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Assessing the Implementation of Health Information Technology in Florida Hospitals

Permanent Link: http://ufdc.ufl.edu/UFE0044441/00001

Material Information

Title: Assessing the Implementation of Health Information Technology in Florida Hospitals Process and Impact
Physical Description: 1 online resource (176 p.)
Language: english
Creator: Bilello, Lori A
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2012

Subjects

Subjects / Keywords: ehr -- hit -- hitech -- quality
Health Services Research, Management, and Policy -- Dissertations, Academic -- UF
Genre: Health Services Research thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: The Health Information Technology for Economic and Clinical Health (HITECH) Act, as part of the American Recovery and Reinvestment Act, allocated over $19 billion for the U.S. health care system to adopt and meaningfully use health information technology (HIT).  This legislation provided specific Meaningful Use Objectives that need to be met in order to receive these funds. The primary objectives of this study are to assess acute care hospitals’ current use of electronic health records and whether meeting the federally mandated objectives is associated with better quality of care.  Specifically, this study ascertains the level of attainment of Florida hospitals in meeting the CMS Stage I meaningful use objectives, identifies hospital organizational characteristics which are associated with higher levels of “meaningful use” among Florida hospitals (HIT Adoption Model), and assesses the association between achievement of the meaningful use objectives and improved patient process measures (Quality Model). This study was conducted using a retrospective, cross-sectional design linking the 2010 Florida Hospital HIT Survey database with the CMS Hospital Compare quality measures for pneumonia and heart failure. The results of the HIT Adoption Model indicate several significant positive relationships between key hospital characteristics and meeting the CMS meaningful use objectives including urban location of hospitals, affiliation with a hospital system, and the presence of a Chief Medical Information Officer on staff.  The Quality Model analysis provided mixed results with only three of the six pneumonia quality measures and one of four heart failure measures showing a significant positive, although weak association with meeting the CMS meaningful use objectives. This study addresses an important gap in the literature, especially regarding the CMS MU objectives and their implementation as a path to standardize and measure the impact on patient care and adds to the evidence regarding the effect of using health information technology on health outcomes.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Lori A Bilello.
Thesis: Thesis (Ph.D.)--University of Florida, 2012.
Local: Adviser: Duncan, R. P.
Local: Co-adviser: Harle, Christopher A.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2012
System ID: UFE0044441:00001

Permanent Link: http://ufdc.ufl.edu/UFE0044441/00001

Material Information

Title: Assessing the Implementation of Health Information Technology in Florida Hospitals Process and Impact
Physical Description: 1 online resource (176 p.)
Language: english
Creator: Bilello, Lori A
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2012

Subjects

Subjects / Keywords: ehr -- hit -- hitech -- quality
Health Services Research, Management, and Policy -- Dissertations, Academic -- UF
Genre: Health Services Research thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: The Health Information Technology for Economic and Clinical Health (HITECH) Act, as part of the American Recovery and Reinvestment Act, allocated over $19 billion for the U.S. health care system to adopt and meaningfully use health information technology (HIT).  This legislation provided specific Meaningful Use Objectives that need to be met in order to receive these funds. The primary objectives of this study are to assess acute care hospitals’ current use of electronic health records and whether meeting the federally mandated objectives is associated with better quality of care.  Specifically, this study ascertains the level of attainment of Florida hospitals in meeting the CMS Stage I meaningful use objectives, identifies hospital organizational characteristics which are associated with higher levels of “meaningful use” among Florida hospitals (HIT Adoption Model), and assesses the association between achievement of the meaningful use objectives and improved patient process measures (Quality Model). This study was conducted using a retrospective, cross-sectional design linking the 2010 Florida Hospital HIT Survey database with the CMS Hospital Compare quality measures for pneumonia and heart failure. The results of the HIT Adoption Model indicate several significant positive relationships between key hospital characteristics and meeting the CMS meaningful use objectives including urban location of hospitals, affiliation with a hospital system, and the presence of a Chief Medical Information Officer on staff.  The Quality Model analysis provided mixed results with only three of the six pneumonia quality measures and one of four heart failure measures showing a significant positive, although weak association with meeting the CMS meaningful use objectives. This study addresses an important gap in the literature, especially regarding the CMS MU objectives and their implementation as a path to standardize and measure the impact on patient care and adds to the evidence regarding the effect of using health information technology on health outcomes.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Lori A Bilello.
Thesis: Thesis (Ph.D.)--University of Florida, 2012.
Local: Adviser: Duncan, R. P.
Local: Co-adviser: Harle, Christopher A.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2012
System ID: UFE0044441:00001


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1 ASSESSING THE IMPLEMENTATION OF HE ALTH INFORMATION TEC HNOLOGY IN FLORIDA HOSPITALS: P ROCESS AND IMPACT By LORI A. BILELLO A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMEN T OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2012

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2 2012 Lori A. Bilello

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3 To my family for all of their support and encouragement

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4 ACKNOWLEDGMENTS I would like to express my gratitude to my Dissertation Committee C hair, Dr. R. Paul Duncan, who encouraged me to pursue my doctorate degree and his confidence and support all throughout this process M y sincere appreciation to my Co c hair, Christopher Harle for sharing his knowledge of Health Information Te chnology and providing invaluable advice on my topic, as well as guidance and patience through the dissertation process. I want to thank Jeffery Harman, a committee member, whose was instrumental in helping me with the study design analysis and interpret ation of the results. Thirdly, I want to thank Robert Cook a committee member, who provided a unique prospective of the topic as a practicing physician. I would also like to thank the WellFlorida Council and Christopher Sullivan, formerly from the Agency for Health Care Administration for providing me the opportunity to be a part of the Florida HIT Environmental Scan project wh ich resulted in the development of an unique database which was used for my study for this dissertation the Florida Hospital H e alth I nformation T echnology Survey. Lastly, I would like to thank my family and friends for their belief in me and their support in my decision to return to graduate school.

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5 TABLE OF CONTENTS page ACKNOWLEDGMENTS ................................ ................................ ................................ .. 4 LIST OF FIGURES ................................ ................................ ................................ .......... 9 LIST OF ABBREVIATIONS ................................ ................................ ........................... 11 ABSTRACT ................................ ................................ ................................ ................. 12 2 CHAPTER 1 INTRODUCTION ................................ ................................ ................................ .... 14 Role of Health Information Technology in Healthcare Quality ................................ 14 Rationale ................................ ................................ ................................ .......... 14 Policy Environment and Emphasis ................................ ................................ ... 16 Study Objectives ................................ ................................ ................................ ..... 17 Significance of Study ................................ ................................ .............................. 18 2 BACKGROUND ................................ ................................ ................................ ...... 19 Government Policies, Legislation and Progr ams ................................ .................... 20 Meaningful Use (MU) Objectives ................................ ................................ ............ 21 Electronic Health Records (EHRs) Definition ................................ .......................... 22 3 LITERATURE REVIEW ................................ ................................ .......................... 28 HIT Adoption Levels ................................ ................................ ............................... 28 Predictors of Hospital HIT Adoption ................................ ................................ ........ 29 HIT and Quality ................................ ................................ ................................ ....... 33 CPOE Studies ................................ ................................ ................................ .. 34 CDSS Studies ................................ ................................ ................................ .. 36 EHR Studies ................................ ................................ ................................ ..... 39 Limitations of Prior Studies ................................ ................................ ..................... 43 4 CONCEPTUAL FRAMEWORK ................................ ................................ ............... 45 ................................ ................................ ........... 45 Delone and McLean Information System Success Model ................................ ....... 47 Conceptual Model Development ................................ ................................ ............. 48 Hypotheses ................................ ................................ ................................ ............. 49 5 DATA AND METHODS ................................ ................................ ........................... 54 Data Sources ................................ ................................ ................................ .......... 54

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6 Florida Health Information Technology Environmental Scan ............................ 55 CMS Hospital Compare ................................ ................................ .................... 57 AHCA Hospital Beds and Services List ................................ ............................ 58 Florida Hospital Uniform Reporting System ................................ ...................... 58 Am erican Hospital Association Annual Survey ................................ ................. 58 Study Objective 1 ................................ ................................ ................................ .... 59 Study Objective 2 ................................ ................................ ................................ .... 59 Dependent Variable ................................ ................................ .......................... 60 Independent Variables ................................ ................................ ..................... 60 Study Objective 3 ................................ ................................ ................................ .... 60 Dependent Variables ................................ ................................ ........................ 61 Independent variable ................................ ................................ ........................ 62 Control Variables ................................ ................................ .............................. 63 Statistical Analysis ................................ ................................ ................................ .. 63 HIT Adoption Model ................................ ................................ .......................... 63 Quality Model ................................ ................................ ................................ ... 64 6 RESULTS ................................ ................................ ................................ ............... 74 Descriptive Statistics ................................ ................................ ............................... 74 HIT Adoption Analysis ................................ ................................ ............................. 75 Bivariate Statistics ................................ ................................ ............................ 76 Multivariate Analysis ................................ ................................ ......................... 76 Sensitivity Analysis ................................ ................................ ........................... 78 Quality Measures Analysis ................................ ................................ ...................... 79 Bivariate Statistics ................................ ................................ ............................ 80 Multivariate Analysis ................................ ................................ ......................... 80 Sensitivity Analysis ................................ ................................ ........................... 83 Specified Models ................................ ................................ .............................. 84 7 DISCUSSION AND CONCLUSIONS ................................ ................................ .... 100 Summary and Interpretation of Results ................................ ................................ 100 MU Objectives Met ................................ ................................ ......................... 100 HIT Adoption ................................ ................................ ................................ .. 101 Impact on Quality ................................ ................................ ........................... 103 Limitations ................................ ................................ ................................ ............. 105 Policy Implications ................................ ................................ ................................ 107 APPENDIX A FLORIDA HOSPITAL INFORMATION TECHNOLOGY SURVEY ........................ 113 B SUPPLEMENTAL STATISTICAL DATA ................................ ............................... 159 LIST OF REFERENCES ................................ ................................ ............................. 169 BIOGRAPHICAL SKETCH ................................ ................................ .......................... 176

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7 LIST OF TABLES Table page 2 1 CMS Stage 1 meaningful use objectives and measures for eligible hospitals .... 25 3 1 HIMSS EMR Adoption Model ................................ ................................ ............. 44 5 1 CMS Stage 1 Meaningful Use Measures matched to Florida Hospital Survey Measures ................................ ................................ ................................ ............ 68 5 2 Description and categorization of variables ................................ ........................ 72 6 1 Survey respondents and population characteristics ................................ ........... 85 6 2 Bivariate statistics: hospital characteristics by MUSum ................................ ...... 86 6 3 Poisson regression estimates for Total MU objectives met (MUSum) ................ 87 6 4 Poisson regression estimates for core MU objectives met (MUCore) ................. 88 6 5 Logistic regression estimates for binary MUSum ................................ ................ 89 6 6 Dependent variable characteristics ................................ ................................ ..... 90 6 7 Bivariate statistics: Hospital Compare measures and MUSum ........................... 91 6 8 Summary of GLM regression estimates for Hospital Compare measures .......... 91 6 9 GLM regression estimates for Pneumonia quality measures ............................. 92 6 10 GLM regression estimates for heart failure quality measures ............................. 92 6 1 1 Summary of GLM regression estimates for Hospital Compare measures (expanded model) ................................ ................................ ............................... 93 6 12 GLM regression estimates for Pneumonia quality measures (expanded model) ................................ ................................ ................................ ................ 94 6 13 GLM regression estimates for heart failure quality measures (expanded model) ................................ ................................ ................................ ................ 95 6 14 Logistic regression estimates for B inary CMS hospital quality measures ........... 96 6 15 CPOE and CDSS analysis with CMS hospital quality measures ........................ 96 6 16 Specified model fo r PNIAS ................................ ................................ ................. 97 6 17 Specified model for HFLVSF ................................ ................................ .............. 97

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8 7 1 Florida hospitals meeting meaningful use 2010 ................................ ............. 112 7 2 Florida hospitals receiving CMS EHR incentive payments 2011 .................... 112

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9 LIST OF FIGURES Figure page 4 1 DeLone and McLean IS Success Model, 1992 ................................ ................... 53 4 2 EHR Use/Quality Model ................................ ................................ ...................... 53 6 1 Total number of MU objectives met by Florida hospitals ................................ .... 98 6 2 Percent of each MU objectives met by Florida hospitals ................................ .... 98 6 3 Residual versus fitted plot after Poisson regression ................................ ........... 99 6 4 Percent of each MU Objective met by low and high HIT Adopters ..................... 99 A 1 Q Q plot residual fitted GLM regression for PNIAT ................................ .......... 159 A 2 Residual versus fitted plot after GLM regression for PNIAT ............................. 159 A 3 Q Q plot residual fitted GLM regression for PNIAS ................................ .......... 160 A 4 Residual versus fitted plot after GLM regression for PNIAS ............................. 160 A 5 Q Q plot residual fitted GLM regression for PNPVS ................................ ......... 161 A 6 Residual versus fitted plot after GLM regression for PNPVS ............................ 161 A 7 Q Q plot residual fitted GLM regression for PNIVS ................................ .......... 162 A 8 Residual versus fitted plot after GLM regression for PNIVS ............................. 162 A 9 Q Q plot residual fitted GLM regression for PNBC ................................ ........... 163 A 10 Residual versus fitted plot after GLM regression for PNBC .............................. 163 A 11 Q Q plot residual fitted GLM regression for PNSC ................................ ........... 164 A 12 Residual versus fitted plot after GLM regression for PNSC .............................. 164 A 13 Q Q plot residual fitted GLM regression for HFLVSF ................................ ....... 165 A 14 Residual versus fitted plot after GLM regression for HFLVSF .......................... 165 A 15 Q Q plot residual fitted GLM regression for HFAIARB ................................ ..... 166 A 16 Residual versus fitted plot after GLM regression for HFAIARB ........................ 166 A 17 Q Q plot residual fitted GLM regression for HFDI ................................ ............. 167

