-conferencing, computer networking, and software management systems allow for

the evaluation, diagnosis and treatment of patients in locations such as their homes.

Medical applications of telehealth are numerous. The main obj ectives include:

* More equitable distribution of healthcare through increased access to services for
individuals with disabilities and others for whom access is difficult (i.e. rural
areas).

* Removing the barriers of distance, time and travel from healthcare.

* Cost effectiveness by avoiding unnecessary emergency room visits and
hospitalizations.

* Preventative medicine and early intervention of medical complications that might
otherwise go unreported.










* Better diagnostic and prognostic capabilities, as patients submit vital health
information daily, allowing for tracking of trends.

* A holistic team approach, which may comprise physicians, nurses, therapists,
psychologists, and social workers.

* Patient-centered treatment and increased patient compliance, as patients are more
aware of their vital parameters (blood pressure, blood glucose levels, body weight
and temperature) and able to become actively involved in the process of managing
their care and treatment interventions.

* Promotion of independence through maintenance of life at home; enhanced quality
of life through prevention of chronic illnesses (Celler et al., 2003; Hersh et al.,
2001).

Telehomecare

Home-based telehealth applications, or telehomecare, represent a special

application of growing significance. One of the central driving forces for telehomecare is

the elderly patient's wish to remain safely at home for as long as possible. The home,

therefore, is becoming an increasingly important location for "care and cure" (Tang &

Venables, 2000) [pg 8]. Using telehealth technology, home-based video visits and

monitoring of vital signs can be accomplished electronically, medication compliance can

be verified, and patient education can be enhanced (Finkelstein et al., 2004).

Through telehomecare, remote health devices can record and transmit vital

information such as blood pressure, blood glucose levels and electrocardiograms from

home-based clients (Nakamura, Takamo, & Akao, 1999; Tsang et al., 2001). Home

monitoring can link patients to clinics, physician offices, disease management companies,

and home care agencies for the purpose of streamlining care delivery, maintaining a

closer patient connection, and monitoring early changes in patient status (Field &

Grigsby, 2002; Frantz, Colgan, Palmer, & Ledgerwood, 2002). Monitoring devices

typically incorporate alert systems that allow for rapid detection and treatment of early










signs and symptoms of instability. Home health devices often provide the patient with

the education necessary for disease-management and long-term compliance. As patients

are responsible for ensuring that accurate information is submitted, telehomecare requires

that patients assume much greater roles in the treatment and care of their chronic illnesses

(Holman & Lorig, 2000). Although telehomecare has the potential to assist elders in the

self-management of their chronic illnesses, and in turn reduce healthcare costs,

randomized controlled trials to test this proposal are lacking.

Telehomecare research studies

As referrals for home health services continue to escalate, healthcare organizations

are encouraged to seek more effective methods for providing patient care and saving

costs. In a landmark study of home health services by Kaiser Permanente Medical

Center, positive health outcomes were reported in terms of quality, patient satisfaction,

and cost savings (Johnston, Wheeler, Deuser, & Sousa, 2000). More than 22 percent of

Kaiser' s enrollees have diagnosed chronic illnesses, and generate 47 percent of the

emergency room visits and approximately 75 percent of the non-obstetric hospital bed

days of care (Bodenheimer et al., 2002a). Kaiser randomized 212 patients into control

and intervention groups, each receiving routine home healthcare (home visits and

telephone contact). In addition, the intervention group was provided with access to a

remote video system, which allowed nurses and patients to interact in real time, and

provided peripheral equipment for assessing vital health information. Remote video

technology in the home healthcare setting was shown to be effective and well received by

patients. Following the 18 month observational study, total cost savings of approximately

$900 per patient in the intervention group was reported, when controlling for equipment









costs and depreciation. Based on these findings, Kaiser Permanente is now integrating

telehomecare services within its organization (Johnston et al., 2000).

Additional smaller studies have compared conventional home healthcare services

with the use of home-based telecommunications equipment for remote monitoring.

Nakamura (1999) evaluated the effect of home healthcare compared to home healthcare

with the addition of a videophone. The videophone allowed patients to receive remote

medical assessments and consultation regarding health problems, ADLs, physical

exercise and nutrition, as well as emotional support for patients and caregivers. Patients

and providers responded to questionnaires at the end of the study, which determined a

potential benefit in the use of the videophone in terms of improving communication and

offering better assistance. As has been noted in numerous other studies (Hebert &

Korabek, 2004; Magnusson & Hanson, 2003; Nelson, Citarelli, Cook, & Shaw, 2003;

Williams, May, & Esmail, 2001), both the participants and the home health professionals

felt that services via videophone could supplement but not replace all face-to-face

healthcare visits.

Many telehomecare applications focus on specific healthcare needs, such as

individuals with congestive heart failure (CHF). CHF is one of the most common causes

of hospitalization due to exacerbation of a chronic condition among adults aged 65 years

and older in the U.S. (Scalvini et al., 2004). Through a randomized controlled trial

(RCT), the U. S. Department of Commerce is examining the benefits of using low-cost

telecommunications and monitoring technologies for homebound frail elders needing

skilled home healthcare (Demiris et al., 2001; Finkelstein et al., 2004). The study is

focusing on elders with CHF, chronic obstructive pulmonary disease (COPD), and









chronic wound-care, but final results have yet to be published. Outcome measures will

evaluate mortality and morbidity, length of time to transfer to a higher level of care (e.g.,

hospitalization or long-term care facility), subj ect perception of telehomecare, subject

satisfaction with care and technology, quality and clinical usefulness of virtual visits,

utilization of services, and costs for both subj ects and service providers. At this point in

time, initial information from the study has shown that elderly patients can use the

technology successfully, are satisfied with the care they receive, are confident in handling

the technology, and are accepting of the underlying concept of telehomecare. Roglieri

and colleagues presented a multicenter, longitudinal comparison of a comprehensive

CHF disease management program focused on patients with pure CHF and CHF-related

diagnoses. The impact of telemonitoring of CHF patients and post-hospitalization

follow-up in a managed care setting was evaluated. The researchers report significant

cost savings for participants based on reduced hospital admissions and readmission rates,

length of stay, and emergency room utilization (Roglieri et al., 1997). Dimmick et al.

(2003) discussed the establishment of a CHF disease management telehomecare program

as part of an integrated telehealth network that linked three hospitals, a federally qualified

healthcare clinic with six sites, a county dental clinic, and patients from nine different

counties and two states. In lieu of providing specific information regarding this CHF

program, the authors analyzed labor and equipment costs and estimated cost savings on a

national scale, proj ecting that the national costs of care for CHF hospitalizations could be

reduced from $8 billion to $4.2 billion annually. The University of California at Davis

Hospital (UCDH) studied 3 groups of individuals with the diagnosis of CHF (Jerant,

Azari, & Nesbitt, 2001). All groups were provided with standard healthcare, a second










group also received a weekly telephone call, and the third group was provided with a

videophone and remote health monitoring equipment. Differences were not detected

between the telephone and telehomecare groups, but trends were seen toward fewer CHF

related and all-cause readmissions, and shorter mean length of stay in both the telephone

and the telehomecare intervention groups compared to standard care. Although the

researchers discussed charges as primary and secondary outcomes, true cost Eigures were

not provided. This study is one that questions whether more expensive telehomecare

programs offer any incremental benefit beyond telephone follow-up.

Diabetes is another significant chronic illness, which is costly and common in the

elderly. The high prevalence and complexity of diabetes poses major clinical challenges

which may be attenuated by telehomecare (Shea et al., 2002). An ongoing RCT is

Columbia University's Informatics for Diabetes Education and Telemedicine (IDEATel)

Project. IDEATel is a four-year demonstration project funded by the Centers for

Medicare and Medicaid Services (Shea et al., 2002). A total of 1,500 participants have

been randomized to a telehomecare intervention (n=750) and a control group receiving

standard care (n=750). IDEATel is a large, complex project designed to provide data

relevant to policy formation for the use of telehomecare in diabetic management.

Outcomes from this study should provide significant information regarding the use of

telehomecare for the management of diabetes in the elderly population. Integrated

telehealth networks have been designed to assist diabetic participants manage their care

through telehomecare support systems (Dimmick et al., 2003; Shea et al., 2002). In

Dimmick et al., participants were given a blood glucose monitor that used telephone lines

to transmit values to a health clinic. Although their sample size was small (N=36),









researchers reported progress in achieving better blood sugar control by participants. The

researchers felt that a key outcome in this demonstration proj ect was the ability to

provide support and incremental education over time so that participants learned to

manage their chronic health problems. The TeleHomecare Proj ect is a partnership

between the Pennsylvania State University, the Visiting Nurses Association (VNA) of

greater Philadelphia, and the American Telecare, Inc. (ATI) (Dansky, Palmer, Shea, &

Bowles, 2001). The TeleHomecare Proj ect was designed to test the effects of

telehomecare on quality and financial costs associated with care for elderly diabetic

patients. All costs were examined, both direct and indirect, but the focus was costs

occurring at the home health agency level. Researchers provide a specific cost

breakdown for each component of the telehomecare services, including home visits,

video visits, training and meeting time, and equipment. Reported outcomes focused on

the percent of patients who were discharged from home healthcare (64 percent of the

telehomecare group compared to 3 9 percent of the control group) and the percent of

patients who were readmitted to the hospital (10 percent telehomecare group compared to

28 percent control group), yet they do not report on cost savings or the findings from the

health-related quality of life measures that were used.

Telerehabilitation

Another application of telehealth is Telerehabilitation. Telerehabilitation is an

emerging practice that uses specialized communications technology for the remote

delivery of care to patients with rehabilitation needs. Telerehabilitation has the potential

to manage multiple components of health, including functional independence, self-care

and self-management of illness (Halamandaris, 2004a). The focus of telerehabilitation is









to increase access to rehabilitation services, and to allow individuals to remain safe and

independent in their homes.

