|UFDC Home||myUFDC Home | Help|
This item has the following downloads:
ASSESSMENT OF A TELEREHABILITATION AND A TELEHOMECARE
PROGRAM FOR VETERANS WITH CHRONIC ILLNESSES
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
Roxanna M. Bendixen
To my loving husband; he is the reason.
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
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
ACKNOWLEDGMENT S .............. .................... iv
LI ST OF T ABLE S ........._..... .............._ ix...___. ....
LI ST OF FIGURE S .............. .................... xi
AB STRAC T ................ .............. xii
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...
LIST OF TABLES
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
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
Roxanna M. Bendixen
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.
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
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 &
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.
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
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
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
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,
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.
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.
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
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
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
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.
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.
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
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
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 (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
Reminders and alerts (re: vaccines, etc.)
Video-based medical consultation
Consultation with specialists
Patient monitoring: in-home (monitoring vital signs, etc.)
Transfer of medical records/images
Professional Education Medical literature searches
Accessing reference material
Consumer Information & Online searches for health information
Health Searches for doctors or health plans
Health insurance benefits information
Participation in support and chat groups
Access to personal health records
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
*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,
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-
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
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.,
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, 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
* Removing the barriers of distance, time and travel from healthcare.
* Cost effectiveness by avoiding unnecessary emergency room visits and
* 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.,
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
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.
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
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
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
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
*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.
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
important and will serve as a standard for the methodology of future telehealth
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
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.
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
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
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.
1999 LHS Database for VISN 8 = 75,715 Veterans
SMissing Data = 9,871
Comparison Pool =
participants = 48
Comparison Pool =
Cross reference with
I2005 outpatient data =
Comparison Pool =
NF/SG patients only =
Comparison Pool =
Non-CCCS patients =
Comparison Pool =
Unverifiable patient data =
Final Comparison Pool =
Matched to LAMP = 115
based on age, marital
diagnoses, number of pre-
study inpatient bed days
Matched to TCCP = 112
based on age, marital
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.
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-
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.
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
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
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.
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
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
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
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.
Table 3-2. Healthcare expenditures for LAMP (n=115) one-year pre-enrollment and one-
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%
costs pre-post +$44,537.60 -$804,268 +$890,814 +$415 -$42,414
days/visits pre- -826 +4167 -8 -116
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%
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.
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
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
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
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
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
Table 3-5. Healthcare expenditures for TCCP (n=1 12) one-year pre-enrollment and one-
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%
costs pre-post +$665,412 +$49,463 +$538,015 +$6,879 +$71,056
days/visits pre- -91 +2000 +99 +203
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
Difference in -551 -30 +53 -113
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
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.
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
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
Intercept 17,872 2943 6.07***
Treatment 6, 195 4162 1.49
Time (pre-post) -4,146 4162 -1.00
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
Intercept 14,345 2540 5.65***
Treatment -1,178 3592 -0.33
Time (pre-post) -2,183 3592 -0.61
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
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
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
Variable B SEB J
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
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.
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
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
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
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.
HEALTH STATUS AND OUTCOMES FROM THE VETERANS SHORT FORM-12
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
Question 1 In general, would you say your health is:
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
Table 4-1. Continued.
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
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
Quite a bit
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
Table 4-1. Continued.
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
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.
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
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