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10 A 18 Residual versus fitted plot after GLM regression for HFDI ............................... 167 A 19 Q Q plot residual fitted GLM regression for HFSC ................................ ........... 168 A 20 Residual versus fitted plot after GLM regression for HFSC .............................. 168

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11 LIST OF ABBREVIATION S AHA American Hospital Association AHCA Agency for Health Care Administration ARRA American Recovery and Reinvestment Act of 2009 CMS Centers for Medicare & Medicaid Services CDSS Computerized Physician/Provider Decision Support System CPOE Computerized Physician Order Entry EHR Electronic Health Record HHS Department of Health and Human Services HIMSS Healthcare Information and Management Systems Society HIT Health Information Technology HITECH Health Information Technology for Economic and Clinical Health Act IOM Institute of Medicine IT Information Technology MU Meaningful Use ONC Office of the National Coordinator for Health Information Technology

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12 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 ASSESSIN G THE IMPLEMENTATION OF HE ALTH INFORMATION TEC HNOLOGY IN FLORIDA HOSPITALS: P ROCESS AND IMPACT By Lori A. Bilello August 2012 Chair: R. Paul Duncan Cochair: Christopher A. Harle Major: Health Services Research The Health Information Technology for Econ omic and Clinical Health (HITECH) Act, as part of the American Recovery and Reinvestment Act, allocated over $19 billion for the U.S. health care system to adopt and meaningfully use health information technology (HIT). This legislation provided specific Meaningful Use o bjectives that need to be met in order to receive these funds. The primary objective s of this study are to assess ac use of electronic health records and whether meeting the federally mandated objectives is associ ated with better quality of care. Specifically, this study ascertains the level of attainment of Florida hospit als in meeting the CMS Stage I Meaningful U se objectives, identifies hospital organizational characteristics which are associated with higher le vels (HIT Adoption Model) and assesses the associat ion between achievement of the Meaningful U se objectives and improved patient process measures (Quality Model) This study was conducted using a retrospective, cross sectional design linking the 2010 Florida Hospital HIT Survey

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13 database with the CMS Hospital Compare quality measures for pneumonia and heart failure The results of the HIT Adoption Model indicate several significant positive relationships between key hospital chara cteristics and meeting the CMS Meaningful U se objectives including urban location of hospitals, affiliation with a hospital system, and the presence of a Chief Medical Information Officer on staff. The Quality Model analysis provided mix ed results with only three of the six pneumonia quality measures and one of four heart failure measure s showing a significant positive, although weak association with meeting the CMS Meaningful U se objectives. This study addresses an important gap in the l iterature, especially regarding the CMS Meaningful Use objectives and their implementation as a path to standardize and measure the impact on patient care and adds to the evidence regarding the effect of using health information technology on health outcom es.

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14 CHAPTER 1 INTRODUCTION Role of Health Information Technology in Healthcare Quality There is a common belief that widespread adoption of health information technology (HIT) has the potential to improve health care quality, reduce costs and increase th e efficiency of the health care delivery system (IOM, 2001; ONCHIT, 2004; Hillestad et al, 2005; Blumenthal, 2010). The basis for this belief derives from logic and several narrowly defined studies or anecdotal evidence. While the potential benefits of im plementing health information technologies such as electronic health records and computerized provider order entry systems are clear in theory, identifying and measuring their impact on health care has been challenging. In this study, the process of HIT a doption and the relationship between the level of HIT adoption and the quality of care delivered in Florida hospitals will be systematically and rigorously examined. Rationale The implementation of health information technology has become a major priority in the health care industry due to: (1) rising health care costs; (2) escalating concerns for patient safety and reducing medical errors; (3) focus on improving the provision of evidence based care; and (4) the increasing number of regulatory requirements placed on health care providers (Doebbeling, Chow & Tierney, 2006). The National Coordinator of Health Information Technology, appointed by the Secretary of Health and Human Services, envisioned that widespread use of health information technology will result in fewer medical errors, fewer unnecessary treatments or wasteful care, fewer variations in care, and will ultimately improve care (ONCHIT, 2004). It is believed the use of HIT will result in the prevention of many medical errors

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15 because all of the data, accessible and readable by all, and that automated order entry systems and decision support systems would be able to check for errors and provide evidence based clinical guidelines to aid health care providers in decision making at the point of care (IOM, 2001). There is evidence that having electronic health records that are readily accessible can reduce treatment errors that result from gaps in knowledge regarding past medical hi story, allergies, or medications, especially when patients are being treated by multiple providers. Additionally, there is evidence that decision support tools can integrate electronic patient information directly into the provision of care and can reduce errors of omission that result from gaps in provider knowledge or the failure to synthesize and apply that knowledge in clinical practice (Shekelle & Goldzweig, 2009). Furthermore, a nationwide electronic health information infrastructure will allow provid ers real time access to health records across health care settings, reduce duplication of services and help coordinate care during transitions of care. Through the standardization of information and processes in the delivery of care, there will be less opp ortunity for errors and omissions that may lead to poor outcomes. Although the preceding assumptions regarding the successes to be found when HIT is readily adopted are commonly held during the last decade there has been an increased focus on studying the actual impact of HIT on the delivery of health care. The studies that have been conducted have resulted in mixed findings of the benefits of HIT adoption on improving care and/or reducing costs. One of the challenges in conducting HIT research and compar ing results with other studies is the wide variety of HIT

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16 systems and definitions. Most of these studies have been performed at a single facility testing a particular function of their health information system. The specific nature of these studies, especi ally the type of health information systems deployed in these facilities and the specific functions tested, can reduce the generalizability of their findings. The potential benefits of HIT are likely to be realized not just by investing in specific systems but by effectively managing and integrating those systems into patient care delivery processes across the patient care continuum. Therefore, to study the impact of HIT on health care quality, it is important to identify not only the components and functi onality of health information systems but also how they are used. To provide strong, general evidence of HIT value in terms of quality, studies that span multiple hospitals and carefully measure HIT functionality and usage at a detailed level are needed. T o date, there have been very few studies conducted that link actual usage of HIT to hospital performance or quality. Policy Environment and Emphasis Despite the lack of consistent and convincing evidence of the benefits of HIT, current national health po licy reflects the belief in its potential and has committed a sizable investment in the implementation of HIT. The American Recovery and Reinvestment Act of 2009 Health Information Technology for Economic and Clinical Health Act (HITECH Act) appropriated $19.2 billion to improve health care delivery and patient care through investment in health information technology over the next five years. These funds are targeted to increase the use of Electronic Health Records (EHRs) by physicians and hospitals acros s the country by reimbursing providers for a large portion of the initial costs of purchasing or upgrading their health information systems. By providing this sizable appropriation, government officials support the

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17 expected benefits of widely using electro nic health records and made the promotion of a nationwide, interoperable health information system a national priority. The HITECH Act attempts to standardize the measurement of HIT system use roviders will be required to perform in order to participate in the EHR incentive program (ARRA, 2009). These objectives were selected as measures that demonstrate the provision of care meeting nationally recognized standards of care from the National Qual ity Forum, the Agency for Healthcare Research and Quality and other organizations. The legislation provides an incremental approach in implementing these standards of care in three stag es to be released in the first 3 years of the EHR incentive program w ith Stage 1 having been released by the Centers for Medicare & Medicaid Services (CMS) in July 2010. This study uses survey data gathered from Florida hospitals that collected information on the type of health information systems hospitals have in place a nd how these systems are used relative to the CMS Stage 1 Meaningful Use (MU) objectives and in doing so, examines the relationship between HIT usage and health care quality in these hospitals. Study Objectives The primary objective of this study is to as use in terms of the CMS Stage 1 MU objectives and whether meeting these objectives is associated with better quality of care. The specific objectives are: 1. To ascertain the level of attainment of CMS Stage I MU object ives among Florida hospitals. 2. To identify hospital organizational characteristics associated with higher levels of

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18 3. To assess the relationship between hospitals achieving the MU objectives and quality of care. Significance of Study Even t hough the potential of HIT for improving the health care delivering system is very compelling and has now been adopted as a national strategy to improve health care quality and increase the efficiency of health care delivery system, there are many challeng es in trying to assess the impact of HIT on the quality of care. In particular, the many disparate systems currently available to providers and the variation of system functionalities make it difficult to compare or measure the impact of these systems. T o address this issue, the focus in this study is on the achievement of standardized MU objectives and not on which system is being used. This focus allows a more rigorous assessment of HIT impact on the delivery of care. This study will be an important c ontribution to the literature since the MU objectives are a new federal requirement to participate in the CMS EHR incentive objectives and the impact these objectives w ill have on improving the quality of care. Evidence of whether the Stage 1 MU objectives are associated with the quality of hospital care will be important as policy makers continue to add or modify these objectives and the corresponding reporting requir ements for Medicare and Medicaid providers. Furthermore, identifying the characteristics of hospitals that are able to meet the MU objectives in the early stages of the EHR incentive program will provide valuable information to support efforts in targetin g those hospitals that need the most assistance in implementing EHRs.

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19 CHAPTER 2 BACKGROUND Throughout the last decade, health care researchers and policy makers have promoted the use of health information technology, especially electronic health records, as a way to transform the delivery of health care (Chaudhry et al., 2006). This discussion came to the forefront as a way to improve health care after the release of the To Err Is Human: Building a Safer Heal th System which estimated that at least 44,000 deaths in the United States are caused by clinical errors each year (IOM, 2000). Many of these deaths are the result of process errors, medication errors, or failure to provide the standard of care for a give n medical condition (IOM, 2000). The Institute of Medicine released a follow up report, Crossing the Quality Chasm: A New Health System for the 21st Century, which outlined several initiatives to prevent medical errors and improve the quality of health care in the United States. Key recommendations included the widespread use of health information technology and providing medical care based on the best available evidence (IOM, 2001). The IOM advocated for a nationwide commitment of all stakeholders to building an information infrastructure to support health care delivery, consumer health, quality measurement and improvement, public accountability, clinical and health services research, and clinical education (IOM, 2001). Since the release of this repor t, many of the recommendations have been endorsed by industry leaders, health care associations, health care policy makers, and some have been incorporated into legislation.

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20 Government Policies, Legislation and Programs Partly in response to the 2001 IOM report, several government policies and programs have been developed during the last decade to promote the use of information technology in the health care industry. President George W. Bush issued an Executive Order in April, 2004 which directed the impl ementation of a nationwide interoperable health information technology infrastructure and widespread adoption of EHRs within ten (10) years and established the National Coordinator for Health Information Technology (Executive Order No. 13335, 2004). The Ex ecutive Order directed the National Coordinator to produce a report on the development and implementation of a strategic plan to guide the nationwide implementation of interoperable HIT in both the public and private sectors. This strategic plan, called T he Decade of Health Information Technology: Delivering Consumer centric and Information rich Health Care was released in July 2004 and outlined four major goals, corresponding strategies and action steps in realizing the vision for improved health care thr ough the widespread use of health information technology. The goals outlined in the plan included informing clinical practice through EHR adoption, electronically connecting clinicians to other clinicians, using information tools to personalize care delive ry, and advancing surveillance and reporting for population health improvement (ONCHIT, 2004). The benefits of a consumer centric and info rmation rich health care system include the reduction of medical errors, decrease in the variation in the quality of care, and increase in consumer access and knowledge of their medical information (ONCHIT, 2004). This plan was updated in 2008 and was the framework for much of the HIT related legislation that followed.