Over 50 million Americans today live with a functional impairment, often

combined with a chronic illness, which impacts their ability to perform basic and

instrumental activities of daily living (CDC, 2003b). Although most do not receive

specialized therapy, millions require some sort of therapeutic intervention. Typically

these rehabilitative interventions are supplied through inpatient care, skilled nursing

facilities, outpatient clinics, or home health visits. Unfortunately, as healthcare delivery

is restructured in the U.S. due in part to financial considerations, rehabilitation

entitlements are being reduced, resulting in shortened lengths of stay in acute and

subacute care settings. With earlier discharges, there is an increased need to deliver

services to patients in their homes in a comprehensive yet efficient and cost effective

manner. Researchers have expressed confidence in the "idea" of applying IT for the

remote delivery of medical rehabilitation services and support for independent living;

they are sure that the potential is great (Burns et al., 1998; Kinsella, 1999; Rosen, 2004;

Schopp, Hales, Brown, & Quetsch, 2003; Winters, 2002; Winters & Winters, 2004).

Telerehabilitation research studies

The ability to remotely assess and monitor physical outcomes is an important area

in telerehabilitation. Telerehabilitation has been used successfully for administration of

standardized assessment tools (N. C. Dreyer, K. A. Dreyer, Shaw, & Wittman, 2001;

Hauber & Jones, 2002; Russell, Jull, & Wootton, 2003b; Savard, Borstad, Tkachuck,

Lauderdale, & Conroy, 2003) suggesting this is an accurate and reliable method of

performing physical and cognitive assessments. Additionally, televideo technology may









also have potential for providing cost-effective in-home assessments for home

modification services prior to a patient's discharge (Sanford, Jones, Daviou, Grogg, &

Butterfield, 2004). Findings from this small study suggest that remote telerehabilitation

assessments have the potential to enable specialists to diagnose potential accessibility

problems in home environments and prescribe appropriate modifications regardless of the

location of the client, home, or specialist.

Managing the health complications of disability is costly. A number of studies in a

variety of care settings illustrate the ability to provide clinical care through

telerehabilitation. Russell and colleagues used an Internet-based system in a replicated

home environment within a clinical setting to provide rehabilitation to patients who had

undergone total knee arthroplasty. Treatment for both the control and intervention

groups included therapist guided stretching and mobilizations, a tailored exercise

program and education. Treatment outcomes for the telerehabilitation group were

comparable to the control group. Following the treatment intervention, patients were

surveyed and reported high ratings for satisfaction of the telerehabilitation program, and

ease of use of the technology (Russell, Buttrum, Wootton, & Jull, 2003a).

Telerehabilitation may provide a way to improve care and to continue patient

education following discharge from a hospital or inpatient setting. In a quasi-

experimental study, 35 spinal cord injury (SCI) patients were recruited for a

telerehabilitation intervention in the prevention of pressure ulcers (Phillips, Temkin,

Vesmarovich, Burns, & Idleman, 1999). Pressure sores have been identified as one of the

most common problems for SCI patients, and are also a serious problem for the elderly.

Pressure ulcers can lead to expensive and dangerous complications, and treatment often










requires that patients be hospitalized (Vesmarovich, Walker, Hauber, Temkin, & Burns,

1999). The study's main objectives were to determine which of three approaches to care

(videophone, telephone, standard care) produced the lowest incidence of pressure ulcers,

promoted the most effective care of sores that did develop, and lead to the fewest

hospitalizations in newly injured patients with SCI after discharge. Phillips and

colleagues reported that the telerehabilitation intervention was effective in ulcer tracking

and management of all ulcer occurrences. Interestingly, the video group reported the

greatest number of pressure ulcers, but the investigators felt that visual contact with the

nurse in the video group may have attributed to more ulcers actually being identified and

reported.

A large client base for rehabilitation includes adults with stroke and traumatic brain

injury (TBI), yet few telerehabilitation studies have focused on these populations. Savard

and colleagues reported on two clinical programs that used videoconferencing to provide

rehabilitation consultation to individuals with neurologic diagnoses living in remote areas

(Savard et al., 2003). The Minnesota Telerehabilitation Initiative serves patients and

clinicians in rural Minnesota. The Pacific Rim Initiative serves patients and clinicians on

the island of American Samoa. Both service areas have a scarcity of rehabilitation

clinicians. Both programs used a two-monitor system for continuous presence

videoconferencing between the patient in their home and the rehabilitation specialist in

the clinic. Their patient population included elderly individuals with diagnoses of TBI,

stroke, and Parkinson's disease. All patients reported satisfaction with the project, 23

patients had positive clinical outcomes, and average mileage saved was 150 miles one

way. Two cases studies were presented. As these studies were descriptive in nature, the










authors were unable to provide more than recommendations to others considering the

provision of telerehabilitation services.

Telerehabilitation may be a way to extend post-acute stroke care into a non-clinical

setting, such as the community. Telerehabilitation allows providers to monitor patients'

progress, identify areas in need of improvement, and ultimately improve function and

decrease long-term disability and costs. A recent community-based study presented a

model for providing telerehabilitation for stroke patients using videoconferencing (Lai,

Woo, Hui, & Chan, 2004). Twenty-one stroke patients attended an 8-week intervention

program at a community center for seniors. The intervention used a videoconference link

and provided education, exercise, and psychosocial support for 1.5 hours at one session

per week. Significant improvements were noted in balance, stroke knowledge, self-

esteem, and health-related quality of life. Advantages of community-based

telerehabilitation includes ease of access, enhanced learning and applying knowledge in a

group atmosphere, increased social support, and allowance of real-time interaction

between participants and the medical professionals. The authors recommend that future

studies consider investigating the length, duration and frequency of the intervention, as

results may improve with more intense exercise and additional education.

The development of telerehabilitation multi-center teams may make it possible to

conduct, analyze and publish more extensive research results in the area of

telerehabilitation. In 1997, NIDRR issued a set of proposed priorities for a new

Rehabilitation Engineering Research Center (RERC) on Telerehabilitation. NIDRR's

main motivation was to explore methods to eliminate the barrier of distance in the

delivery of comprehensive rehabilitation services. As well, the INTTEGRIS Jim Thorpe









Rehabilitation Center has teamed up with a group of researchers, clinicians, engineers,

and administrators to create the Collaborative Alliance for Research in Telerehabilitation

(CART). CART's goal is to create a large database of telerehabilitation studies through

aligning standardized instruments for data gathering and developing a framework for

collection of data across multiple institutions. CART argues that the development of a

model database linking the delivery of telerehabilitation services, reimbursement, and

outcome evaluation is critical to meeting the challenge for long-term sustainability of

telerehabilitation (Kaur, Forducey, & Glueckauf, 2004).

The current literature also provides educational articles that define the basic

operations of a telerehabilitation program (Winters & Winters, 2004), emerging

opportunities in telerehabilitation (Winters, 2002), advantages and disadvantages of

telerehabilitation (Torsney, 2003), and important components to consider when designing

a telerehabilitation program (Schopp, Hales, Quetsch, Hauan, & Brown, 2004). Winters

(2002) reports that one of the apparent reasons telerehabilitation isn't thriving may be

because there is not one optimal protocol for rehabilitation. Different problems require

different technologies and procedures. Based on these reports, the development of a

conceptual framework may be needed to provide a foundation for clinical research in

telerehabilitation.

Telehealth applications within the Veterans Health Administration

The Veterans Health Administration (VHA) provided medical care to

approximately 5.3 million veterans in 2005 (OPA, 2006). A significant portion of this

medical care is provided for the management of chronic illnesses which are especially

prevalent amongst the aging veteran population in comparison to their community

counterparts (Asch et al., 2004; Kazis et al., 2004b; Rogers et al., 2004; Yu et al., 2003a).









Additionally, when compared to the general U.S. population, veteran enrollees tend to be

poorer and more likely to live alone (Stineman et al., 2001). Living alone may increase

healthcare utilization due to lack of available support at home, inability to rely on others

for assistance, or lack of support for basic and instrumental activities of daily living

(Guzman, Sohn, & Harada, 2004). Prior studies have found living alone to be an

independent risk factor for morbidity and mortality (DHHS, 2004; Lund et al., 2002).

Furthermore, serious health or disabling conditions may lead to residence in a nursing

home due to the difficulties of home management. Each of these issues significantly

increases the healthcare challenge and places our veterans at risk for healthcare crises.

Cost effective and efficient approaches that foster the well-being and independence of our

veteran enrollees must be explored. Telehealth is viewed by many individuals within

VHA as an innovative means to increase access and improve healthcare for veterans

through telecommunications applications linking clinical care, education, and

administrative systems.

In October 1999, the Veterans Health Administration (VHA) published a notice

entitled, "Telemedicine Strategic Planning Document," which outlined a national

strategy for VHA telehealth and provided recommendations for the development,

evaluation and optimization of telehealth to improve healthcare for veterans (VHA,

1999). This planning document concluded the following:

* Telehealth has the potential to serve the healthcare needs of veterans by decreasing
the barriers of distance and time. In remote areas, travel distances represent a
significant barrier for veterans to access timely care.

* Telehealth has the potential to enhance care for veterans who may be isolated from
necessary care, and to augment healthcare services in home and community based
care locations.










*Telehealth must be more thoroughly evaluated to demonstrate the efficacy, safety,
reliability and outcomes of clinical Telehealth.

Despite over three decades of telehealth activities in different healthcare sectors,

few clinical studies in telehealth have comprehensively evaluated and documented such

outcomes. To address these strategic planning initiatives, in April 2000 the VHA

initiated funding of several clinical demonstration proj ects to test the integration of care

coordination with communications technology for disease management (Meyer et al.,

2002). Numerous publications have resulted from this initiative, but few VHS telehealth

programs have existed long enough to provide convincing cost effectiveness results.

Telehealth research studies within the VHA

The use of technology to improve health behaviors and self-management in the

veteran population and reduce the risk of early institutionalization is a focus of telehealth

within the VHA. The Rural Home Care Proj ect (RHCP) was one of eight clinical

demonstration projects within this original initiative (Kobb et al., 2003). A prospective,

quasi-experimental design with period data collection at 6-month intervals was used in

one of the initial studies. The population of interest included veterans with multiple co-

morbidities who were high-cost medical users. The authors report that the intervention

group showed greater improvement in healthcare resource consumption than the usual

care group when comparing 6-month pre- to 6-month post-enrollment data. Patient and

provider satisfaction was also reportedly high. This VHA telehealth initiative included a

multi-site study, which analyzed healthcare utilization and clinical impact. Three

telehomecare demonstration proj ects from Ft. Myers, Lake City, and Miami, Florida were

included (Cherry, Dryden, Kobb, Hilsen, & Nedd, 2003). All participants (n=345) were

elderly, had multiple chronic diseases (specifically CHF, coronary artery disease,









diabetes, hypertension, and COPD), and were high cost users of the VHA within the

previous year (> $25,000). Home-based monitoring equipment allowed for daily

responses to be categorized and risk prioritized to alert the care coordinators at each of

the VA hospitals of the most serious outcomes first. Care coordinators contacted

veterans by telephone based on the seriousness of the alerts. The intervention group was

compared to themselves at 6 months pre- and 6 months post-enrollment. The authors

report reductions in inpatient admissions, emergency room encounters, and hospital bed

days of care, as well as improvements in medical compliance.