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21 In 2009, the Health Information Technology for Eco nomic and Clinical Health (HITECH) Act, as part of the American Recovery and Reinvestment Act, allocated over $19 billion for the U.S. health care system to adopt and meaningfully use HIT. This legislation firmly established the national agenda and timeli nes for the widespread adoption of HIT. The HITECH Act gives Department of Health and Human Services (HHS) the authority to establish programs to improve health care quality, safety, and efficiency through the use of electronic health records and health in formation exchange. This legislation provided funding for EHR incentive programs that pay eligible health care professionals and hospitals to purchase or upgrade certified EHRs (ARRA, 2009). Eligible professionals (physicians, dentists, certified nurse mid wives and nurse practitioners) who meet CMS established EHR meaningful use criteria and patient volume thresholds may receive as much as $44,000 under the Medicare EHR incentive program or $63,750 under the Medicaid EHR incentive program. Eligible hospital s may access payments through both the Medicare and Medicaid EHR incentive programs. Through these programs, hospitals can receive millions of dollars for the meaningful use of federally certified EHRs (CMS, 2010). Meaningful Use Objectives As provis ioned by the HITECH Act, the HHS wa s directed to develop specific MU objectives that providers must report on in order to participate in the EHR incentive program (ARRA, 2009). The goal of the HITECH Act is not only adoption of EHR technology but also usi ng this technology to achieve significant improvements in health care processes and outcomes (Blumenthal 2010). HHS announced in July 2010 the first of three stages of objectives that hospitals must meet in order to qualify for

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22 Medicare and Medicaid incen (CMS, 2010). Stage 1 MU objectives include 14 core or required objectives and 10 menu objectives which hospitals must choose 5 to report on. These objectives are grouped by major policy priorities: 1) improve quality, safety, efficiency, and reducing health disparities; 2) engage patients and family in health care; 3) improve care coordination; 4) ensure adequate privacy and security protections for personal health information; 5) improve population and public health (CMS, 2010). Specific measures are outlined in Table 2 1 and address activities such as recording patient information, ordering and receiving test results, computerized order entry for medications and medication checks, decision support sys tems, and exchanging information with patients, other providers and health agencies. (NQF) previously endorsed standards. These standards developed through a consensus process involvi ng medical associations, purchasers, health care professionals, and others, have been identified and accepted by healthcare professionals as useful, achievable and relevant to improving health care quality and performance (NQF, 2011). Many of these measur es are already in use by physicians hospital value based purchasing (HVBP) programs. Electronic Health Records (EHRs) Definition EHRs are complex health information systems that have evolved over time. Because of this evolutionary process, there are many ways to define what comprises an electronic health record, and these definitions oftentimes depend on who is using it and how it is used. It is important to note, EHRs are al so often referred to as Electronic

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23 Medical Records or EMRs. For the purpose of this study, any reference to EMRs in the literature will be reported as EMRs/EHRs to reduce confusion and maintain consistency in the discussion of these systems. The most com monly cited definition of an EHR, (HIMSS) definition of an EHR as provided below: patient he alth information produced by encounters in one or more care settings. Included in this information are patient demographics, progress notes, problems, medications, vital signs, past medical history, immunizations, laboratory data and radiology reports. The EHR system The EHR has the ability to generate a complete record of a clinical patient encounter as well as supporting other care related activities directly or indirectly via interface including evi dence based decision support, quality management, and outcomes reporting http://www .himss.org/ASP/topics_ehr.asp.) The adoption of information technology in the health care field has been a slow and costly proce ss as compared to other industries. Hospital information systems were first initiated in the 1960s and became prevalent in U.S. hospitals in 1970s but were mostly confined to certain departments or functions in the hospital such as registration and billin g, scheduling systems and laboratory information systems (Collen, 1995). Over the years, silos of health information in hospitals have evolved. Most hospital EHRs combine data from these disparate systems administrative services such as registration a nd billing with large ancillary services, such as pharmacy, laboratory, and radiology, and other various clinical care components such as nursing plans, medication administration records, and physician orders (NIH, 2006). The number of integrated component s and features of hospital EHRs is dependent on how information systems evolved in a given facility, their financial ability to purchase or custom build these systems and the vendors they have selected for the various components.

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24 To meet the CMS MU objec tives, hospitals must have an integrated EHR system that has the ability to: collect and store a wide range of clinical and demographic information; provide computerized physician order entry (CPOE) to electronically order laboratory, pharmacy, and radiolo gy services; include an integrated decision support system that can incorporate clinical practice guidelines; and have the ability to exchange information with others inside and outside of their network. Only a small percentage of hospitals in the U.S. c urrently meet these criteria due to the substantial investment in human and financial resources that is required to achieve this level of EHR adoption (HIMSS, 2010).

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25 Table 2 1. CMS Stage 1 meaningful use objectives and measures for eligible hospitals Poli cy Priority Core Objectives Measures Improving quality, safety, efficiency, and reducing health disparities Use CPOE for medication orders directly entered by any licensed healthcare professional who can enter orders into the medical record per state, loc al and professional guidelines More than 30% of unique patients with at least 1 medication in their department have at least one medication order entered using CPOE Implement drug drug and drug allergy interaction checks The hospital has enabled this functionality for the entire EHR reporting period Record patient demographics (sex, race, ethnicity, date of birth, preferred language, and date and preliminary cause in the event of death) M emergency department have demographics recorded as structured data Maintain up to date problem list of current and active diagnoses More than 80% of all unique patients admitted to the hosp ital or emergency department have at least one entry or an indication that no problems are known for the patient recorded as structured data Maintain active medication list More than 80% of all unique patients admitted to the hospital or emergency depart ment have at least one entry (or an indication that the patient is not currently prescribed any medication) recorded as structured data Maintain active medication allergy list More than 80% of all unique patients admitted to the hospital or emerg ency department have at least one entry (or an indication that the patient no known medication allergy) recorded as structured data Record and chart changes in vital signs (height, weight, blood pressure, body mass index, growth charts for children) For more than 50% of all unique patients age 2 and over admitted to the hospital or emergency department, height weight and blood pressure are recorded as structured data Record smoking status for patients 13 years of age or older More than 50% of all unique patients 13 years old or older admitted to the hospital or emergency department have smoking status recorded as structured data Implement one clinical decision support rule and ability to track compliance with the rul e One clinical decision support rul e implemented

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26 Table 2 1. Continued Policy Priority Core Objectives Measures Report hospital clinical quality measures to CMS or States For 2011, provide aggregate numerator and denominator and exclusions through attestation; for 2012, electronically submit measures Engage patients and families in their health care On request, provide patients with an electronic copy of their health information (including diagnostic test results, problem list, medication lists, medication allergies, and for hospitals, discharge summary and procedures) More than 50% of requesting patients receive an electronic copy of their health information within 3 business days Provide patients with an electronic copy of their discharge instructions at time of discharge, upon req uest More than 50% of all patients who are discharged from the inpatient or emergency department of a hospital or who request an electronic copy of their discharge instructions are provided with it Improve care coordination Capability to exchange key clin ical information (for example, problem list, medication list, medication allergies, diagnostic test results) among providers of care and patient authorized entities electronically electron ically exchange key clinical information Ensure adequate privacy and security protections for personal health information Protect electronic health information created or maintained by the certifies EHR technology through the implementation of appropriate technical capabilities Conduct or review a security risk analysis, implement security updates as necessary, and correct identified security deficiencies as part of the risk management process Policy Priority Menu Objectives Measures Improving quality safety, efficiency, and reducing health disparities Implement drug formulary checks The hospital has enabled this functionality and has access to at least one internal or external drug formulary for the entire EHR reporting period Record advance direct ives for patients 65 years of age or older More than 50% of all unique patients 65 years of age or older admitted to a hospital have an indication of an advance directive status recorded Incorporate clinical laboratory test results into certified EHR tec hnology as structured data More than 40% of clinical laboratory test results whose results are in positive/negative or numerical format are incorporated into EHRs as structured data

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27 Table 2 1. Continued Policy Priority Menu Objectives Measures Generat e lists of patients by specific conditions to use for quality improvement, reduction of disparities, research, or outreach Generate at least one report listing patients with a specific condition Use certified EHR technology to identify patient specific education resources and provide those to the patient as appropriate More than 10% of all unique patients seen by the EP or hospital are provided patient specific education resources Improve care coordination The hospital who receives a patient from anothe r setting of care or provider of care or believes an encounter is relevant should perform medication reconciliation The hospital performs medication reconciliation for more than 50% of transitions of care The hospital who transitions their patient to ano ther setting of care or provider of care or refers their patient to another provider of care should provide summary of care record for each transition of care or referral The hospital who transitions or refers their patient to another setting of care or pr ovider of care provides a summary of care record for more than 50% of transitions of care or referrals Improve population and public health Capability to submit electronic data to immunization registries or immunization information systems and actu al submission in accordance with applicable law and practice submit electronic data to immunization registries and follow up submission if the test is successful (where registries can acce pt electronic submissions) Capability to submit electronic data on reportable (as required by state or local law) lab results to public health agencies and actual submission in accordance with applicable law and practice Perform at least one test of cert provide electronic submission of reportable lab results to public health agencies and follow up submission if the test is successful (where public health agencies can accept electronic submissions) Capability to submit electronic syndromic surveillance data to public health agencies and actual submission in accordance with applicable law and practice provide electronic syndromic surveillance data to pub lic health agencies and follow up submission if the test is successful (where public health agencies can accept electronic submissions)

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28 CHAPTER 3 LITERATURE REVIEW The scientific literature on health information technology has grown considerably in th e last twenty years with most of the studies focused on two main themes: 1) adoption of HIT; and 2) its impact on the quality, safety and efficiency of heath care provision. It is important to examine both of these themes since the rate and extent of HIT adoption has an impact on how well hospitals and providers might use HIT in the delivery of patient care independent of any influence it may have on the quality, safety and efficiency of the care provided. HIT Adoption Levels Over the past decade, there h as been significant pressure from the federal government and other payers on hospitals and physicians to adopt HIT, especially electronic health records. However, the adoption rate remains fairly low, especially in comparison to hospitals in Europe (Ander son et al., 2006). Several national surveys are available that examine the rate and level of HIT adoption in hospitals. Most notable are the annual surveys performed by Health Information Management Systems Society (HIMSS) and the American Hospital Associ ation (AHA). These studies indicate there are two major levels at which technology is adopted: 1) the organization level, at which the IT system is purchased and installed; and 2) the user level, where the intended users of the information system decide wh ether or not to incorporate that technology in their dail y practice (Fonkych & Taylor, 2005). Generally, t hese national studies tend to capture types of systems available within health care organizations but do not capture the extent to which a system is u sed in patient care delivery. The national HIMSS Analytics survey showed that the vast majority of hospitals are still only in the low or

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29 medium stage of EHR adoption process. They categorize adoption levels into seven stages with hospitals at Stage 1 only having major ancillary clinical systems installed (i.e., pharmacy, laboratory, radiology) and Stage 7 where hospitals no longer uses paper charts to deliver and manage patient care. The HIMSS EMR/EHR Adoption Model and specific characteristics of each st age are illustrated in Table 3 1 The 2010 HIMSS survey showed that 90% of hospitals had clinical information systems for ancillary services but only 1.0% of hospitals in 2010 had reached Stage 7 The majority of the hospitals in the middle stages (HIMSS, 2010). Jha and his colleagues at the Harvard School of Public Health reported similar results with 2.7% of the hospitals they surveyed in 2009 having a comprehensive electronic health record, according to their definition where twenty four specified clini cal functions are fully functional across all departme nts within the hospital (Jha et al., 2010). However, national surveys by the American Hospital Association and RAND have reported significantly higher EHR adoption rates with the AHA survey citing 11% o f hospitals being fully functional while RAND reported between 20 30% (AHA 2007, Fonkych & Taylor, 2005). The estimates vary depending on how EHRs are defined, how well the questions can distinguish between EHR adoption and use, and the proper characteriza tion of the multitude of features available in hospital information systems. Predictors of Hospital HIT Adoption Various studies have examined the characteristics of hospitals that have adopted HIT using data from national studies such as the HIMSS or AH A surveys as well as hospitals).

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30 Understanding the factors or characteristics that may influence hospital adoption of EHRs can guide policymakers in the distribution of fu nding to incentivize providers to adopt and meaningfully use EHRs. Many of the hospital characteristics examined include size (number of beds), system affiliation, ownership status, geographic location (rural or urban) and financial status. Using data fro m the 2004 H IMSS Analytics survey, Kazley and Ozcan (2007) performed a national study of over 4,000 hospitals which examined how the organizational and environmental factors correlate with a hospital's EMR/EHR adoption. Their findings showed EMR/EHR adopt ion is significantly associated with hospital system affiliation, size (larger) and urban location. Other characteristics such as market competition, non profit or public ownership, teaching status, public payer mix, and operating margin were not statistic ally significant. A RAND study, also using 2004 H IMSS Analytics data, found that HIT adoption and use is more common for academic and pediatric hospitals and that system affiliation is the strongest predictor of hospital HIT use. They also found that E HRs are less likely in small hospitals, rural hospitals and for profit hospitals (Fonkych & Taylor, 2005). However, Li and colleagues (2008), who examined multi hospital system affiliation on the level of EMR/EHR adoption in greater detail, found that sma ll hospitals owned by multihospital systems had a significantly higher EMR/EHR level compared with independent hospitals. They speculate that smaller hospitals in multi hospital systems have an advantage over small independent hospitals in HIT capacity be cause of the greater availability of capital, access to shared HIT capacity, and technical expertise.

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31 Jha et al. (2009) collaborated with the American Hospital Association (AHA) to survey all acute care member hospitals in 2008 for the presence of specific electronic health record functionalities. They examined the relationship of adoption of EHRs to hospital characteristics such as size, geography, ownership, teaching status, and presence of markers of a high technology institution (dedicated coronary care unit, burn unit, or PET scanner). Larger hospitals, those located in urban areas, and teaching hospitals were reported to have a significant higher number of electronic health record functionalities. Wang et al. (2002) studied the factors influencing hos pital HIT adoption including several financial factors, using a sample of 1,441 U.S. hospitals in 1998. Their results show that hospitals affiliated with a multi hospital system and those that are for profit are more likely than others to have IT applicati ons as well as hospitals with higher cash flows, and operating margins. A Florida study resulted in similar findings as the national studies. Hikmet et al. (2008) collected HIT data from a survey of 98 Florida hospitals in 2003. Their study examined whethe r specific organizational characteristics, such as hospital size, geographic location, system affiliation, and tax status influence adoption of health care information technologies. They found that non profit hospitals, larger hospitals (regardless of syst em affiliation), as well as small hospitals affiliated with multi hospital systems, had higher levels of HIT adoption. Geographic location of hospitals was not a factor in HIT adoption. The authors attributed this result to the high number of small, rura l hospitals affiliated with multi hospital systems in Florida.

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32 Major barriers to EHR adoption has been identified in several surveys. The barriers to adoption include capital costs of implementing an EHR (54%), ongoing costs of maintaining a system (32%), interoperability with current systems (27%), acceptance by the clinical staff (23%), and the availability of well trained IT staff (16%) (AHA, 2007). Similar barriers have b een identified by Jha and his colleagues based on a 2008 national survey of hospitals. Their findings showed that among hospitals without EHRs, the most commonly cited barriers were inadequate capital (74%), maintenance costs (44%), physician resistance to EHRs (36%), unclear return on investment (32%), and lack of availability of HIT staff (30%) (Jha et al, 2009). T he Lewin Group report from the Health Information Technology Panel findings commissioned by the National Coordinator for Health Information Tec hnology in 2005 also found that ongoing operating costs, including specialized staff for the ongoing operation and maintenance of systems, as a major factor in affecting HIT adoption (Lewin Group, 2005). Hersh and Wright (2008), using the HIMSS Adoption Mo del, found that the amount of IT staff varies by level of EHR adoption, with 0.082 FTE per bed at the lowest level of the HIMSS Adoption Model (Stage 0) and increasing to 0.210 FTE bed at higher levels (Stage 4). In summary, most of these studies found tha t larger hospitals, non profit hospitals, hospitals in urban settings and hospitals affiliated with multi hospital systems had greater levels of HIT adoption. Issues regarding having adequate capital, physician support and adequate HIT staff are also know n to be factors in HIT adoption.

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33 HIT and Quality Much of the HIT literature begins with a discussion regarding the consensus among policy makers, health care researchers and quality experts that widespread adoption of HIT will lead to increased efficiency and improved patient care (IOM, 2001; ONCHIT, 2004; Blumenthal, 2010). The enthusiasm regarding the potential benefits of HIT on improving the delivery of health care has led to a national policy that providers should adopt HIT, although the evidence in s upport of these benefits varies greatly by type of application. There has been some evidence that HIT has a positive impact on quality by eliminating poorly written and poorly organized paper medical records, standardizing physician orders, and incorporati ng clinical pathways and other tools to literature for the National Health Service in the United Kingdom, Car and colleagues (2008) found evidence of primary studies showing positive benefits from EHRs in particular institutions, but the nature and magnitude of benefits were not consistent across studies, nor was there clear findings on how benefits might be applied to other institutions and settings. This is especially true w ith studies with favorable results that were based on home grown health information systems such as the one developed by the Veteran Health Administration. Two systematic reviews of the literature, one funded by AHRQ in 2006 and one follow up study publis hed in 2009 found some evidence of cost and quality benefits of comprehensive HIT systems at a few organizations (Chaudhry et al., 2006; Goldzweig et al., 2009; Shekelle & Goldzweig, 2009). In particular, these studies found that HIT, especially more advan ced EHR systems with computerized physician order entry (CPOE) and clinical decision support systems (CDSS) have been shown to decrease

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34 medication errors and improve quality by providing decision support tools and increasing adherence to clinical practice guidelines. Much of the relevant research on CPOE and CDSS, either alone or as integrated systems. Evidence of CPOE and ient outcomes are presented in detail in the following sections. CPOE Studies Computerized physician order entry (CPOE) is a n important component of a comprehensive HIT system and has been defined as an electronic application used by physicians to order dr ugs, laboratory tests, radiology and other medical procedures as well as requests for consultations (Poon et al., 2004). It is important to note that CPOE systems offer a variety of capabilities depending on the vendor. The successful use of these systems also depends on how well the hospitals have integrated CPOE throughout their facility and patient care processes. Several systematic reviews of the literature show that there is strong and consistent evidence that CPOE is an important intervention in the reduction of medication errors and adverse drug events. Medication errors are one of the most common errors that occur in hospitals due to the complexity of the medication process with ordering, dispensing, and delivery/administration of medications to pa tients requiring the participation of numerous health care providers (IOM, 2000). Ammenswerth et al. (2008) performed a systematic and quantitative review of the literature to determine the effect of e prescribing/CPOE on the risk of medication errors and adverse drug events (ADE). They identified 27 studies that met their inclusion criteria and the majority of these studies were conducted in hospital inpatient units. Two

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35 of the studies were randomized trials and the rest were before/after implementation s tudies. Twenty three of the 25 studies that evaluated the effects on the medication error rate showed a significant decrease in medication errors with the use of e prescribing/CPOE with relative risk ratios between 0.01 and 0.87. They also saw a reduction in actual and potential adverse drug events. A systematic review of studies that examined the impact of CPOE on prescribing errors for pediatric and adult inpatients was performed by Reckmann et al. (2009). They identified 4 pediatric studies and 9 adult studies that met their inclusion criteria. The pediatric studies were performed in either the pediatric or neonatal intensive care units and all the studies showed a decrease in medication errors and an increase in the proper administration of IV drug the rapies. All but one of the adult studies demonstrated lower medication errors with the use of CPOE. The use of CPOE in the management of hospital patient orders is not confined to only medications but can include many hospital departments. Besides reduc ing medication errors or adverse drug events, the use of CPOE in hospitals can also reduce turnaround time for procedures, overuse of diagnostic tests, and reduce length of stay. Kuperman and Gibson (2003) examined the literature on CPOE by major outcome or effect. They identified several randomized control trials on the use CPOE for laboratory test ordering which resulted in a 5% to 18% reduction in laboratory orders due to the identification of duplicate orders or other unnecessary orders. They found mi xed evidence with regards to CPOE in reducing the use of radiology procedures where one study had positive results and another study showed minimal impact on reducing radiology orders even though there was an improvement with procedure turnaround

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36 time. Kup erman and Gibson (2003) identified two studies that demonstrated a reduction in inpatient length of stay, a randomized control trial by Tierney et al. (1993) and a time series study by Mekhjian et al. (2002). The study by Tierney and colleagues showed that the use of CPOE with decision support capabilities in certain medical units resulted in a significant reduction of length of stay by 0.89 days while the Mekhjian study only had a decrease of 0.20 days in one of the two hospitals that participated in the s tudy. A recent study was done to try to link the CMS MU objectives to quality. Jones and colleagues (2011) used a national survey to estimate data for one particular MU objective electronic medication order entry which served as the primary variable of interest for their study and examined its impact on mortality rates. Their results suggest that the initial meaningful use threshold for hospitals using electronic orders for at least 30% of eligible patients did not have a significant impact on deaths fr om heart failure and heart attack. However, the proposed threshold for the stage 2 of Meaningful U se using the orders for at least 60% of patients, was associated with lower mortality. CDSS Studies Computerized decision support systems (CDSS) can vary grea tly in their form and functionality as well as the extent of their integration in the care process and the tools for checking the completeness and accuracy of patient o rders while more sophisticated systems integrate patient data with evidence based practice guidelines for the comprehensive management of patient care. These applications can produce patient specific output in the form of care recommendations, assessments, alerts and reminders to actively support clinical decision making (Car et al., 2008). Many researchers view embedding CDSS into well developed, comprehensive EHRs as a

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37 way to truly harness the full potential of HIT to provide timely, relevant information, guide clinical decisions and improve patient safety and outcomes (Staggers, Weir, & Phansalkar, 2008). Garg et al. (2005) performed a comprehensive systematic review of the literature and identified 100 studies that included randomized and nonrandomized controlled trials from 1973 to 2004 that evaluated the effect of a CDSS compared with care provided without a CDSS on practitioner performance or patient outcomes. These studies included inpatient and outpatient settings and were grouped by major activity preventive care, diagnosis, disease management and organizational efficiency. There were 10 trials evaluating diagnostic capabilities and 4 of these studies found CDSS to improve practitioner performance. Two of the 4 successful CDSS studies examined d iagnostic capability for cardiac ischemia in the emergency department and found that it significantly decreased the rate of unnecessary hospital admissions by 15%. Of the 97 studies they identified as assessing practitioner performance, the majority (64%) improved diagnosis, preventive care, disease management, or drug prescribing. Of the 52 studies they identified as assessing patient outcomes, the researchers only found 7 studies that improved patient outcomes with CDSS; however, they noted many of these studies did not have adequate statistical power to detect differences. Damiani et al. (2010) performed a systematic review of available literature and focused their research on a key component of CDSS, computerized clinical guidelines, and its impact on t he process of care compared with the use of non computerized clinical guidelines such as paper guidelines, peer to peer consultation or previous experience. Of the 45 articles they selected for the study, 64% showed a positive effect

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38 with the use of comput erized clinical guidelines. Specifically, they found a significant impact on the process of care with the automatic provision of recommendations provided by the computerized clinical guidelines as part of clinician workflow as compared to the use of non co mputerized clinical guidelines. Kaushal, Shojania and Bates (2003) also examined studies using stand alone CDSS (without CPOE) and their impact on medication safety. They identified 7 studies in their literature review, 6 were randomized control trials an d one was a prospective, before after analysis. These results showed that 6 of the 7 studies demonstrated improvements in medication safety including lower numbers of toxic levels of medications, and improved antibiotic drug selection (greater pathogen su sceptibility). A meta analysis of research involving 311 unique studies using CDSS and Knowledge Management Systems (KMS) from 1976 to 2010 was conducted by the Duke Evidence based Practice Center under contract to the Agency for Healthcare Research and Qu ality (AHRQ, 2012). This meta analysis examined three main constructs of these systems: their impact on clinical effectiveness, their impact on outcomes and to identify features that impact their success. The researchers found 22 studies that assessed the impact on morbidity and found moderate success in reducing patient morbidity with a combined relative risk of 0.88 (95% CI 0.80 to 0.96). The researchers found 6 studies that assessed the impact on mortality and found limited evidence that CDSSs were eff ective in reducing patient mortality. The meta analysis did find that CDSS and KMS can improve the process of delivering care, especially in the areas of performing preventive services (n = 25; OR 1.42), ordering clinical studies (n = 20; OR 1.72), and pr escribing therapies (n = 46; OR 1.57).

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39 However, some studies have illustrated that CPOE and CDSS can have unintended consequences or introduce other types of errors. Kaushal and colleagues (2003) found that early in the implementation phase of these syste ms, incorrect default dosing or medication recommendations may create potentially erroneous orders. Some CDSSs may be based on flawed or outdated clinical practice guidelines, have faulty algorithms or were not thoroughly tested (Car et al., 2008). User error is always an issue with computers such as inputting incorrect patient data which will result in erroneous CDSS recommendations, clicking on the wrong patient or item selection (often select the item above or below the intended choice), or ignoring al erts generating by the system (Ash et al., 2009). Improper use may occur when the technology is a poor fit with the current workflow processes. Careful testing, documentation and ongoing scrutiny of these systems by clinicians, managers and software design ers is necessary to identify emerging issues and remedy them before there is serious patient consequences (Harrison, Koppel & Bar Lev, 2007). EHR Studies As noted earlier, there have been many studies on certain components of HIT such as CPOE and CDSS but there are only a limited number of studies that have focused on hospital EHRs and their impact on health care quality and outcomes. Recent studies have shown some degree of success but also some mixed results. Early success with electronic medical records can be found with the Veterans Health Administration (VHA) and their implementation of a national, interoperable electronic medical record system in the early 1990s. Asch et al. (2004) compared the quality of care received by patients at 12 VHA facilitie s with a sample of almost a 1,000 patients from community hospitals. They collected data on 348 indicators targeting 26

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40 conditions and found that the VHA facilities were associated with higher levels of overall patient care, chronic disease management an d preventative care but not in acute care. However, this study was also during the same period when the VHA was implementing their quality performance measurement system which may have influenced these results. Kazley and Ozcan (2008) performed a nationw ide cross sectional study using the HIMSS Analytics hospital HIT adoption database and Hospital Quality Alliance (HQA) quality measures for 2004 to investigate the impact of EMRs/EHRs and hospital quality performance. Four of the ten HQA process measures s elected showed a statistically significant positive relationship for hospitals with EMRs/EHRs as compared to hospitals without EMRs/EHRs and one measure had a negative relationship. The positive measures included the use of beta blocker at arrival to the h ospital, use of aspirin at discharge from hospital, and use of beta blocker at discharge from hospitals for acute myocardial infarction patients, and the assessment of left ventricular function for congestive heart failure patients. The negative result was a measure for pneumonia that examined if patients were given antibiotic within 4 hours of admission. DesRoches and colleagues (2010) performed a study of U.S. hospitals using EHR adoption information from a national survey to investigate the relationship between the adoption of EHRs and select measures of health care quality and efficiency from HQA and Medicare using 2008 data. In particular, their investigation looked at whether EHR adoption was associated with better performance on standard process of c are measures, lower mortality and readmission rates, shorter lengths of stay, and lower inpatient costs. Their results showed no significant relationship between EHRs and