The VA Connecticut Healthcare System used telehomecare, integrated with the

VA' s electronic medical record system, to determine whether telehomecare could reduce

healthcare costs and improve quality of life outcomes relative to standard care for

chronically ill and frail elderly veterans (Noel et al., 2004). Home telecommunication

units allowed for peripheral devices to monitor vital signs and provided a questionnaire to

evaluate quality of life. Data was transmitted over telephone lines directly into the

facility's electronic database. In comparison to the randomized control group, at six

months the telehomecare group showed a significant decrease in costs in hospital bed

days of care and emergency room visits, as well as a decrease in blood glucose levels.

Functional level and patient-rated health status did not show a significant difference for

either group at any period in time during the study.

Most of the telehealth studies within the VHA focus on healthcare costs and

utilization, and little is known about the impact on physical and cognitive functioning. A

case-control design study determined a causal relationship between the use of

telehomecare and care coordination and improvements in functional and cognitive status










(Chumbler, Mann, Wu, Schmid, & Kobb, 2004). The investigators examined changes

over a 12-month period and analyzed the before-after improvements in functional health

and cognitive outcomes using the Functional Independence Measure and the Mini Mental

Status Examination. The telehomecare group had significant improvements in all

outcome measures over the 12 months.

Results from an effectiveness study of a care coordination telehomecare program

for veterans with diabetes determined that after two years of enrollment, a statistically

significant reduction in hospitalizations was observed in the treatment group (T. E.

Barnett et al., 2006). An interesting phenomenon with many of the VHA telehealth

programs is the increase in care-coordinator initiated primary care clinic visits following

enrollment (Chumbler et al., 2005). This increase in newly scheduled clinic visits is

congruent with daily monitoring and the necessity to intervene quickly before a

hospitalization is required. In lieu of observing healthcare utilization at 12-months post-

enrollment, Barnett et al. observed outcomes at 24 months following implementation and

noted a reduction in care-coordinator initiated clinic visits.

Summary

As the chronically ill and disabled elderly populations become ever larger, there is

greater urgency to find ways to provide efficient, cost-effective care, as well as improve

functional performance and quality of life (Cruise & Lee, 2005). In an attempt to address

this need, the provision of healthcare services has shifted from inpatient and outpatient

settings to the home as the site of care. Allowing patients to remain within their home

environments and still have direct communication with their healthcare providers

increases access and quality of care, and may in turn reduce healthcare expenditures.

Recent advances in information technology allow for the provision of such care to










patients in their homes through telehealth applications. Telehealth may provide the

means, yet significant research questions remain.

A number of studies in a variety of care settings illustrate the promise of telehealth,

but little systematic and controlled research has occurred to date. Based on the available

literature, it appears that telehealth programs have yet to provide compelling obj ective

documentation of successful outcomes. Because of serious limitations in experimental

design, these studies are hindered by small sample sizes, short durations, and other

methodological flaws. Moreover, few studies provide actual evidence that the

interventions have resulted in clinical outcomes comparable to or better than the gold

standard, conventional face-to-face care, although the technology and the technique

seems to show promise in certain areas (Frantz et al., 2002). The overall methodology,

quality of the evaluative studies, and small sample sizes that limit statistical power

precludes producing convincing scientific results. These outcome studies have

demonstrated inconclusive medical and functional improvements and cost savings, and

result in the lack of evidence-based guidelines that are imperative for the implementation

of telehealth programs (Palsbo & Bauer, 2000; Whitten & Kuwahara, 2003). Such

evidence-based results are essential to add to the scientific knowledge base and ensure

acceptance in the professional community.

The next generation of studies needs to advance beyond efforts to replicate these

earlier studies. Although large-scale randomized trials are important before one can

argue convincingly that the medical, psychosocial, functional, and fiscal outcomes of

telehealth are positive, comprehensive studies evaluating current telehealth models are






48


important and will serve as a standard for the methodology of future telehealth

applications.















CHAPTER 3
HEALTH RELATED COST ANALYSIS

The Veteran's Health Administration (VHA) has experienced unprecedented

growth in the healthcare system workload over the past few years. During the last six

years, the VHA has provided more medical services to more veterans and family

members than at any time during VHA' s history (OPA, 2006). The number of veteran

enrollees receiving medical services within the VHA increased by 22 percent from 2001

to 2005. Many veteran enrollees today are elderly, chronically ill and disabled. Chronic

illnesses account for a disproportionate amount of healthcare utilization and costs within

the VHA (Yu et al., 2003a). Based on a recent study, data indicates that 72 percent of the

VHA patients have one or more chronic illnesses, and these patients account for 96.5

percent of the total VHA healthcare costs (Yu et al., 2004). Overcoming these challenges

is a maj or barrier facing the VHA and healthcare in general today. It has been proposed

that telehealth can help meet these challenges (American Telemedicine Association

[ATA], 2003; Bashshur, 2001; Brantley, Laney-Cummings, & Spivack, 2004; Cherry et

al., 2003; Hibbert et al., 2004; Krupinski et al., 2002; Liss, Glueckauf, & Ecklund-

Johnson, 2002; MacDonald-Rencz, Cradduck, & Parker-Taillon, 2004; OAT, 2002).

Telehealth used as a part of a coordinated, comprehensive care program has demonstrated

the ability to assist with the management of chronic conditions and reduce healthcare

costs.

Telehealth is a specific clinical application of monitoring patients in their homes

from a central station usually located at a hospital. Telehealth is viewed by the VHA as









one of the more innovative advanced telecommunication applications. Telehealth has the

potential to link clinical care, education, fiscal, and administrative systems to improve

veteran's healthcare, while at the same time increase veteran's access to care. The

premise is that improvements in healthcare services and reductions in healthcare costs

can be effected by establishing a continuum of patient care from the patient's home to

service providers in the healthcare sector.

Clinical effectiveness as well as the educational benefits of telehealth have been

presented in the literature (Gamble, Savage, & Icenogle, 2004; Grigsby & Sanders, 1998;

Taylor, 1998). Healthcare cost savings have been demonstrated in numerous telehealth

studies (Bynum, Irwin, Cranford, & Denny, 2003; Finkelstein et al., 2006; Hooper et al.,

2001; Joseph, 2006; Noel et al., 2004). In a randomized controlled trial, Finkelstein and

colleagues demonstrated that telehealth visits between a skilled home healthcare nurse

and chronically ill patients at home using videoconferencing technology improved patient

self-care activities and lowered costs when compared to traditional face-to-face home

healthcare visits. Nakamara focused on activities of daily living (ADLs) in his

effectiveness study, and determined that there was not only a reduction in healthcare

costs, but also significant improvement in ADLs, communication and social participation

for participants in a telehealth intervention when compared to a control group receiving

traditional care (Nakamura et al., 1999). Noel and colleagues (2004) determined that a

home telehealth system which monitors vital signs and provides patient questionnaires

reduced cost and improved quality of life outcomes for elderly patients with complex

comorbidities In a recent report on home telehealth for diabetic patients, Dansky

showed that monitoring patients in their homes contributed substantial overall cost









savings despite the additional expenses associated with the technology (Dansky et al.,

2001). Meystre concluded following a literature review on the state of telehealth, that

long-term disease monitoring of patients at home is the most promising application for

technology for delivering cost effective quality care (Meystre, 2005). The use of

technology combined with a chronic care model has the potential to reduce healthcare

costs and lower use of healthcare services, as well as improve the management of chronic

illnesses (Bodenheimer et al., 2002a; Liss et al., 2002).

In contrast, critical reviews of the cost-effectiveness and cost-benefit of telehealth

report that current research has methodological and analytical weaknesses, and that it is

premature to generalize about either the positive or negative effects of telehealth

applications (Gamble et al., 2004; Hakansson & Gavelin, 2000; Mair & Whitten, 2000).

There continues to be a call for studies measuring the cost-effectiveness of the

application of telehealth to specific clinical practices compared to conventional medical

care (Gamble et al., 2004; Ohinmaa & Hailey, 2002).

This chapter of the dissertation presents the health-related cost analyses between a

telerehabilitation program (LAMP) and a matched comparison group, a telehomecare

program (TCCP) and a matched comparison group, and a comparison between the

telerehabilitation and telehomecare program. Methods for obtaining the cost data and the

comparison groups are presented, as well as the results and discussion from the analyses.

Methods

Cost Data

The U. S. Department of Veterans Affairs (VA) uses the Decision Support System

(DSS) to track its healthcare system workload and determine the cost of patient care. The

National Data Extracts (NDEs) were created to assist VA researchers in accessing this









workload and cost information. The NDEs are extracted from DSS and report total actual

costs of every inpatient and outpatient encounter provided by the VA. NDEs include

information based on fiscal years and report costs that incurred from the beginning of a

Eiscal year up to the current month. VA fiscal years run from October 1 through

September 30.

There are three core NDE Hiles: inpatient discharge, inpatient treating specialty,

and outpatient files. The inpatient discharge files have one record for each hospital

discharge that occurred during the fiscal year. This file includes the entire cost for the

hospital stay, i.e., nursing care, pharmacy, and laboratory testing. The inpatient treating

specialty reports the type of bedsection unit where the care was provided, allowing for

nursing home bed days of care (BDOC) to be distinguished from hospital bed days of

care. The outpatient NDE files consist of one record for each unique clinic encounter,

defined as a clinic stop. Therefore, there is a separate record for each clinic the patient

visits, even if the patient visits multiple clinics in one day. Each record contains the total

cost of the encounter and information that identifies the patient, the location of the

service and the date the service occurred. Outpatient visits include the costs of laboratory

testing and ancillary services. Pharmacy records and associated costs are stored in

separate files.