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41 mortality or readmission rates, as well as most of their process of care measures exc ept for the prevention of surgical complications which had a slight, but statistically significant, improvement. The authors noted that further study is needed to look beyond adoption of EHRs and more on how EHRs are being used in hospitals. Parente and McCullough (2009) performed a longitudinal study using HIMSS Analytics database and MEDPAR data for the period from 1999 2002. Specifically, they studied whether EHRs, electronic nurse charting, and picture archiving and communications systems (PACS) had an impact on 3 outcome measures: infection due to medical care, postoperative hemorrhage, and postoperative pulmonary embolism or deep vein thrombosis. Their results showed that EHRs was the only HIT application to have a statistically significant positiv e effect on one measure infection due to medical care. McCullough et al. (2010) built upon their earlier study by examining HIT adoption decisions over a 3 year time period (2004 2007) using the HIMSS Analytics database tabase for process/quality measures. Their findings showed that for most of their selected measures, quality was higher for hospitals with EHRs but only two of these measures were statistically significant higher rates of pneumococcal vaccination and ap propriate antibiotic selection for pneumonia. Similar to the McCullough study, Jones et al. (2010) also performed a longitudinal study using HIMSS hospital HIT adoption data from 2003 and 2006 and Hospital Compare data from 2004 and 2007 to examine a diffe rence in quality over time. They used a difference in difference analytic approach to estimate the relationship between

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42 hospital EHR adoption levels and improvement in quality measures during the two time periods. Over the specified time period, the resea rchers saw an increase in the adoption of EHRs and a movement towards more advanced EHRs. They found that hospitals that maintained a basic EHR during this time period had statistically significant improvements in their quality scores for heart failure, A MI and pneumonia as compared to hospitals with no EHRs. However, hospitals that adopted EHRs during this time period did not have improved quality scores as compared to hospitals with no EHRs, indicating that it takes time for the benefits of EHRs to take effect. Himmelstein et al. (2010) examined both the cost and quality impacts of HIT in hospitals. Using HIMSS Analytics data, they created composite scores for each hospital based on the number of computer applications implemented in their facility inclu ding EHRs. They used Medicare cost reports and the 2008 Dartmouth Atlas data on costs and quality of care as their outcome measures. The Dartmouth Atlas data included 4 quality scores for pneumonia, congestive heart failure, and acute myocardial infarct ion and a quality composite score. The researchers found that hospitals with higher overall computer application scores had slightly better composite quality scores, especially those hospitals with EHRs and CPOE. For specific quality measures, more comput erized hospitals scored higher on process measures of care for acute myocardial infarction, but not for pneumonia or heart failure. Menachemi, Chukmaitov, Saunders, & Brooks (2008), performed a similar study among Florida hospitals and found that hospitals that adopted a greater number of IT applications were significantly more likely to have better quality outcomes on certain

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43 inpatient quality indicator measures, including risk adjusted mortality from coronary angioplasty, gastrointestinal hemorrhage, and acute myocardial infarction. Limitations of Prior Studies While the potential benefits of health information technology are clear in theory, identifying its impact on health care quality has proven difficult and rates of use within the hospital industry ha ve been limited. Most of the studies discussed in the systematic reviews have been performed at single institutions, oftentimes with hospitals that have developed their own HIT applications or are considered HIT leaders. Many of the HIT studies on quality in the literature focused primarily on CPOE and CDSS (Chaudhry et al., 2006 ; Goldzweig et al., 2009). The specific nature of these studies on HIT adoption, especially the type of health information systems deployed in these facilities and the specific fun ctions tested, can reduce the generalizability of their findings. Moreover, the potential benefits of HIT are likely to be realized not just by investing in specific systems but by effectively managing and integrating those systems into patient care delive ry processes. Therefore, to study the impact of HIT on health care quality, it is important to identify not only the components and functionality of health information systems but also how they are used. Only recently national studies have been performed on the impact of hospital EHRs and quality. Many of these studies show mixed results. To provide strong, general evidence of HIT value on health care quality, especially with EHRs, studies that span multiple hospitals and carefully measure HIT functionali ty and usage at a detailed level are needed. There have been very few studies to date that link actual usage instead of adoption of HIT to hospital performance or quality and to my knowledge, none that have specifically tested the CMS MU objectives.

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44 Table 3 1. HIMSS EMR Adoption Model Stage Description Stage 0 The organization has not installed all of the key ancillary department systems (e.g. laboratory, pharmacy, radiology). Stage 1 Major ancillary clinical systems are installed (i.e., pharmacy, laborat ory, radiology). Stage 2 Major ancillary clinical systems feed data to a clinical data repository (CDR) that provides physician access for retrieving and reviewing results. The CDR contains a controlled medical vocabulary, and the clinical decision suppor t/ rules engine (CDS) for rudimentary conflict checking. Information from document imaging systems may be linked to the CDR at this stage. The hospital is health information exchange (HIE) capable at this stage and can share whatever information it has in the CDR with other patient care stakeholders. Stage 3 Nursing/clinical documentation (e.g. vital signs, flow sheets) is required; nursing notes, care plan charting, and/or the electronic medication administration record (eMAR) system are scored with extra points, and are implemented and integrated with the CDR for at least one service in the hospital. The first level of clinical decision support is implemented to conduct error checking with order entry (i.e., drug/drug, drug/food, drug/lab conflict checkin g normally found in the pharmacy). Some level of medical image access from picture archive and communication systems (PACS) is available for access by physicians outside the Radiology Stage 4 Computerized Practi tioner Order Entry (CPOE) for use by any clinician is added to the nursing and CDR environment along with the second level of clinical decision support capabilities related to evidence based medicine protocols. If one patient service area has implemented C POE with physicians entering orders and completed the previous stages, then this stage has been achieved. Stage 5 The closed loop medication administration environment is fully implemented. The eMAR and bar coding or other auto identification technology, such as radio frequency identification (RFID), are implemented and integrated with CPOE and pharmacy to maximize point of care patient safety processes for medication administration. Stage 6 Full physician documentation/charting (structured templates) is implemented for at least one patient care service area. Level three of clinical decision support provides guidance for all clinician activities related to protocols and outcomes in the form of variance and compliance alerts. A full complement of PACS syste ms provides medical images to physicians via an intranet and displaces all film based images. Stage 7 The hospital no longer uses paper charts to deliver and manage patient care and has a mixture of discrete data, document images, and medical images withi n its EMR environment. Clinical data warehouses are being used to analyze patterns of clinical data to improve quality of care and patient safety. Clinical information can be readily shared via standardized electronic transactions (i.e. CCD) with all entit ies who are authorized to treat the patient, or a health information exchange (i.e., other non associated hospitals, ambulatory clinics, sub acute environments, employers, payers and patients in a data sharing environment). The hospital demonstrates summar y data continuity for all hospital services (e.g. inpatient, outpatient, ED, and with any owned or managed ambulatory clinics). Source:http://www.himssanalytics.org/docs/HA_EMRAM_Overview_ENG.pdf

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45 CHAPTER 4 CONCEPTUAL FRAMEWORK As demonstrated in the re view of the literature, a few national studies been performed on the impact of hospital EHRs and quality and only a limited number of these studies have examined the link between actual usage of HIT, inste ad of simple adoption of HIT, and hospital performa nce or quality. The purpose of this dissertation is to study the possible linkages between the actual usage of EHRs in hospitals and the quality of care at these organizations A conceptual model derived from two overreaching frameworks of investigation, one focused on quality (Donabedian) and the other on information systems (Delone and McLean), will be employed which will allow this study to both build upon and contribute to the literature. The following provides an overview of these frameworks and th e coalescence of these into this study's conceptual model. Although Florence Nightingale and Dr. Ernest Codman are consider early pioneers for evaluating and monitoring health care quality and patient outcomes, Avedis Donabed ian is considered the father of the quality movement for the health care industry (Iezzoni, 2003; Car et al., 2008). Donabedian's seminal paper introduced the concepts of structure, process, and outcome for the evaluation of the quality of health care in 1 966 (Car et al., 2008). ent structures, which influence processes and outcomes (Donabedian, 2005). Donabedian defined structure as the qualifications of the providers of care, the tools and r esources they

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46 have available to care for patients and the physical and organizational environment in ls and procedures in the diagnosis, treatment and management of patient conditions (Donabedian, 1980, p. 82). And, outcomes refer to the impact of the processes working within the structure. To view HIT usage and quality care in Donabedian's framework, ho spitals that invest in the resources to adopt EHRs (structure), provide the incentives and/or requirements to staff and physicians to use EHRs effectively (process), would see changes in the quality of care (outcome). In this model, the use of EHRs influen ce the process of care with the use of CPOE and CDSS as well as standardizing the information collected from patients such as demographic data, problem lists, medication lists etc Improved and automated processes are expected to reduce medical errors, and support and inform clinical decisions, thus improving health care quality outcomes (Chaudhry et al., 2006; Goldzweig et al., 2009; Shekelle & Goldzweig, 2009; Kazley & Ozcan, 2008). Other structural elements in hospitals can also influence the quality of care including organizational and financial structures and will need to be considered in developing a model to assess the impact of EHRs on hospital quality. Hospital size (patient volume), ownership, and especially nursing staffing ratios have been found to impact the quality of care. Stanton and Rutherford (2004) performed a review of studies funded by the Agency for Healthcare Research and Quality (AHRQ) as well as other research on the relationship of nurse staffing levels to adverse patient outcomes. They identified a broad array of research on this topic and have found several studies that

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47 have shown an association between lower nurse staffing levels and higher rates of poor patient outcomes such as pneumonia, shock, cardiac arrest, and urinary tract infections. Delone and McLean Information System Success Model structure, including EHRs, can impact the process of care delivery and quality of care. To provide an information systems perspective, the Delone and McLean Information System Success Model (D & M IS Success Model) provides a framework to examine the use of information technology (Delone & McLean, 1992). Their model is based on the pioneering research on communicati ons by Shannon and Weaver (1949) and as well as the more current work on information influence theory of Mason (1978). Shannon and Weaver (1949) addressed three levels of communication : the technical, level, the semantic level and the effectiveness level The technical level of communications is the accuracy and efficiency of the communication system, the semantic level is the success of the information in conveying the intended meaning and the effectiveness level is the effect of the information on the receiver. The original D & M IS Success Model (shown in Figure 4 1 ) posits that information of the system and erformance construct for semantic success, and the constructs of use, user satisfaction individual impacts, and organizational impacts to measure effectiveness success. functionality, availability and adaptability of the information system to the organization

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48 and def timeliness of the information to the user (Delone & McLean, 2003). Their model is grouped into three dimensions: the IS system (system quality and information quality), the actual use of the system, and the consequences of system use (individual and organizational impact). They argue that their model is a causal model and each of these steps is a necessary condition for the resultant outcomes. These dimensions align with dimension is comparable to outcomes. For example, HIT usage and quality care in the D & M IS Succe ss Model may be represented as hospitals that value, invest resources, and communicate with users regarding the adoption of EHRs (system quality and information quality), provide the training, feedback, incentives and/or requirements to use components of EHRs (use of the system), would see changes in the way the practitioners use the technologies (individual impact) that then affect the quality of care (organizational outcome). Conceptual Model Development The proposed conceptual model for this study builds upon Donabe framework and incorporates output elements from the D & M IS Success Model to show the relationship between structure and the use of EHRs and how the use of EHRs impact the process of care which then impacts the quality of care. Figure 4 2 illustrates this study's conceptual model and explains how the variables included in the adoption of EHR system which includes hospital characteristics and EHR system functions (structure) influence the actual usage of HIT in th e delivery of health care ( process) result ing in the number of MU measures met by each hospital

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49 (Objectives 1 and 2) By meeting the MU objectives, clinicians are adhering to clinical practice guidelines and have access to structured patient data ( individual impact ) to improve info rmed decision making and therefore increase the quality of care delivered in the hospitals (organizational impact ). It is posited that this model will show that EHR enhanced health care processes will demonstrate better quality outcomes than those who do n ot have EHRs or use them ineffectively (Objective 3) This model also shows the relationship between hospital characteristics, HIT adoption and HIT use. Hypotheses H ypotheses have been articulated to evaluate the association of financial and organizatio hospitals. Several studies described in the literature review section (Chapter 3) have shown that key hospital organizational characteristics such as non profit status, system affiliation, large bed size and positive operating margins are positively associated with higher levels of EHR adoption; however, some of these findings are inconsistent, especially regarding profit status of hospitals (Fonkych & Taylor, 2005 ; Li et al., 2 008 ; Wang et al., 200 2 ) For this study, it is expected that for profit hospitals, especially those hospitals owned by large national chains, are expected to have a higher level of adoption than non profit hospitals due to their market power in negotiating competitive prices for EHR systems and a pool of expertise in implementing a national strategy for EHR adoption among their hospitals. Furthermore, with the availability of the CMS incentives, for profit hospital chains will likely take advantage of thes e resources and have a system wide approach in achieving the CMS MU objectives. Therefore, it is postulated as follows:

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50 Hypothesis 1 : For profit hospitals will be associated with a greater number of the CMS MU objectives than non profit hospitals. Hospita ls that are affiliated with hospital systems are expected to have a higher level of adoption due to economies of scale and shared technical expertise, as well as more financial resources and access to capital (Kazley & Ozcan 2007 ; Li et al., 2008) Furthe rmore, group purchasing of common systems and interoperability are more likely to occur in hospital systems. As discussed for the for profit chains, hospitals affiliated with a health system will likely have a system wide approach in achieving the CMS MU o bjectives Therefore it is postulated as follows: Hypothesis 2 : System affiliated hospitals will be associated with a greater number of the CMS MU objectives met than independent hospitals. ogy. Larger organizations tend to have more financial resources and personnel dedicated to health information technology ( Fonkych & Taylor, 2005 ; Jha et al., 2009 ; Kazley & Ozcan 2007) Larger hospitals also tend have the administrative structure in plac e to effectively monitor and manage HIT systems, including the provision of ongoing training. Therefore, it is postulated as follows: Hypothesis 3 : Large hospitals will be associated with a greater number of the CMS MU objectives met than small or medium size hospitals. Urban hospitals are generally larger than rural hospitals and have more access to specialized staff, capital, and equipment in their community ( Fonkych & Taylor, 2005 ; Jha et al., 2009 ; Kazley & Ozcan 2007). HIT vendor support and other s pecialized technical support will also be more available in the urban areas. Furthermore, local

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51 competition drives hospitals to be more technologically advanced than hospitals that have no competition. Therefore, it is postulated as follows: Hypothesis 4 : Urban hospitals will be associated with a greater number of the CMS MU objectives met than rural hospitals. Hospitals with positive operating margins have the financial resources and cash reserves for capital investments such as health information technolo gy (Wang et al., 200 2 ). They also have the funds to maintain and upgrade health information systems. Therefore, it is postulated as follows: Hypothesis 5: Hospitals with high er positive operating margins will be associated with a greater number of the CMS MU objectives. Hospitals with in house information technology staff should have the capability to implement and manage electronic medical records and will have greater ability to integrate systems within the hospital. Based on the Hersh and Wright (2008) hospital IT staff analysis, higher ratios of IT FTE per bed were found in hospitals who had achieved higher levels of HIT adoption ( 0.210 FTE bed at HIMSS Stage 4 Adoption Level). Therefore, it is postulated as follows: Hypothesis 6 : Hospitals with high er ratios of IT staff (FTE) per bed will be associated with a greater number of the CMS MU objectives. Medical staff leadership has been found to be an important catalyst in EHR implementation ( Boonstra & Broekhuis 2010; Goroll et al, 2009; Saathoff, 2005). A Chief Medical Information Officer (CMIO) on staff can provide insights on how systems can improve physician workflow and provide the leadership to the medical staff to move adoption more quickly. Therefore, it is postulated as follows:

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52 Hypothesis 7 : Hosp itals with Chief Medical Information Officers will be associated with a greater number of the CMS MU objectives met than hospitals without CMIOs. The conceptual model discussed above leads to the following hypothesis reflecting the main objective of this s tudy: is there a relationship between hospitals achieving the MU objectives and better quality of care ? If the MU objectives are good indicators of relevant use of health information technology, it is expected that meeting these objectives will have a posi impact on quality is believed to lie with clinicians having immediate access to standardized patient information and increased adherence to clinical practice guidelines which will improve the pro cess of care and therefore improve the quality of care Hypothesis 8 : Hospitals with h igher number s of MU objectives met will be associated with higher scores on Hospital Compare quality measures Based on past evidence, hospitals that are effectively us ing CPOE and CDSS are expected to have higher scores on quality measures and will also be tested individually.

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53 Figure 4 1. DeLone and McLean IS Success Model, 1992 Figure 4 2. EHR Use/Quality Model

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54 CHAPTER 5 DATA AND METHODS The primary object current EHR use in terms of the CMS Stage 1 MU objectives and whether meeting these objectives is associated with better quality of care Specifically, this study ascertain s the level of attainm ent of Florida hospit als in meeting the CMS Stage I MU objectives, identifies hospital organizational characteristics which are a ssociated with higher levels of es the association between achievement of t he MU objectives and improved patient process measures. This chapter describes the data sources and variables used for this study and the method for analysis to address the study objectives. Data Sources Due to the timeliness of the subject matter with th e release of the Stage 1 MU objectives in July of 2010, there has not been any national publicly available data regarding hospitals ability to meet the Stage 1 MU objectives. However, for a state to implement the Medicaid EHR Incentive Program, the Cente rs for Medicare and Medicaid Services (CMS) required states to submit a Medicaid Health Information Technology Plan (SMHP). The SMHP included an environmental scan to identify the health information technology baseline of Medicaid providers in the state. This environmental scan included questions regarding the hospitals current ability to meet the Stage 1 MU objectives. Other data sources used in this study include the CMS Hospital Compare database for the dependent variables and the Agency for Health Car e Administration (AHCA) Hospital Beds and Services List, the Florida Hospital Uniform Reporting

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55 System, and the American Hospital Association Annual Survey as sources for the independent variables. Florida Health Information Technology Environmental Scan surveyed acute care hospitals, federally qualified health centers (FQHCs), rural health clinics (RHCs), Regional Health Information Organizations (RHIOs)/Health Information Exchange s (HIEs), and select healthcare professionals participating in Medicaid and who may be eligible for the Medicaid EHR incentives (physicians, dentists, certified nurse midwives, and nurse practitioners). This study utilizes the survey data from the Florid a Hospital Health Information Technology survey conducted as part of a statewide HIT Environmental Scan in August 2010. This survey provides detailed information on acute care hospital EHR adoption levels in Florida including information on the type of he alth information systems hospitals have in place and how these systems are used. The survey also addresses their system, especially the ability to meet the CMS St age 1 MU guidelines for Medicare HIT incentive payments. I served as the project manager for the Environmental Scan and was responsible for the overall development, implementation and analysis of this research. The survey tool was developed by researchers at the University of Florida and University of Alabama Birmingham and implemented by the WellFlorida Council. A draft final survey was pilot tested in five Florida hospitals, including urban and rural hospitals. The pre testing was performed by administr ators and Chief Information

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56 interpretability. A copy of the 2010 Florida Hospital Health Information Technology Survey instrument is included in the Appendix. The surve y population included all 211 acute care hospitals in Florida Sp ecialty hospitals, long term care hospitals and federal hospitals (VA, military) were excluded because o nly acute care hosp itals are eligible for the CMS E H R incentive program. The survey was emailed directly to hospital CEOs and was fielded for 64 days (August 9 October 13, 2010). Hospital CEOs were asked to direct the survey to the most knowledgeable person in their institution on information technology for completion. Extensive follow up procedures, including direct emails and phone calls to hospital CEOs, led to a 76% response rate (161 hospitals). This survey was administered using an on line survey tool and the data were converted into SAS and STATA for analysis. The survey instrumen t includes several sections that are relevant to this study. The first section asked for identifying information about the hospital (hospital name, address, AHCA hospital identification number and type of hospital). The third section asked about the hospit information systems and the financial and human resources devoted to the management of these systems. The fourth section asks for detailed information about the current clinical information systems at their facility including EHRs, laboratory, pharmacy and radiology systems, the functionality of these systems and the extent to which they may meet the federal guidelines for meaningful use. The fifth section asks detailed questions on the medication management system and the type of patient alerts built into the system for the prevention of medication errors.

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57 The 2010 Florida Hospital HIT survey question response categories use ranges (0, 1 25%, 26 50%, 51 75%, 76 99%, 100%) which resulted in six of the survey questions responses not perfectly aligning with the MU objective threshold For instance, the CMS measure may specify that 80% of patients have at least one entry recorded as structured data while the Florida Hospital HIT Survey had quartile ra nges for responses. In this case, hospitals that reported 76 99% for the measure is considered to meet the CMS standard of 80%. See Table 5 1 for a crossw alk between the CMS Stage 1 MU o bjectives and the hospital survey questions. T he hospital survey dat a is used to determine how many of the Stage 1 MU measures each participating hospital has met. CMS Hospital Compare The Hospital Compare website was created through the efforts of CMS and the Hospital Quality Alliance (HQA) and includes consensus derive d set of hospital quality measures appropriate for public reporting established by the National Quality Forum. Hospital Compare displays rates for Process of Care measures for patients being treated for a heart attack, heart failure, pneumonia, asthma (chi ldren only) or patients having surgery. Hospitals voluntarily submit data on a quarterly basis about the treatments their patients receive for these conditions as part of the CMS Hospital Inpatient Quality Reporting Program. The measures selected for this study include all of the process measures for heart failure and pneumonia and are indicative of processes that can be enhanced by having an effective health information system in use. The heart failure measures are endorsed by the American College of Cardi ology/American Heart Association and incorporated in their Heart Failure Practice Guidelines (Hunt et al, 2009). The pneumonia measures are part of the Infectious Diseases Society of America/American Thoracic Society consensus guidelines on the management of

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58 community acquired pneumonia in adults (Mandell et al, 2007). The data includes all patients treated with these conditions, not just Medicare beneficiaries. The dataset used in the analysis were released on August 5, 2011 and include the reporting per iod of October 2009 through September 2010. AHCA Hospital Beds and Services List The AHCA Hospital Beds and Services List is produced quarterly by AHCA as part of their Certificate of Need hospital bed inventory process. The July 2010 AHCA Hospital Beds and Services List document was used to identify the hospitals for participation in the Florida Environmental Scan survey (AHCA, 2010). In order to accurately match the hospital characteristics information to the time of the survey, this database was used to determine hospital bed size, ownership status and geographic designation (urban or rural). Florida Hospital Uniform Reporting System Florida requires all hospitals in the state to report detailed information about its patient discharges and financial in formation. Florida Hospital Uniform Reporting System year. This data includes a basic hospital profile as well extensive financial data of es including their income statement and balan ce s heet. The 2010 database was used to obtain hospital operating margin d ata and system affiliation data and was provided by Arlene Schwahn, Office of Data Dissemination Florida Agency for Health Care Adminis tration. American Hospital Association Annual Survey The annual survey performed by the American Hospital Association contains over 800 hospital specific data items on hospitals across the country and collects data

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59 covering organizational structure, person nel, hospital services, and financial per form ance Hospitals are identified through state heath care agencies, Medicare and Medicaid participati on, and state and local associations. T he AHA Annual Survey is completed online by most hospitals in the U.S. w ith over 5 7 00 hospitals responding to the survey (85% response rate) The 2009 database was used to calculate the nurse staffing to bed ratio (AHA, 2011) There were 142 Florida hospitals reporting nurse staffing information from the 161 hospitals who par ticipated in the Florida Hospital HIT survey. Study Objective 1 The first objective was to ascertain the level of attainment of Florida hospitals in meeting the CMS Stage I MU objectives according to the guidelines provided by CMS. The Stage 1 MU objectiv es include 14 core or required objectives and 10 menu objectives. The AHCA hospital survey had questions relating to 13 of the 14 core objectives and all 10 of the menu objectives (the MU measure that required hospitals to report to CMS was excluded from the survey s ince it was not in effect yet). Th e estimated total number of MU objectives met per hospital was used to formulate the dependent variable for Study Objective 2 and used as the main predictor or explanatory variable of interest for Study Objecti ve 3. Study Objective 2 The second objective is to identify hospital organizational characteristics that are addressed as the HIT Adoption Model. The following variables inc luded in the HIT Adoption Model are described below and variable specifications are provided in Table 5 2.

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60 Dependent Variable The dependent variable is the total number of the MU objectives (MUSum) met by each hospital as reported in the 2010 Florida Hospi tal HIT Survey Independent Variables Variables that have been previously documented as influencing HIT adoption and hospital quality (as discussed in Chapter 4) are included in the analysis. These include hospital ownership status (for profit versus non profit), system affiliation, geographical location (rural versus urban), hospital size (small, medium, and large), nurse staffing to bed ratio, and operating margin. Hospital operating margin is defined as the difference between the net operating revenue and net operating expense. Additional variables obtained from the Flo rida Hospital HIT survey was tested including whether having a Chief Medical Information Officer (CMIO) or a high number of hospital IT staff influence HIT adoption. In particular, a HIT staff per bed ratio was used to provide an adequate measure across all hospitals (Hersh & Wright, 2008). Questions regarding these variables were included in the Florida Hospital HIT survey to determine if organizational structure and specialized resource s influence hospitals ability to implement HIT. Study Objective 3 The third and main objective of this study is to assess the relationship between hospitals achieving the MU objectives and quality of care and is addressed as the Quality Model. The followin g variables in Quality Model included in the analysis are described below and variable specifications are provided in Table 5 2.

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61 Dependent Variables The dependent variables or outcomes of interest include 10 hospital process measures that cover 2 clinical conditions pneumonia and heart failure. Pneumonia and circulatory conditions including heart attacks and heart failure are in the top 5 most common causes for hospital admissions patients in the U.S. for those who are 65 and older (Wier et.al, 2010). Usi ng evidence based medicine to address these issues can improve patient outcomes. The following p rocess of c are measures represent a subset of best practices for the treatment of these conditions and were selected from the database as having the most quali fied responses from hospitals surveyed in Florida. A qualified response submitted to CMS. These measures are calculated in percentage terms with the numerator as the sum of all eligible cases during the reporting period where the recommended care was provided and the denominator is the sum of all eligible cases (QualityNet, 2010). The following descriptions of measures are abbreviated descriptions from the Hospital Compare Technical Appendix (CMS, 2011). Pneumonia Initial Antibiotic Timing (PNIAT) : Pneumonia in patients receive antibiotics within 6 hours after arrival at the hospital. Appropriate Initial Antibiotic Selection (PNIAS) : Immuno competent patients with pneumonia receive an initial antibiotic regimen that is consistent with current guidelines. Pneumococcal Vaccination Status (PNPVS) : Pneumonia inpatients age 65 and older who were screened for pneumococcal vaccine status and were administered the vaccine prior to d ischarge, if indicated.