The NDEs are SAS files stored at the VA Austin Automation Center (AAC). They

are accessed using SAS batch programs. To access the NDE files, an account was

established at the AAC in Austin, Texas. A "Time Sharing Request Form" as well as a

"Privacy Act Statement" was submitted in order to work with real Social Security

Numbers (SSNs) from a single Network (VISN 8) for this project only. This was









required as the local VHA facilities use real SSNs as the patient medical record number.

The medical center director from the Malcom Randall VAMC in Gainesville, Florida

granted approval to access real SSNs. To obtain NDE records for our study participants,

real SSNs were linked to encrypted SSNs included in the NDEs. All data from this point

on contained only data with encrypted SSNs.

Linking of the Treatment Groups to the Comparison Group Pool

Our matched comparison group was obtained from a database from the 1999

Veterans Large Health Study (LHS). The LHS was a national VA survey that established

baseline health status on approximately one million veterans. The LHS was based on a

random sample of all veteran enrollees in the nation.

A data use agreement was submitted to the Office for Quality Performance (OQP)

requesting use of the data for benchmarking of the SF-36 / SF-12 Health Related Quality

of Life Survey and comparison of VA health related costs (hospitalizations, clinic visits,

emergency room visits, nursing home BDOC). Following approval from OQP, a

compact disk was provided which contained encrypted SSNs, diagnoses, age, marital

status, education, and SF-36V scores of all veterans from VISN 8 that participated in the

1999 LHS. The database consisted of 75,715 veteran enrollees.

Cleaning of the database was required and initially included removing all

individuals with missing demographic and diagnostic data, leaving the pool with 65,844

veterans. Forty-eight veterans enrolled in LAMP or TCCP also participated in the 1999

LHS; therefore, they were removed from the LHS database so that they were not double

counted. As the LHS database was from a 1999 study, it was necessary to cross-

reference these individuals with individuals in VISN 8 who had received medical care

during FY 2005 (at least 1 clinic visit). This ensured that the veterans used for the









matched comparison group were alive and utilizing services during the full pre-post

periods. Individuals who died during the study period were not eligible for inclusion in

the study. This reduced our comparison group pool to 46,307. Next, individuals who

were not being treated in the North Florida/South Georgia Health Care System were

deleted from the database. This reduced the total pool to 10, 120. Lastly, the comparison

pool was cross-referenced with all enrollees in the CCCS database to ensure that no

veterans in the comparison group had ever participated in a VA telehealth program. This

reduced our total to 9918. From the 9918, 56 individuals were then deleted from the pool

due to unverifiable inpatient data, leaving 9862 individuals in our final comparison pool.

These 9862 patients comprised the control pool for subsequent matching to the treatment

groups. Figure 3-1 presents the initial linking procedure.

Reported long-term chronic diseases

The LHS database consisted of veterans who were enrolled in and receiving

healthcare through the VA at the time of the 1999 survey. Reported demographics and

disease states for the LHS veterans were obtained in 1999. To ensure comparability of

our treatment and comparison groups, inpatient and outpatient workload files with

reported primary and secondary diagnoses based on the International Classification of

Diseases, Ninth Revision, Clinical Modification (ICD-9) diagnostic codes were obtained

for LAMP and TCCP from 1997-99. Detailed clinical information, including diagnoses,

came from the VA National Patient Care Database (NPCD) healthcare

workload/encounter files, which includes the Patient Treatment Files (PTF) and the

outpatient files. PTF and outpatient files for fiscal years 1997, 1998, and 1999 were

explored in order to review diagnoses and ensure that each of the study arms was










Figure 3-1. Preparation of comparison pool for final matching to LAMP and TCCP.







56


1999 LHS Database for VISN 8 = 75,715 Veterans


SMissing Data = 9,871


Comparison Pool =
65,84
LAMP/TCCP LHS
participants = 48

Comparison Pool =
65,796
Cross reference with
I2005 outpatient data =
19,489
Comparison Pool =
46,307
NF/SG patients only =
S- 36,187

Comparison Pool =
10,120
Non-CCCS patients =
S- 202

Comparison Pool =
9918

Unverifiable patient data =
S- 56


Final Comparison Pool =
9862


Matched to LAMP = 115
based on age, marital
status, long-term
diagnoses, number of pre-
study inpatient bed days
of care


Matched to TCCP = 112
based on age, marital
status, long-term
diagnoses, number of
pre-study inpatient bed
days of care









receiving VA care and was diagnosed with their reported chronic illness between 1997-

99. All patients enrolled in LAMP and TCCP were identified in the 1997-99 PTF,

signifying they were receiving VA care during that time period. Only diagnoses reported

in the 1997-99 NPCD files were used for matching purposes. Therefore, VA healthcare

use and chronic illness diagnoses were consistent between our study arms. Chronic

illnesses used for matching purposes for our comparison group and our treatment groups

were diagnosed by 1999 or earlier.

Enrollment date

Specific enrollment dates were available for each member of the treatment group.

These specific dates were used as a baseline to determine health related costs 12-months

pre-enrollment and 12-months post-enrollment. Because our matched comparison group

was not actually enrolled in a program, this was not possible; therefore, an arbitrary

enrollment date was required for analysis purposes. To determine an arbitrary enrollment

date, frequency of enrollment for LAMP and TCCP was calculated from October 1, 2002

through September 30, 2004 (the study period). Eighty-nine percent of our treatment

group was enrolled between June 2003 and February 2004. Therefore, the median point

of October 1, 2003 was chosen as an appropriate enrollment date. For our comparison

group, FY 2003 served as the pre-enrollment period and FY 2004 served as the post-

enrollment period.

Inpatient bed days of care pre-enrollment

Inclusion criteria for enrollment in a VHA telehealth program includes previous use

of medical services, especially hospital BDOC, and was deemed an important variable for

matching purposes. Data on hospital BDOC 12 months pre-enrollment was obtained for

both treatment groups and the comparison pool. The NDE files report inpatient BDOC at









discharge. Our comparison pool was provided with an arbitrary enrollment date of

October 1, 2003 (the first day of FY 2004) in order to determine health related costs for

one-year pre and one-year post enrollment. Therefore, for our comparison pool, 12

months pre-enrollment extended from October 1, 2002 through September 30, 2003 (FY

2003). To determine pre-enrollment BDOC for inpatient stays that spanned more than

one fiscal year (i.e., stays with admission dates before October 1, 2002 or discharge dates

after September 30, 2003), total inpatient BDOC were allocated proportional to the

number of days that occurred within FY 2003.

Matching

The demographics of age and marital status, and diagnoses of arthritis,

hypertension, congestive heart failure, chronic lung disease, diabetes, and stroke, as well

as number of pre-enrollment BDOC were used for matching purposes. Initially, SAS

logistic regressions (stepwise) were run for both LAMP and TCCP to determine which

variables were significant to the treatment group at the p=.05 level Using this

methodology, in the LAMP treatment group the variables of chronic lung disease and

CHF dropped out of the model. Therefore, these diagnoses were not used for matching

purposes for the LAMP comparison group. For TCCP, the variables of chronic lung

disease and arthritis dropped out of the model and were not used for matching purposes

for the TCCP comparison group.

Matching was accomplished by creating a dummy string variable where the

elements of the character string represented the variables that remained in each of the

regression models. Initially, marital status and diagnoses were dichotomized (1=yes and

2=no), and age and inpatient BDOC remained continuous variables. During the matching

process, age was stratified to simplify the dummy variable. For LAMP, age stratification









was 1 = ages 49-57, 2 = ages 58-66, 3 = ages 67-75, 4 = ages 76-84 and 5 = ages 85-98,

which covered all age ranges. TCCP age stratification was 1 = ages 37-57, 2 = ages 58-

66, 3 = ages 67-75, 4 = ages 76-84 and 5 = ages 85-90, which covered all age ranges.

The number of pre-enrollment inpatient BDOC remained a continuous variable. Based

on this technique, a dummy string variable of 112121327 would represent a LAMP

participant who was married and diagnosed with arthritis, diabetes and hypertension,

within the age range of 67-75, and who had 27 inpatient BDOC pre-enrollment. Dummy

string variables were created for all study participants in LAMP and TCCP, as well as the

full comparison pool.

Using the dummy string variable, 76 percent of LAMP and 68 percent of TCCP

had direct matches with a patient from the comparison pool. Once the exact direct

matches were determined, the remaining were matched manually on age and pre-BDOC

and as many of the residual demographic variables as possible.

Telehealth vs. Standard Care

The type of healthcare delivery a patient received was also an independent variable

in thi s study. The three types of servi ce delivery include: VA tel erehabilitati on/care

coordination (LAMP), VA telehomecare/care coordination (TCCP), and VA standard

care. For the LAMP and TCCP cohorts, ongoing daily monitoring exists between the

care coordinator and the patient through various types of technology. Due to daily

monitoring, patients receive increased access to primary care, specialty or rehabilitative

care, and self-management support. In contrast, our matched comparison groups had

access to all VA healthcare services, with intermittent contact with their primary care

providers.










Study Design

A retrospective, matched comparison study design was implemented. The LAMP

(telerehabilitation) program included veterans with functional deficits and chronic

illnesses, who were at risk for multiple VA hospital and nursing home bed days of care.

Veterans were eligible for enrollment in LAMP if they presented with deficits in at least

two activities of daily living (ADLs), including mobility and transferring. Veterans

enrolled had to live at home, have electricity and phone service, and accept remote

monitoring technology into their homes. The TCCP (telehomecare) program included

veterans with chronic illnesses, who were at risk for multiple VA inpatient and outpatient

vi sits. Veterans were eligible for enrollment in TCCP if they were noninstituti onalized,

had a history of high healthcare costs and utilization, had electricity and phone service,

and accepted remote monitoring technology into their homes.

Both treatment and comparison groups received their healthcare from the North

Florida/South Georgia Healthcare System. Treatment and comparison groups were

matched on demographic variables of age and marital status, as well as primary

diagnoses, and number of 12-month pre-study bed days of care. All groups had to be

enrolled in the VA for the entire 24-month observation. Actual enrollment dates were

used for our treatment groups to determine pre-post costs. The arbitrary enrollment date

of October 1, 2003 was used for the comparison groups to determine pre-post healthcare

expenditures.

Although selection criteria was stringent for matching of the comparison groups,

the absence of randomization between the treatment and comparison groups may result in

selection bias. A difference-in-differences (DiD) approach was used to allow for the

control of any remaining differences between the treatment and comparison groups,









including the differences that may not be directly observed. Such unobserved differences

may influence both the treatment and comparison groups, as well as the estimated

treatment effect. The DiD method controls for selection bias through measuring the

treatment effect while accounting for any pretreatment differences between the groups.

This method has often been used in studies of labor economics, with applications

increasing in health services research (Tai-Seale, Freund, & LoSasso, 2001; Wagner,

Hibbard, Greenlick, & Kunkel, 2001), as well as telehealth studies (T. E. Barnett et al.,

2006; Chumbler et al., 2005). The concept of DiD observes the treatment and control

group before and after the intervention. Prior to the intervention, intrinsic differences

between the groups are measured. Following the intervention, the treatment effect plus

the intrinsic differences between the groups are measured. The treatment itself is then

calculated by subtracting the intrinsic difference between the two groups pre-intervention

from the combined treatment effect plus intrinsic difference post-intervention. Therefore,

we are measuring the difference between the differences to obtain the treatment effect.

Statistical Analysis

The dependent variables used in this study were healthcare expenditures defined as

costs incurred by the VHA for inpatient BDOC (hospitalizations), outpatient clinic visits,

emergency room visits (ER), and nursing home care unit (NHCU) BDOC. Costs

presented exclude costs of contract medical services provided at non-VA facilities. Total

costs were summed for the final analyses, with cost breakdowns presented in order to

clarify final results. In an attempt to decrease variability and skeweness in the cost data,

the natural log of costs (Incosts) were initially considered for these analyses. Prior to

logging, natural costs were positively skewed. Logging costs resulted in negative

skeweness, but did not decrease variability enough to undertake the analyses.









Additionally, a linear regression model using natural log converts to a nonlinear model,

which requires complicated corrections, and is difficult to interpret.

A multivariable statistical model was implemented using actual costs as the

outcome, based on a difference-in-differences (DiD) approach. The DiD model was used

to compare LAMP with their matched comparison group and TCCP and their matched

comparison group to determine where differences lie within the groups based on total

healthcare costs. The statistical model used for patient costs in this research study was:

E(Costs) = ao + al (Treatment) + a2 (Time) + u3 (Treatment x Time) + PX.

The parameter u3 TepreSents the DiD estimate of the treatment effect.

Finally, a one-way analysis of variance (ANOVA) was used to compare the two

telehealth programs, LAMP and TCCP, to determine where differences lie within the

treatment groups. ANOVA was used to compare the independent variables of age,

marital status, diagnoses, and pre-BDOC, which were the variables used for initial

matching of their comparison groups. Following ANOVA, the DiD approach was

performed to determine if LAMP and TCCP differed in treatment effects based on costs,

after accounting for the covariates determined by ANOVA.

SPSS version 12.0 (SPSS, Inc., Chicago, IL) and SAS version 9.1.3 (SAS Institute,

Cary, NC) were both used for these analyses, with significance level set at .05. All

analyses were two-sided. Analyses followed intention to treat such that all subjects who

were enrolled and participated for one full year in LAMP or TCCP during October 1,

2002 through September 30, 2004 were included in the analyses regardless of study

participation level.









Results

Descriptive baseline data including age, marital status, diagnoses and pre-BDOC

for LAMP and its matched comparison group are presented in Table 3-1. Chi-square for

descriptive variables and independent samples t-tests for continuous variables were used

to compare treatment and comparison groups on these baseline characteristics.

Table 3-1. Baseline characteristics of telerehabilitation group, Low ADL Monitoring
Program (LAMP), and matched comparison group*
Characteristics LAMP Comparison Group p value
(n=115) (n=115)
Age, mean, s/d 72.4 +9.4 71.7 +9.6 .63
Marital Status (73.0) (73.9) .88
Arthriti s (50.4) (60.0) .15
Hypertension (65.2) (54.8) .11
Diabetes (24.3) (23.5) .88
Stroke (35.7) (27.8) .20
Pre-BDOC,
mean, s/d 12.6 +26.3 12.6 +26.2 .98
*Data are given as number (percentage) unless otherwise indicated. BDOC indicates
hospital bed days of care.

LAMP and Matched Comparison Group

LAMP and matched comparison group participants were primarily male (97

percent) with more than 70 percent married. On average, study participants were age 72.

On the average, participants reported four chronic illnesses. More than 50 percent

reported they had been diagnosed with hypertension and arthritis, approximately 25

percent reported diabetes, and approximately 30 percent had suffered a stroke. The

average number of hospital BDOC one year pre-enrollment was 12.6.

Total summed actual costs and cost itemization for LAMP and their matched

comparison group are presented in Tables 3-2 and 3-3. Tables include one-year pre-

enrollment costs in comparison with one-year post enrollment costs.













































Difference in
costs pre-post
Difference in
days/visits pre-
post


Table 3-2. Healthcare expenditures for LAMP (n=115) one-year pre-enrollment and one-
year post-enrollment
Total Sum BDOC Clinic ER NHCU

Pre-Enroll $2,767,712.90 $1,494,483 $1,162,211 $23,842 $87,177
Day s/Vi sits 1449 4561 116 214
Percent of total 54.0% 42% 0.86% 3.15%

Post-Enroll $2,812,250.50 $690,215 $2,053,015 $24,257 $44,763
Day s/Vi sits 623 8728 108 98
Percent of total 24.5% 73% 0.86% 1.6%

Difference in
costs pre-post +$44,537.60 -$804,268 +$890,814 +$415 -$42,414
Difference in
days/visits pre- -826 +4167 -8 -116
post

Table 3-3. Healthcare expenditures for LAMP matched comparison group (n=1 15) one-
year pre-enrollment and one-year post-enrollment
Total Sum BDOC Clinic ER NHCU

Pre-Enroll $2,055,283.60 $1,231,656 $642,052 $16,908 $164,668
Day s/Vi sits 1443 3088 76 404
Percent of total 60% 31% 0.8% 8%

Post-Enroll $1,578,459.30 $553,924 $862,510 $12,826 $149,198
Day s/Vi sits 699 293 1 72 400
Percent of total 35% 55% 0.8% 9.5%


-$476,824.30


-$677,732

-744


+$220,458

-157


-$4,082

-4


-$15,470

-4


Hospital bed days of care

Costs for hospital BDOC in the year preceding enrollment in LAMP totaled

approximately $1,500,000 and consisted of 1449 days of care. These 1449 hospital days

were for 55 patients. The average cost of a BDOC pre-enrollment in LAMP was $1,030.

Total costs for hospital BDOC for LAMP decreased more than $804,000 and 826 days in










the year following enrollment. This represents a 46 percent decrease in costs. The

average cost of a BDOC post-enrollment was $1,100.

Costs for hospital BDOC in the pre-enrollment year for our matched comparison

group totaled approximately $1,230,000 and consisted of 1443 days. These 1443 BDOC

were for 55 patients, and the average pre-cost of a hospital BDOC for our matched

comparison group was $853. Post-costs for hospital BDOC for the matched comparison

group decreased approximately $678,000 and 744 days, or 45 percent. The average post-

cost of a BDOC decreased by $61.

Clinic visits

Every LAMP participant in our study received at least one clinic visit both pre and

post-enrollment. Costs for clinic visits pre-post for LAMP increased more than $890,000

following enrollment, representing an increase of 4167 clinic visits. In an attempt to

determine which clinic encounters increased, clinic visits were calculated for each clinic

stop code for one-year pre-enrollment in LAMP and one-year post enrollment in LAMP.

Clinic visits increased specifically in the area of preventive medicine, including

laboratory and x-rays, and primary and geriatric patient care. Increases were also noted

in physical medicine and rehabilitation, including speech language services, occupational

and physical therapy services, as well as prosthetics. Prosthetic devices increased from

573 pre-enrollment to 1 193 post-enrollment. The provision of prosthetic or assistive

devices, such as bathroom aids and mobility devices was a primary focus of LAMP

services. Diabetes care, urology care, and home health aide assistance were also noted to

increase for the LAMP intervention group. Clinic visits also included 127 home

assessments performed by LAMP and approximately 2605 patient contacts resulting from

enrollment in LAMP.









One hundred eleven of the 1 15 veterans in the matched comparison group received

a pre-clinic visit, and 113 received a post-clinic visit. Clinic visits pre-post for the

matched comparison group increased by 157 visits, which resulted in a cost increase of

approximately $220,000. There were no significant differences in clinic stop codes pre

versus post for the comparison group. In fact, preventive services such as laboratory and

x-rays, as well as primary care and geriatric care decreased by over 100 visits during the

post-year.

Emergency room visits

ER visits for the LAMP participants remained stable over the two-year period.

LAMP ER visits increased in dollar amount by $415, but total visits decreased by 8.

Fifty-one LAMP enrollees required ER services pre-enrollment, and 48 LAMP enrollees

required ER services post-enrollment. Our matched comparison group decreased their

ER visits by 4, resulting in a savings of over $4,000. Thirty-six patients required pre-ER

visits, which decreased to 32 patients in the post-period.

Nursing home bed days of care

A main hypothesis was that LAMP would maintain independence over time to a

greater extent than the other study arms. While it may be difficult to determine which

outcomes signify that one of the study arms is more independent than another, the use of

NHCU may be such an outcome. Functional decline and decreased independence in self-

care are the main reasons patients are placed in nursing homes (Andresen, Vahle, &

Lollar, 2001; Yu, Wagner, Chen, & Barnett, 2003b). LAMP participants spent 214 days

in NHCU pre-enrollment, which decreased to 98 days post-enrollment, demonstrating a

decline of 1 16 days. This amounted to a cost reduction of over $42,000. For LAMP,









NHCU BDOC averaged approximately $14,000 per person pre-enrollment, and

decreased to an average of $8,000 per person post-enrollment.