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62 Influenza Vaccination Status (PNIVS) : Pneumonia patients age 50 years and older, hospitalized during October through February who were screened for influenza vaccine status and were vaccinated prior to discharge, if indicated. Blo od Cultures Performed in the Emergency Department (PNBC): Pneumonia patients whose initial emergency room blood culture specimen was collected prior to first hospital dose of antibiotics. Smoking cessation advice/counseling (PNSC) : Pneumonia patients with a history of smoking cigarettes were given smoking cessation advice or counseling during a hospital stay. Heart Failure Evaluation of left ventricular systolic function (HFLVSF) : Heart failure patients with documentation that an evaluation of the left vent ricular systolic function was performed before arrival, during hospitalization, or is planned for after discharge. ACE inhibitor or ARB for left ventricular systolic dysfunction (HFAIARB) : Heart failure patients with left ventricular systolic dysfunction a nd without contraindications to these medications are prescribed an ACE inhibitor or an ARB at hospital discharge. Discharge instructions (HFDI) : Heart failure patients discharged home with written instructions or educational material given to patient or c are giver at discharge or during the hospital stay. Smoking cessation advice/counseling (HFSC) : Patients with a history of smoking cigarettes are given smoking cessation advice or counseling during a hospital stay. Based on CMS recommendations for the use of these data, hospitals that treated less than 25 qualified patients in a particular measure were excluded from that Independent variable The MU objectives were designed to demonstrate that proper use of electronic health records wou ld result in better patient care and include many basic functions of EHRs as well some more sophisticated uses. Many of the MU objectives are interrelated such as CPOE for medications and checks for drug drug interactions or

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63 drug allergy interactions and h ospitals use these functions together for maximum benefit. The total number of the MU objectives met by each hospital will be used as the independent variable (MUSum) Control Variables Covariates that have been previously documented as influencing HIT ado ption and hospital quality (as discussed in Chapter 4) are included as control variables. These include hospital ownership status, system affiliation, geographical location, hospital size, nurse staffing to bed ratio, and operating margin. Statistical An alysis This study was conducted using a retrospective, cross sectional design linking primary and secondary data sources with the hospital as the unit of analysis. Descriptive and bivariate analyses were performed on key hospital characteristics of hospita ls participating in the 2010 Florida Hospital HIT Survey database and include the following variables: MUSum, hospital ownership status, system affiliation, geographical location, hospital size operating margin, IT staff ratio per bed, RN staff ratio per bed and the existence of a CMIO To explore the potential for non response bias, se veral of these variables (urban/rural, bed size, ownership status, hospital affiliation) were examined to determine whether respondents differed from non respondents HIT Adoption Model An analysis was performed to examine the correlations of independent variables an d descriptive statistics were calculated using frequencies, means, percentages, and standard deviations for all variables. All 161 hospitals participating in th e 2010 Flo rida Hospital HIT Survey were included in the model. Poisson regression or n egative binomial regression techniques are considered the method s of choice for this model

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64 since the dependent variable MU objectives met is count data Both Poisson regression and negative binomial regression were performed to test model fit since the distribution is slightly over dispersed where the variance of the dependent variable exceeds the mean ( mean = 14.7 and variance of = 18.6 ) Based on similar HIT adoption models (Kazley & Ozcan, 2007; Hikmet et al., 2008; Jha et al., 2009) the specification for the HIT Adoption Model is provided below: Log e (MUSum) = 0 1( OWNERSHIP ) 2(SYSTEM) 3(RURAL) 4(SIZE) 5(NURSESTAFF) + 6(OPMARGIN) 7(CMIO) 8(ITSTAFF) Tests for linearity and model fit were performed. An analysis using the sum of only the core MU objectives was also performed since the core MU objectives must be met by all hospitals in order to be eligible for the incentives This was followed by a sensi tivity analysis that converted the outcome measure, MUSum into a binary variable using the mean as the cutoff point which provides a broader view of hospitals who are above average in achieving the MU objectives. Quality Model The Quality Model test s each CMS Hospital Compare measure individually and result s in 10 separate analyses. The distributions of the dependent variables are predominantly no n normal and skewed to the left Generalized Linea r Model (GLM) regression was chosen whic h allows analysis of n on normally distributed dependent variables where models are fitted via Maximum Likelihood estimation. There are 3 components of GLM: 1. Random Component identifies the dependent variable (Y) and specify/assume a probability distribution for it. 2. Systematic Component specifies the explanatory or independent variables.

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65 3. Link specifies the relationship between the mean or expected value of the random component and the systematic component (Dobson & Barnett, 2008) The random component is the Hospital Compar e measures which are proportional data based on a dichotomous response (yes/no) which are best represented as a binomial distribution. The systematic component is the independent variables already described in a previous section and specified below. A logi t link was specified due to the proportional nature of the outcome measures and will result in odds ratios being reported for each independent variable The general specification for the Quality Model that is the basis for each Hospital Compare Measure of interest uses the following basic model to test the hypothesis that hospitals that have achieved a greater number of the CMS MU objectives will perform better on quality of care measures. Logit ( Hospital Compare Measure ) 0 1(MUSum) 2( OWNERSHIP ) 3(SYSTEM) + 4(RURAL) 5(SIZE) 6(NURSESTAFF) 7(OPMARGIN) Depending on which H ospital Compare m easure is tested, the number of hospitals in the dataset varies due to lack of reporting or not meeting the 25 patient per hospital threshold. use in terms of the CMS Stage 1 MU objectives and whether meeting these objectives as a whole, is associated with better quality of care. It is believed that having a fully functional, integrated HIT system in use in hospitals will improve care; therefore, this study focused on how many MU objectives hospitals are able to achieve. As noted in Chapter 3, two major components of a hospital EHR system have been found to have a positive effect on quality: CPOE and CDSS. A targeted analysis was performed to

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66 specifically test the MU objectives for CPOE and CDSS to determine if there is a relationship with the CMS Hospital Compare quality measures. Additionally for each CMS Hospital Compare quality measure, there may be specific MU objectives that are more likely to influence the provision of care. A more specific analysis for two of the CMS Hospital Compare measures was performed that examined specific MU obje ctives that are thought to be influential in the process of delivering care. The pneumonia measure Initial Antibiotic Selection (PNIAS ) and the heat failure measure evaluation of the left ventricular systolic function (HFLVSF) were selected for this a nalysis and the MU objectives selected were based on the standard of care for these procedures Based on the Infectious Diseases Society of America/American Thoracic Society consensus guidelines on the management of community acquired pneumonia in adults the following MU objectives were selected for the analysis: demog raphics, vital signs, problem list, med ication list, allergy list, CPOE for medication orders, drug drug interaction checks CDSS drug form ulary ch ec k and e lectronic lab results (Mandell e t al., 2007) Rationale for the selection of these (especially weight and age) vital signs, other health problems, allergies, current medications and blood culture results are in order t o select the proper antibiotic and dosage amount and to avoid any allerg ic reactions or drug interactions. The CPOE, CDS S and drug formulary objectives are also included since they can provide guidance in the proper ordering and administration of medicat ions. Similarly, for the heat failure measure (HFLVSF), selection of MU objectives was based on the American demog raphics, vital

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67 signs, problem list, CDSS and e lectronic lab results Initial work up in th e emergency department for heart failure patients requires an initial exam (vitals, history, and problem list) and lab tests such as a complete blood count, urinalysis, serum electrolytes blood urea nitrogen, serum creati nine, fasting blood glucose lipid profile, liver function tests, and thyroid stimulating hormone (Hunt et al., 2009). Initial work up also includes a n electrocardiogram and t wo dimensional echocardiography with Doppler during initial evaluation of patients which should be identified as a recommended procedure through the CDSS as well as the other tests noted above. Sensitivity analysis was performed on the quality models by converting the quality m easures into binary variables and testing their association with MUSum and covariates. Lastl y, t ests for linearity and model fit were performed including testing for selection bias. Selection bias is a possible concern with this model since hospitals that are already high quality providers may be more likely to adopt EHRs than those hospitals th at have lower quality. The potential endogeneity problem between MUSum and the quality measures was tested using the Hausman procedure by regressing MUSum on all exogenous variables, then adding the residual as a new variable into the initial structural e quation of the quality models (Hausman, 1978).T he coefficient s for the residuals (MUSum_resid) in each of the quality measure models were not significant indicating that there is no evidence of endogeneity

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68 Table 5 1. CMS S tage 1 Meaningful Use Measures matched to Florida Hospital Survey Measures Core CMS MU Objectives Measure Hospital Survey Measure Survey R esponse C ategories 1. Record patient demographics (sex, race, ethnicity, date of birth, preferred language, date an d preliminary cause of death ) data recorded as structured data Please indicate the approximate percentage who have the following clinical information documented in an electronic record 0, 1 25%, 26 50%, 51 75%, 76 99%, 100% 2. Record vital signs a nd chart changes (height, weight, blood pressure, body mass index, growth charts for children) Over 50% of patients 2 years of age or older have height, weight, and blood pressure recorded as structured data Please indicate the approximate percentage who h ave vital signs recorded electronically and plotted over time. 0, 1 25%, 26 50%, 51 75%, 76 99%, 100% 3. Maintain up to date problem list of current and active diagnoses Over 80% of patients have at least one entry recorded as structured data Please indi cate the approximate percentage who have the following clinical information documented in an electronic record. 0, 1 25%, 26 50%, 51 75%, 76 99%, 100% 4. Maintain active medication list Over 80% of patients have at least one entry recorded as structured data Please indicate the approximate percentage who have the following clinical information documented in an electronic record. 0, 1 25%, 26 50%, 51 75%, 76 99%, 100% 5. Maintain active medication allergy list Over 80% of patients have at least one entry recorded as structured data Please indicate the approximate percentage who have the following clinical information documented in an electronic record. 0, 1 25%, 26 50%, 51 75%, 76 99%, 100% 6. Record smoking status for patients 13 years of age or older Over 50% of patients 13 years of age or older have smoking status recorded as structured data have an electronic health record & are 13 years old or older, please indicate the approximate percentage who have their smoking status recorded. 0, 1 25%, 26 50%, 51 75%, 76 99%, 100% 7. Computer provider order entry (CPOE) for medication orders Over 30% of patients with at least one medication in their medication list have at least one medication ordered through CPOE Pl ease indicate the approximate percentage who have at least one medication ordered through CPOE. 0, 1 25%, 26 50%, 51 75%, 76 99%, 100% 8. Implement drug drug and drug allergy interaction checks Functionality is enabled for these checks for the entire rep orting period systems have drug allergy interaction current computer systems have drug drug interaction checks enabled? Yes No Unsure

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6 9 Table 5 1. Continued Core CMS MU Objectives Me asure Hospital Survey Measure Survey Response C ategories 9. Implement one clinical decision support rule and ability to track compliance with the rule One clinical decision support rule implemented have at least one cli nical decision support rule (such as an alert, reminder, diagnostic support or clinical guideline) related to a high priority hospital condition? Yes No Unsure 10. Report clinical quality measures to CMS or states For 2011, provide aggregate numerator and denominator through attestation; for 2012, electronically submit measures 11. Provide an electronic copy of hospital discharge instructions on request Over 50% of all patients who are discharged from the inpatient or emergency department of an eligi ble hospital or critical access hospital and who request an electronic copy of their discharge instructions are provided with it Ability to provide patients with an electronic copy of discharge instructions at time of discharge, upon request. 0, 1 25%, 26 50%, 51 75%, 76 99%, 100% 12. On request, provide patients with an electronic copy of their health information (including diagnostic test results, problem list, medication lists, med. allergies, and for hospitals, discharge summary and procedures) Over 50% of requesting patients receive electronic copy within 3 business days Ability to provide patients with an electronic copy of their health information, upon request. 0, 1 25%, 26 50%, 51 75%, 76 99%, 100% 13. Implement capability to electronically exc hange key clinical information among providers and patient authorized entities capacity to electronically exchange information systems exchange (send or receive) clinical data or other information (e.g. clinical messages) with any of the following? List of entities 14. Implement systems to protect privacy and security of patient data in the EHR Conduct or review a security risk analysis, implement security updates as necessary, and cor rect identified security deficiencies Does your hospital conduct or review a security risk analysis and implement security updates as necessary in order to protect electronic health information? Yes No Unsure