Our matched comparison group spent over 400 days in NHCU pre-study period,

which decreased by 4 days post-enrollment. This amounted to a cost savings of

approximately $15,000. Yet, for our matched comparison group, NHCU costs per person

increased from $18,000 pre to approximately $25,000 post, compared to $8,000 per

person post-enrollment in LAMP.

TCCP and Matched Comparison Group

Descriptive baseline data including age, marital status, diagnoses and pre-BDOC

for TCCP and its matched comparison group are presented in Table 3-4. Chi-square for

descriptive variables and independent samples t-tests for continuous variables were used

to compare treatment and comparison groups on these baseline characteristics.

Table 3-4. Baseline characteristics of telehomecare group, Technology Care
Coordination Program (TCCP), and matched comparison group*
Characteri sti cs TCCP Comparison Group p value
(n=112) (n=112)
Age, mean, s/d 70.94 +11.2 70.5 +10.9 .77
Marital Status (62.5) (67.0) .12
Hypertension (68.8) (57.4) .06
Diabetes (40.2) (38.4) .78
Stroke (6.3) (8.0) .60
CHF (15.2) (14.3) .85
Pre-BDOC.
mean, s/d 10.23 +30.1 10.8 +34.1 .88
*Data are given as number (percentage) unless otherwise indicated. CHF indicates
congestive heart failure, BDOC indicates hospital bed days of care.

TCCP and matched comparison group participants were primarily male (98

percent) with more than 60 percent married. On average, study participants were aged

70. Participants reported approximately four chronic illnesses. More than 60 percent of

the groups had been diagnosed with hypertension, 40 percent reportedly were diabetic, 6-










8 percent had suffered a stroke, and approximately 15 percent had congestive heart

failure (CHF).

Total summed costs and breakdowns for TCCP and their matched comparison

group are presented in Tables 3-5 and 3-6. Tables include pre-enrollment costs in

comparison with one-year post-enrollment costs. Actual enrollment dates were used for

TCCP to determine pre-post costs. The arbitrary enrollment date of October 1, 2003 was

used for the comparison group to determine pre-one year and post-one year healthcare

expenditures.

Table 3-5. Healthcare expenditures for TCCP (n=1 12) one-year pre-enrollment and one-
year post-enrollment
Total Sum BDOC Clinic ER NHCU

Pre-Enroll $1,474,699 $801,490 $557, 159 $10,456 $105,594
Day s/Vi sits 1146 3414 94 338
Percent of total 54.4% 37.8% 0.7% 7.2%

Post-Enroll $2,140,111 $850,953 $1,095,174 $17,335 $176,650
Day s/Vi sits 1055 5414 193 541
Percent of total 39.8% 5 1.2% 0.7% 7.2%

Difference in
costs pre-post +$665,412 +$49,463 +$538,015 +$6,879 +$71,056
Difference in
days/visits pre- -91 +2000 +99 +203
post

Table 3-6. Healthcare expenditures for matched comparison group (n=1 12) one-year pre-
enrollment and one-year post-enrollment
Total Sum BDOC Clinic ER NHCU

Pre-Enroll $1,606,664 $872,972 $521,625 $6254 $205,812
Day s/Vi sits 1216 2616 31 549
Percent of total 54.3% 32.5% 0.4% 13.0%

Post-Enroll $1,362,215 $438,097 $763,532 $9493 $151,094
Day s/Vi sits 665 2586 84 436
Percent of total 32.0% 56.5% 0.7% 11.0%









Table 3-6. Continued.
Total Sum BDOC Clinic ER NHCU

Difference in -$244,449 -$434,875 +$241,907 +$3,239 -$54,718
costs pre-post
Difference in -551 -30 +53 -113
days/visits pre-
post

Hospital bed days of care

Costs for hospital BDOC for TCCP increased more than $49,000 in the year

following enrollment, but decreased by 91 days. Hospital BDOC in the year preceding

enrollment in TCCP totaled approximately $800,000 and consisted of 1 146 days of care.

These 1 146 hospital days were for 42 patients. The average cost of a BDOC pre-

enrollment in TCCP was $700. The average cost of a BDOC post-enrollment increased

to $800.

Costs for hospital BDOC for the matched comparison group decreased

approximately $435,000 and 551 days. Hospital BDOC in the pre-enrollment year for

our matched comparison group totaled approximately $873,000 and consisted of 1216

days. These 1216 BDOC were for 42 patients, with the average pre-cost of a hospital

BDOC for our matched comparison group at $718. Post-costs for hospital BDOC for the

matched comparison group decreased to an average cost of $658.

Clinic visits

Costs for clinic visits pre-post for TCCP increased more than $538,000 following

enrollment. This represents an increase of 2000 clinic visits. To determine where the

increase was, clinic visits were calculated for each clinic stop code for one-year pre-

enrollment and one-year post enrollment in TCCP. Clinic visits increased in the area of

preventive medicine, including laboratory and x-rays, and primary and geriatric patient









care. Prosthetic devices increased from 321 pre-enrollment to 534 post-enrollment.

Diabetes care, ophthalmology, and home health aide assistance were also noted to

increase for the TCCP intervention group. Clinic stops included more than 1154 patient

intervention contacts resulting from enrollment in TCCP.

Costs for clinic visits pre-post for the matched comparison group increased

approximately $242,000, but number of clinic visits decreased by 30 visits. Preventive

services such as laboratory and x-rays, as well as primary care and geriatric care

remained stable over the year.

Emergency room visits

ER visits increased for both TCCP and their matched comparison group. TCCP ER

visits increased by approximately 100 visits post-enrollment, and more than $6,800.

Forty-one patients visited the ER pre-enrollment in TCCP, and 61 patients visited the ER

post-enrollment. ER visits for the matched comparison group increased by 53 visits and

approximately $3,200, which was an increase from 21 patients to 28 patients pre-post.

Nursing home bed days of care

Pre-enrollment NHCU for TCCP included 338 days at nearly $106,000. The post-

enrollment costs increased by $71,000, and 203 days. For the TCCP matched

comparison group, we see a decline in NHCU of 113 days and $54,700.

Cost Analysis: Difference-in-Differences Approach

As shown in Table 3-7, multivariate results determined that 1 year following

enrollment in LAMP, there are no significant differences in total healthcare costs (costs

include inpatient BDOC, clinic, ER, NHCU), between LAMP and their matched

comparison group.









Table 3-7. Multivariable regression analysis summary examining the relationship among
LAMP and matched comparison group, pre-post intervention, and total
healthcare costs based on the DiD method, with the comparison group serving
as the reference group.
Variable B SEB J
Main Effects
Intercept 17,872 2943 6.07***
Treatment 6, 195 4162 1.49
Time (pre-post) -4,146 4162 -1.00
Interactions
Time*Treatment 4,534 5886 0.77
Note. R2 = 0.02 n=230 ***p < .001

In this model, the overall regression equation was significant (F(3,456)= 3.09, p <

.05), demonstrating the relationship between costs and treatment, but the coefficient of

determination (R2 = 0.02) represents a weak association. The intercept (17,872) is the

mean predicted costs pre-intervention when holding treatment and time constant. The

treatment coefficient (6, 195) is not statistically significant, demonstrating there are no

baseline significant differences in the treatment groups prior to the intervention. The

slope for time (pre-post intervention = -4, 146) is the predicted intervening time effect on

costs for LAMP, which was not significant. The interaction demonstrates the treatment

effect, and is the product of time and treatment on health related costs. The slope of the

product of the two variables represents the change in costs for LAMP as time increases.

Based on the regression coefficient (4,534), we are unable to detect a statistically

significant treatment effect. The R2 Value is an indicator of how well the model fits the

data (e.g., an R2 ClOse to 1.0 indicates that we have accounted for almost all of the

variability with the variables specified in the model). The R2 Of 0.02 indicates that the

variables, treatment and time, account for no more than 2 percent of the variance in costs.

Table 3-8 presents the DiD results for the multivariate analysis between TCCP and

their matched comparison group.









Table 3-8. Multivariable regression analysis summary examining the relationship among
TCCP and matched comparison group, pre-post intervention, and total
healthcare costs based on the DiD method, with the comparison group serving
as reference group.
Variable B SEB J
Main Effects
Intercept 14,345 2540 5.65***
Treatment -1,178 3592 -0.33
Time (pre-post) -2,183 3592 -0.61
Interaction
Time*Treatment 8,124 5080 1.60
Note. R2 = 0.01 (n=224) ***p < .001

In this model, the overall regression equation was not significant (F(3,442)= 1.46, p >

.05), demonstrating this linear model does not fit the data and has no predictive

capability. The regression equation does not provide a basis for predicting costs based on

treatment and, therefore, we are unable to statistically detect a treatment effect with this

model. The R2 Of 0.01 indicates that this group of variables (treatment and time) account

for no more than 1 percent of the variance in costs. Residual scores for our regression

equation are widely dispersed around the regression line, indicating a large error

component.

Treatment Group Comparisons

A one-way analysis of variance (ANOVA) was used to compare LAMP and TCCP

to determine where mean differences lie within the groups based on the independent

variables of age, marital status, diagnoses and pre-BDOC. These are the variables used

for initial matching of the comparison groups. A significant difference was found

between the treatment groups and the diagnoses of arthritis (F(1,225)= 51.04, p < .001),

stroke(F(1,225)=33.5, p < .001), and diabetes (F(1,225)=6.65, p < .05). This analysis revealed

that participants in LAMP had more incidences of arthritis and stroke than participants in

TCCP. This is not surprising, as inclusion in the LAMP program focused on individuals









with rehabilitative needs. Additionally, the variable arthritis was not used for matching

purposes for TCCP and their comparison group. Differences were also found between

TCCP and LAMP in the area of diabetes, with TCCP demonstrating higher prevalence of

diabetes.

Following the comparison, a multivariable regression model using the DiD method

was calculated to examine the effects of group assignment (LAMP/TCCP) and health-

related costs, covarying out the effects of arthritis, stroke, and diabetes. Table 3-9

presents the results.

Table 3-9. Multivariable regression analysis summary examining the relationship in
healthcare costs between LAMP and TCCP, pre-post intervention, covarying
out the effects of diagnoses based on the DiD method, with TCCP as the
reference group.
Variable B SEB J
Main Effects
Intercept 10,999 3061 3.59**
Treatment 12,060 4416 2.73**
Time (pre-post) 5,941 3927 1.51
Arthriti s -3,517 3461 -1.02
Diabetes 5,727 3004 1.91
Stroke 3,890 3783 1.03
Interactions
Time*Treatment -5,553 5517 -1.01
Note. R2 = 0.04 (n=227) **p < .01

In this model, the overall regression equation was significant (F(6,447)= 2.85, p <

.01), demonstrating the relationship between costs and treatment is not likely to be the

result of chance, although the coefficient of determination (R2 = 0.04) represents a weak

association. The intercept (10,999) is the mean predicted costs for the pre-enrollment

period when holding time and treatment constant. The slope for the treatment group

(LAMP) is the predicted effect on costs of being in the treatment group. Based on the

significance of the model and the significance of the treatment variable, there are









additional cross-sectional selection biases evident between our two groups prior to the

intervention. The slope for time (pre-post intervention = 5,941) is the predicted

intervening time effect on costs, which was not significant. The interaction is the product

of time and treatment on health related costs. The slope of the product of the two

variables represents the change in costs for LAMP as time increases. The coefficient for

this variable was negative, and may be interpreted to mean a negative effect on costs

based on treatment by LAMP, but this was not significant. Additionally, we are unable to

detect a statistically significant effect on costs determined by any of the diagnoses in the

model. The R2 Of 0.04 indicates that these variables (treatment, time, arthritis, diabetes,

stroke) account for no more than 4 percent of the variance in costs.

Discussion

This retrospective study examined the effectiveness of a VA telerehabilitation

program (LAMP) and a VA telehomecare program (TCCP) for a cohort of chronically ill

veterans with matched comparison groups by examining healthcare costs at 12 months

following the intervention. In the absence of a randomized controlled trial, this

quasiexperimental design attempted to overcome methodological shortcomings by using

strict matching criteria and a DiD approach to evaluate treatment effectiveness. The DiD

method controls for any intrinsic differences between the groups pre-intervention, as well

as intervening time factors during the intervention, and provides the observed treatment

effect.

Using the DiD approach and actual costs summed for these analyses, no significant

differences were observed in post-enrollment costs between LAMP and their matched

comparison group, TCCP and their matched comparison group, or between the two

treatment groups, LAMP and TCCP. The point estimate of the DiD treatment effect in









each of these models was extremely large relative to the mean costs. Therefore, the

inability to detect significance is a result of the high variability of the estimate, and does

not signify that there is no treatment effect. A larger sample size may improve accuracy

of prediction. Additionally, logging of the costs would reduce variability and may

increase the ability to detect significance.

There are numerous factors to consider. During the 12-months following

enrollment, LAMP participants experienced a considerable increase in clinic visits/stops,

increasing 4, 167 visits at an increase in costs of more than $890,000. Although inpatient

BDOC costs were reduced, including both inpatient BDOC and nursing home BDOC, the

increase in clinic costs increased LAMP's overall costs $44,538 post-enrollment. It is

important to note that one of telehealth' s primary focuses is to increase access to care; as

a result, much of the increase in clinic visits was a product of enrollment in LAMP. The

increase in LAMP clinic stops includes services provided by the intervention, i.e., the

initial evaluation and home assessment, adaptive equipment provided for self-care and

safety, and remote monitoring interventions. Additionally, due to the intensity of daily

monitoring, patients were more apt to be brought into the clinic for check-ups or more in-

depth evaluation in order to ensure an illness did not escalate and require hospitalization.

Although the number of intervention-related clinic stops is provided, it is difficult to

determine how many of the care coordinator-patient contacts resulted in additional

primary or geriatric care visits, lab and diagnostic visits, or secondary clinic visits such as

ophthalmology or audiology. This significant increase in care coordinator-initiated clinic

visits has been observed in other VA home telehealth studies (Chumbler et al., 2005;

Kobb et al., 2003).









During the 12-months following enrollment, TCCP participants also experienced a

large increase in clinic visits, increasing 2,000 visits and more than $500,000. For TCCP,

while the number of inpatient BDOC decreased, inpatient costs increased slightly post-

enrollment. The combined effect of increased costs per BDOC and additional clinic

visits increased TCCP's costs post-enrollment by more than $665,000. The increase in

TCCP clinic stops includes services provided by the intervention, such as the initial home

visits and installation of remote monitoring equipment, and the follow-up monitoring

interventions. TCCP's primary goal of remotely monitoring health symptoms and

providing increased access to care resulted in a significant increase in clinic visits. Due

to the intensity of daily monitoring, patients may receive additional primary or geriatric

care visits, which in turn increase lab and diagnostic procedures. In comparison, TCCP's

matched cohort received 30 less clinic visits, which resulted in a savings of $242,000. It

is evident that when treatment is decreased, costs decrease. Longer term observations are

required to determine the health-related cost effects of these increases and decreases in

ambulatory care.

Additionally, as stringent as our matching criteria were, this was not a randomized

controlled trial; matching was performed retrospectively based on the variables that were

available. When we analyze the cost distribution, LAMP enrollees are considerably more

costly and, therefore, possibly less healthy. The average cost of each BDOC was

approximately $200-$300 higher per day for the LAMP group pre-and post-enrollment in

comparison with both their matched cohort and the telehomecare (TCCP) group.

When actual costs are observed pre and post-study period, we note a significant

decrease in inpatient costs (BDOC) for LAMP (t(114)=3 .09, p<; .01), and both of the










comparison groups. LAMP's matched comparison group decreased approximately 45

percent, or $678,000, based on pre-study costs and post-study costs (t(114)=3.09, p < .01).

Hospital costs for TCCP's matched comparison group decreased approximately 50

percent, or $435,000 within the same study period (tllni=1.95, p <.05). This

phenomenon may be a result of regression to the mean. When using a pre-post design,

regression to the mean may bias results in healthcare expenditures (Barnett, van der Pols,

& Dobson, 2005; T. E. Barnett et al., 2006; Yudkin & Stratton, 1996). Regression to the

mean (RTM) is a statistical phenomenon that may occur whenever there is a nonrandom

sample from a population and two measures that are imperfectly correlated, such as pre-

enrollment costs and post-enrollment costs following an intervention. Veterans in this

study were enrolled into a telehealth program because of their high usage of VA medical

services. Our comparison groups were matched with our treatment groups based on pre-

BDOC and also demonstrate high levels of healthcare use at baseline. In RTM, observed

change may be most negative for those with the largest pretest values. This is often

interpreted as showing the effect of the treatment. While the regression effect is real and

complicates the study of subj ects who are initially extreme on the outcome variable (i.e.,

costs), we attempted to control for it statistically through the DiD design. Unfortunately,

the uses of costs in the design, which were highly variable within and between our study

populations, resulted in a high error rate for our regression analysis.

The observed decrease in inpatient costs may also be explained by a system-wide

secular trend within VA hospitals to decrease inpatient length of stay (BDOC) and

transition to more ambulatory care (Payne et al., 2005; Phibbs, Bhandari, Yu, & Barnett,

2003; Yu et al., 2003a). Additionally, all four study arms demonstrated high costs based










on numerous hospitalizations in our pre-study period. We would assume that if a patient

had been hospitalized in our pre-study time period, following a successful hospital stay

they would not require hospitalization during our post-study period. The ability to

observe these patients over a longer period of time may provide more accurate effects of

treatment versus non-treatment.

There are limitations in the study that need to be addressed. Healthcare costs

included inpatient BDOC, clinic visits, emergency room visits, and NHCU, and were

summed for these analyses. Summed costs included VA-incurred expenses and did not

consider whether the patient utilized services other than the VA, which may be a key

reason for the inability to detect significance. Research has determined that between 25

and 50 percent of veterans are dual users, and seek both primary and inpatient care

outside of the VA (Borowsky & Cowper, 1999; Payne et al., 2005; Stroupe et al., 2005).

This percentage increases when veterans are not satisfied with their care. LAMP and

TCCP enrollees are carefully monitored and referred to VA services, whereas non-

telehealth veterans may be more apt to seek medical care outside of the VA. This would

likely increase costs for our comparison group. Future studies should consider the impact

of differential use of VA services between the groups.

More notably, skewed distribution and heteroscedasticity problems in healthcare

expenditure models have been well recognized by health service researchers (Manning &

Mullahy, 2001; Yu et al., 2003a). For this study, we analyzed actual healthcare costs.

Models were also analyzed using costs transformed by a natural logarithm function. Due

to the difficulty in interpreting the logged results, and the large mean differences between










groups in the exponentiated residuals, logged costs were not used for the final analysis,

but should be considered in future costs studies.

Although patients within each telehealth program, LAMP and TCCP, and their

matched comparison groups, were comparable with respect to age, marital status, pre-

study period hospital bed days of care, and primary chronic illness, we did not consider

additional comorbidities. Many of our participants had multiple comorbidites, which

may result in higher healthcare expenditures, and require more intense remote

momitonng.

Approximately 3 0-3 5 percent of the matching was performed manually. The

ability to acquire a direct one-to-one match was increasingly difficult due to the high

number of variables incorporated into the matching dummy string along with the wide

variability of the pre-BDOC. If pre-BDOC had been stratified, additional matches may

have been obtained, but this was not optimal. Moreover, although careful steps were

taken to ensure close matching of the comparison groups, we had limited access to such

sociodemographic information as educational level, income, or the presence of a

caregiver or other social support within the home.

This study attempted to quantify the effect of telerehabilitation and telehomecare in

reducing healthcare costs among four groups of veterans. The analyses observed

veterans enrolled in LAMP, veterans enrolled in TCCP, and corresponding matched

comparison groups who have not received any type of telehealth intervention. The initial

hypothesis for this study was that veterans enrolled in LAMP, veterans enrolled in TCCP,

and their corresponding matched group of veterans who have not received tele-

rehabilitation or telehomecare interventions will differ in their VA healthcare costs.









Based on results from the multivariable regression analyses, we rej ect the hypothesis that

our four study arms will differ in VA healthcare costs following one-year enrollment in a

telehealth program. It should be noted that based on the variance of errors in each of the

regression equations, numerous unknown or unidentified factors must account for the

remaining variance in the models.

Future research should consider using a randomized controlled trial design,

following the intervention and comparison groups for more than 12 months, analyzing

differential use of VA services, and collecting information to identify care coordinator-

initiated outpatient visits.















CHAPTER 4
HEALTH STATUS AND OUTCOMES FROM THE VETERANS SHORT FORM-12
HEALTH SURVEY

Development of the Veteran's SF-36

The ability to quantify an individual's perception of their illness and how their

illness affects their social and functional roles is an important component when

evaluating healthcare requirements and healthcare interventions (IOM, 2001; Kaplan,

2002; Office of Quality Performance [OQP], 2000). Measurements of health-related

quality of life (HRQoL) are increasingly used to assess the impact of chronic disease and

healthcare interventions, as physiologic measures often correlate poorly with functional

ability and well-being (Andresen & Meyers, 2000; Guyatt, Feeny, & Patrick, 1993). The

SF-36 Health Survey is a frequently used patient-derived measure of disease burden and

HRQoL. The SF-36 was adapted from the Medical Outcomes Study 20-item short form

health survey in an attempt to construct a more efficient scale for measuring general

health (Kazis, 2000; J. E. Ware, Jr. & Sherbourne, 1992). The SF-36 includes one multi-

item scale that assesses eight health concepts: 1) limitations in physical activities due to

health problems; 2) limitations in social activities due to physical or emotional problems;

3) limitations in usual role activities due to physical health problems; 4) bodily pain; 5)

general mental health (psychological distress and well-being); 6) limitations in usual role

activities due to emotional problems; 7) vitality (energy and fatigue); and 8) general

health perceptions (J. E. Ware, Jr. & Sherbourne, 1992). These eight concepts have been

summarized into two summary scores: the physical component summary (PCS) and the









mental component summary (MCS). The original version of the SF-36 is scored using

weights derived from a national probability sample of the U. S. population. Scores are

norm-based with a mean of 50 and a standard deviation (SD) of 10, whereby higher

scores indicate better health.

Veteran's SF-36 Health Survey

The Veterans version of the SF-36 (SF-36V) is a patient-based questionnaire

designed specifically for use among veterans (Kazis et al., 1998). In developing the SF-

36V, the original SF-36 was modified to add more precision to the assessment of role

functioning (Kazis et al., 2004a). These modifications included changing dichotomized

yes/no response choices in two of the role items (role limitations due to physical and

emotional problems) to a five point ordinal scale.

The SF-36V is a reliable and valid measure of HRQoL and is widely used within

the Veterans Health Administration (VHA) (Brazier et al., 1992; Kazis et al., 2004a;

Kazis et al., 2004b; Kazis et al., 1999b; Ware, et al., 1995). Items on the scale were

shown to be internally consistent, with Cronbach Apha' s ranging from 0.93 for PCS and

0.78 for MCS (OQP, 2000).

The 1999 Veterans Large Health Study (LHS) used the SF-36V to establish

baseline health status data on nearly one million veterans. The 1999 LHS established the

VA national average for PCS as 36.9 and 45.08 for MCS (Kazis, 2000; OQP, 2000).

These two summaries, PCS and MCS, are scored using a linear t-score transformation

that was normed to a general U.S. population with a mean of 50 and a SD of 10 (Ware &

Kosinski, 2001). Based on these results and results from past surveys, veteran enrollees

report lower levels of health status reflecting more disease and health burden than the

non-VA civilian population (Kazis, Lee, Ren, Skinner, & Roger, 1999a; Kazis et al.,









1998; Kazis et al., 1999b). The 1999 LHS also reported overall PCS and MCS by the 22

established Veterans Integrated System Networks (VISNs). This study took place in

VISN 8, which includes North Florida/South Georgia and is headquartered in Bay Pines,

Florida. VISN 8 overall PCS is 35.99 (0.9 less than the national VA average scores),

with MCS at 43.59 (1.49 less than the national VA average scores).

Development of the Veteran's SF-12

The SF-12 was developed in an attempt to shorten the SF-36 instrument and,

therefore, shorten the time to take or administer the instrument. The ability to reduce

administration time makes the SF-12 an important tool for clinical practice, if the results

can assist with decision-making about the patient. The SF-12 was developed using

regression methods to select items and weighting algorithms for reproducing the PCS and

MCS scales (Ware, Kosinski, & Keller, 1996). A detailed description of the methods

utilized for construction of the SF-12 has been fully documented (Ware, et al., 1996;

Ware, Kosinski, Turner-Bowker, & Gandek, 2002).

An important factor in development of the SF-12 was the ability to accurately

predict SF-36 scores. Based on a study from the general population (n=2,333), the SF-12

achieved multiple R squares of 0.911 and 0.918 in predicting the SF-36 PCS and MCS

scores, respectively (Ware, et al., 1996). Numerous studies have followed the initial

development of the SF-12, and have determined the validity and reliability of the

measurement for a variety of conditions (Cote, Gregoire, Moisan, & Chabot, 2004;

Haywood, Garratt, & Fitzpatrick, 2005; King, Horowitz, Kassam, Yonas, & Roberts,

2005; Resnick & Nahm, 2001; Riddle, Lee, & Stratford, 2001). In each of these studies,

responsiveness to change was less sensitive with the SF-12 than the SF-36, but essentially

parallel for patient groups of at least one hundred.










The Veteran' s version of the SF-12 survey (SF-12V) is a subset of identical items

from the Veteran's version of the SF-36. The SF-12V also provides a physical

component summary score (PCS-12V) and mental component summary score (MCS-

12V). The PCS-12V and MCS-12V scales are scored using norm-based methods

transformed to have a mean of 50 and a SD of 10. Table 4-1 presents the SF-36V

question and the respective SF-12V question.

Table 4-1. Short Form Health Survey-36V questions with respective Short Form Health
Survey-12V questions


SF-36V
Question 1 In general, would you say your health is:
Excellent
Very Good
Good
Fair
Poor
Question 2 Does your health now limit you in these
activities? If so, how much?
2b Moderate activities, such as moving a table, pushing a
vacuum cleaner, bowling, or playing golf!
Yes, limited a lot
Yes, limited a little
No, not limited at all
2d Climbing several flights of stairs?
Yes, limited a lot
Yes, limited a little
No, not limited at all
Question 3 During the past 4 weeks, have you had any of the
following problems with your work or regular daily activities
as a result of your physical health?
3b Accomplished less that you would like:
No, none of the time
Yes, a little of the time
Yes, some of the time
Yes, most of the time
Yes, all of the time


SF-12V
Question 1








Question 2a




Question 2b







Question 3a










Table 4-1. Continued.


SF-36V
3c Were limited in the kind of work or other activities?
No, none of the time
Yes, a little of the time
Yes, some of the time
Yes, most of the time
Yes, all of the time
Question 4c Didn't do work or other activities as carefully as
usual :
No, none of the time
Yes, a little of the time
Yes, some of the time
Yes, most of the time
Yes, all of the time
Question 7 During the past 4 weeks, how much did the pain
interfere with your normal work (including both work outside
the home and housework)?
Not at all
A little bit
Moderately
Quite a bit
Extremely
Question 8 -
These questions are about how you feel and how things have
been with you during the past 4 weeks. For each question,
please give the one answer that comes closest to the way you
have been feeling
8d Have you felt calm and peaceful?
All of the time
Most of the time
A good bit of the time
Some of the time
A little of the time
None of the time
8e Did you have a lot of energy?
All of the time
Most of the time
A good bit of the time
Some of the time
A little of the time
None of the time


SF-12V
Question 3b






Question 4b







Question 5














Question 6a







Question 6b










Table 4-1. Continued.
SF-36V SF-12V
Question 8f Have you felt downhearted and blue? Question 6c
All of the time
Most of the time
A good bit of the time
Question 9 During the past 4 weeks, how much of the time Question 7
has your physical health or emotional problems interfered with
your social activities (like visiting with friends, relatives, etc.)?
All of the time
Most of the time
A good bit of the time
Some of the time
A little of the time
None of the time

Methods

The Veterans SF-36 and Veterans SF-12 were fully developed and supported by the

Veterans Health Study (VHS) (SDR 91006.S, principal investigator Lewis Kazis), which

was funded by the VA Health Service Research and Development Service and the VA

Center for Health Quality, Outcomes and Economic Research in Washington, DC.

Permission to use the SF-36V and the SF-12V for our study was obtained by the VISN 8

Community Care Coordination Service (CCCS) from the developer, Lewis Kazis. There

was no cost for use, only that the developer is made aware of any studies or publications

that utilize the measurement.

Design

This portion of the study includes a retrospective analysis of data collected from

two telehealth programs funded by the VISN 8 CCCS at the NF/SG VA. Veterans who

were enrolled between October 2002 and September 2004 in the Technology Care

Coordination Program (TCCP), a telehomecare program, and the Low ADL Monitoring

Program (LAMP), a telerehabilitation program, were included in our study. Please refer









to Chapters 1 and 3 for in-depth information regarding these two telehealth programs.

Our hypothesis focuses on differences in health status between the two telehealth

programs from baseline to post 12-months enrollment. The SF-36V and SF-12V were

used to measure self-reported physical and mental outcomes. Measurements were

administered at baseline during the initial enrollment and at 12-months follow-up. All

scores were input into the VISN 8 CCCS database in Bay Pines, FL by their respective

telehealth program. The CCCS provided SF-36V and SF-12V data on both telehealth

programs for this portion of the study. This secondary analysis was approved by the

University of Florida and Veterans Administration Institutional Review Boards (IRB

439-2005).

Participants

TCCP is a VA telehomecare program that uses home-based telehealth technology

in conjunction with nurse practitioners and a social worker to coordinate care for

chronically ill veterans living in remote areas in NF/SG. Veterans are eligible to be

enrolled in TCCP if they meet the following criteria: a) past high-cost medical care

needs (>$25,000) and high healthcare utilization (two or more hospitalizations and

frequent emergency room visits), b) have electricity and phone service, c) accept

technology in their homes for monitoring purposes, d) sign an informed consent form or

have the consent form signed by a proxy. Participants included in this study were

veterans enrolled in TCCP between October 2002 and September 2004 who had

completed a full year in the program (n=112). Of the 1 12 enrollees participating in this

study, 84 completed a self-report health survey both at baseline and one year follow-up.

Of the remaining 28 participants, 26 completed baseline testing, but were unavailable for