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70 Table 5 1. Continued Menu CMS MU Objec tives Measure Hospital Survey Measure Survey Response C ategories 1. Implement drug formulary checks Drug formulary check system is implemented and has access to at least one internal or external drug formulary for the entire reporting period Do your hospi systems have drug formulary checks enabled? Yes No Unsure 2. Incorporate clinical laboratory test results into EHRs as structured data Over 40% of clinical laboratory test results whose results are in positive/negative or numerical format are incorporated into EHRs as structured data Out of all clinical lab test results ordered by an authorized provider for patients (inpatient or emergency) in your hospital, please indicate the approximate percentage for which the results are recorde d electronically. 0, 1 25%, 26 50%, 51 75%, 76 99%, 100% 3. Generate lists of patients by specific conditions to use for quality improvement, reduction of disparities, research, or outreach Generate at least one listing of patients with a specific condit ion have the capability of generating lists of patients with a g iven condition (e.g., admitted patients with a diagnosis of pneumonia)? Yes No Unsure 4. Record advance directives for patients 65 years of age or older Ov er 50% of patients 65 years of age or older have an indication of an advance directive status recorded Please indicate the approximate percentage who have an indication of their advanced directive status documented electronically. 0, 1 25%, 26 50%, 51 75%, 76 99%, 100% 5. Use EHR technology to identify patient specific education resources and provide those to the patient as appropriate Over 10% of patients are provided patient specific education resources Please indicate what approximate percentage of your total patients received patient specific education resources through an EHR or other computerized systems. 0, 1 25%, 26 50%, 51 75%, 76 99%, 100% 6. Perform medication reconciliation between care settings Medication reconciliation is performed for over 5 0% of transitions of care systems support medication reconciliation at transitions of care (such as inpatient admission, emergency admission, or discharge to Yes No Unsure

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71 Table 5 1. Co ntinued Menu CMS MU Objectives Measure Hospital Survey Measure Survey Response C ategories 7. Provide summary of care record for patients referred or transitioned to another provider or setting Summary of care record is provided for over 50% of patient transitions or referrals systems allow summary of care records to be shared (in either electronic or paper form) when patients are transitioned to other providers or care settings? Yes No Unsure 8. Submit electronic im munization data to immunization registries or immunization information systems Perform at least one test of data submission and follow up submission (where registries can accept electronic submissions) systems exchange (send or receive) clinical data or other information (e.g. clinical messages) with any of the following? (Please check all that apply) List of entities 9. Submit electronic syndromic surveillance data to public health agencies Perform at least one test of data submission and follow up submission (where public health agencies can accept electronic data) systems exchange (send or receive) clinical data or other information (e.g. clinical messages) with any of the following ? (Please check all that apply) List of entities 10. Submit electronic data on reportable laboratory results to public health agencies Perform at least one test of data submission and follow up submission (where public health agencies can accept electroni c data) systems exchange (send or receive) clinical data or other information (e.g. clinical messages) with any of the following? (Please check all that apply) List of entities

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72 Table 5 2. Description and cate gorization of v ariables Dependent Variables Description Type Identification MUSum Sum of MU objectives met Count Total number of MU objectives met by each hospital MUCore Sum of core MU objectives met Count Total number of core MU objectives met by each hospital PNIAT Pneumonia Initial Antibiotic Timing Proportional (%) Pneumonia cases who received antibiotics within 6 hrs./all eligible pneumonia cases in each hospital PNIAS Pneumonia Appropriate Initial Antibiotic Selection Proportional (%) Pneumo nia cases who received appropriate antibiotics./all eligible pneumonia cases in each hospital PNPVS Pneumonia Pneumococcal Vaccination Status Proportional (%) Pneumonia cases who has been vaccinated./all eligible pneumonia cases in each hospital PNIVS Pneumonia Influenza Vaccination Status Proportional (%) Pneumonia cases who has been vaccinated/all eligible pneumonia cases in each hospital PNBC Pneumonia Blood Cultures Performed in the Emergency Department Proportional (%) Pneumonia cases who had blood cultures done in the ED./all eligible pneumonia cases in each hospital PNSC Pneumonia Smoking cessation advice/counseling Proportional (%) Pneumonia cases who received smoking cessation advice/all eligible pneumonia cases in each hospital HFLVSF Heart Failure Evaluation of left ventricular systolic function Proportional (%) HF cases with eval. of LVSF/all eligible HF cases in each hospital HFAIARB Heart Failure ACE inhibitor or ARB for left ventricular systolic dysfunction Proportional (%) HF cases received ACEI or ARB at discharge/all eligible HF cases in each hospital HFDI Heart Failure Discharge instructions Proportional (%) HF cases with discharge instructions/all eligible HF cases in each hospital HFSC Heart Failure Smoking cessatio n advice/counseling Proportional (%) HF cases who received smoking cessation advice/all eligible HF cases in each hospital

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73 Table 5 2. Continued Independent Variables Description Type Identification MUSum Sum of MU objectives met Co unt Total number of MU objectives met by each hospital OWNERSHIP Ownership for profit or nonprofit Dummy For profit =1 Nonprofit =0 SYSTEM System affiliation Dummy Affiliated = 1 Not affiliated = 0 RURAL Geographical location Dummy Urban = 1 Rural = 0 SIZE Hos pital bed size Categorical Small =150 beds or less Medium = 151 to 350 beds Large = >351 beds NURSESTAFF Nurse staffing to bed ratio Continuous Total nurse FTEs/ beds OPMARGIN Operating Margin Continuous Net operating revenue net operating expense. CM IO Chief Medical Information Officer on staff Dummy Yes = 1 No = 0 ITSTAFF Number of IT staff employed per hospital bed Continuous Ratio of IT staff per bed

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74 CHAPTER 6 R ESULTS The results of the study are presented in three sections. The first section p rovides the hospital characteristics of Florida hospitals who responded to the HIT survey. The second section provides the results of the HIT Adoption analysis which examines key hospital organizational characteristics that may be positively associated wit h higher levels of EHR adoption The third section assess es the relationship between hospitals achieving the MU objectives as a whole and quality of care using the CMS Hospital Compare quality measures Specific MU objectives are also tested to see if th ere is a relationship to the quality measures. Descriptive Statistics The survey population included all 211 acute care hospitals in Florida and excluded specialty hospitals, long term care hospitals and f ederal hospitals (VA, military) of which 161 hospit als responded ( 76% response rate ). Table 6 1 provides the hospital characteristics of those hospitals who responded to the survey compared to the population of acute care hospitals in Florida were similar as the po pulation as a whole which indicated no need for weighting the survey results. Of those hospitals that responded to the survey, 32.9% (53) were small hospitals with 150 beds or less, 37.9% (61) were medium size hospitals with beds between 151 to 350, and 2 9.2%(47) were large hospitals with beds greater than 350. The majority of hospitals were located in urban areas (87.0%), not for profit (63 .4%) and affiliated with a hospital system (76.4%). Non respondents tended to be smaller, more rural hospitals and/ or for profit hospitals.

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75 Other sample characteristics included in the study include operating margin (mean 3%, std. deviation 12%), Information Technology (IT) staff per bed ratio ( mean 0.12 staff, st d. deviation 0.11) and nurse staff per bed r atio (1.2 staff, 0.64 st d. deviation). There were 101 hospitals (62.7%) who had responded that they had a Chief Medical Information Officer (CMIO) at their facility. To ascertain the level of attainment of CMS Stage I MU objectives among Florida hospitals (Objective 1), a histogram of the number of MU objectives met is presented in Figure 6 1. Only two hospitals reported that they are able to meet all the core MU objectives and at least 5 of the menu objectives The mean number of MU objectives met is 14.7 and the median is 17 with a m inimum of 0 and a maximum of 22 An examination of individual MU objectives depicted in Figure 6 2 show that most hospitals were able to achieve two core objective s, demographic information (94% of all hospitals surveyed ) an d security controls (94%) and 3 menu objective s: electronic lab results (96%), generate patient lists with specific conditions (94%) and generate patient specific education (93%). Hospitals were least likely to achieve the following MU objectives: elect ronic discharge instructions (20%), electronic copy of records to patients (30%), CPOE (30%), and submit electronic immunization data to registries (25%). HIT Adoption Analysis The HIT Adoption analysis examined key hospital organizational characteristics that may be positively associated with higher levels of EHR adoption A bivariate analysis was performed on the individual hospital characteristics as well as a multivariate analysis which included subcategories of some of these hospital characteristics.

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76 was performed to examine characteristics of hospitals who are achieving high levels of these objectives. Bivariate Statistics Bivariate analysis were performed using simple Poisson regr ession to test the association between the total number of MU objectives met (MUSum) and each hospital characteristic As reported in Table 6 2 t hese results indicate that there is a statistically significant positive relationship between MUSum and hospit als affiliated with a hospital system (p < 0. 0 1) hospitals located in an urban area (p< 0.01) and hospitals with a CMIO, (p < 0. 0 1). However, there were no statistically significant relationships between MUSum and hospital profit status or hospital size e xcept with medium size hospitals (p < 0. 0 1) Additionally, there was a positive statistical relationship with MUSum and hosp ital operating margin (p = 0.0 1 ) and a marginally positive relationship with nurse s taffing per bed ratio (p = 0.0 6 ) but not with th e IT staff per bed ratio. Multivariate Analysis Since the dependent or outcome variable, MUSum is a count variable whose variance was slightly greater than the mean, both negative binomial regression and Poisson regression were performed to determine model fit. The results of the two regressions were nearly identical with the likelihood ratio test of the over dispersion parameter alpha for the negative binomial regression equal to zero indicating that it is equivalent to the Poisson distribution. The LR Ch i square statistic and the Pseudo R 2 were larger for the Poisson model than the negative binomial model which led to the selection of the Poisson regression for the remainder of the analysis. The final Poisson model had a LR Chi square (10) of 59.65 and a Pseudo R 2 of 0.08.

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77 D iagnostics were performed to check the fit between the data and the assumptions of Poisson regression. The Hosmer Lemeshow test provided evidence of linearity (p=0.72) while visual inspection of the residuals versus predicted values sh owed signs of heteroskedasticity at the lower values (Figure 6 3 ). However, the Poisson regression incorporates observed heterogeneity into the Poisson distribution function, Var(y/x) = E(y/x) = m =exp(x ) where as the mean increases, the variance increa ses (unlike OLS which assumes constant variance ). The dispersion of data eases thus the errors in a Poisson regression are inherently heteroskedastic (Maddala, 1983). Table 6 3 summarizes the results of the regression analysis for the H IT Adoption model with the incidence rate ratios (IRR) reported for easier interpretation of results The results indicate several significant positive relationships including urban location ( IRR= 1.32, p<0.01 ) system affiliation ( IRR = 1.22, p<0.01 ) an d the presence of a CMIO on staff ( IRR= 1.16, p<0.01 ) These results show that urban hospitals have about 32 % or 4.7 more MU objectives met than rural hospitals holding covariates constant. Hospitals affiliated with a hospital/healthcare system have 22% or 3.2 more MU objectives met than non affiliated hosp itals. System affiliation was further categorized to examine the differences between local/regional systems and national systems. Results indicate that hospit als in a regional system have 2 0 % or 2.9 m ore MU objectives (IRR = 1.20, p<0.01) than non affiliated hospitals while hospital s in a national system have 30 % or 4.4 more MU objectives (IRR = 1.30, p<0.01) than non affiliated hospitals. Finally, hos pitals with CMIOs on staff have 16 % or 2.4 more MU objectives (IRR = 1.16, p<0.01) than hospitals without a CMIO Large hospital size and for profit

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78 ownership had negative relati onships with the number of MU objectives met, although the observed coefficients are not statistically significant. Stage 1 MU objectives are categorized as C o re or required objectives and M enu objectives For the latter, hospitals have a choice of selecting and reporting on 5 of the 10 possible measures Further analysis of the sum of the C ore MU measures as the dependent variab le (MUCore) are depicted in Table 6 4 and show only two si gnificant positive relationships: geographic location (IRR= 1.38, p=0.01), regiona l system affiliation (IRR = 1.18 p= 0.05). Unlike the original analysis, the CMIO relationship proved not to be si gnificant for the n umber of C ore MU objectives met. Sensitivity Analysis The number of MU objectives hospitals achieved rang ed from zero to 22 with a mean of 14.7 and a median of 17 Based on the mean, a cutoff point of 15 was selected to convert MUSum in to a binary variable. In order to analyze more closely what the relationships are between the high HIT adopters (those who met 1 5 or more MU objectives) and hospital characteristics both a probit and logit regression were performed after converting MUSum into a binary variable. The results between the two analyse s were very similar and the results of the logistic regression are su mmarized in Table 6.5 The results indicate several significant positive relationships including hospit al size medium hospital ( OR = 3.55, p=0.05 ), nat ional system affiliation ( OR = 6.00, p=0.04 ), and the presence of a CMIO on staff ( OR = 3.20, p=.02). This analysis differed from the original analysis with the addition of medium hospital size as a significant variable and the lo ss of regional system affiliation and urban location as a significant variable. Upon examination of the odds ratios, medium size h ospitals have 3.6 times the odds of be ing high HIT adopters (those who met 15 or more MU

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79 objectives) than small hospitals and ho spitals in a national system have 6.0 times the odds of being high HIT adopters than non system affiliated hospitals. H o spitals with CMIOs on staff are 3 2 times the odds of being high HIT adopters than hospitals without a CMIO. In comparing the MU obj ectives met by low and high HIT adopters (Figure 6 4), hospitals that are considered low adopters were least likely to achieve the following MU objectives: core objective 7 electronic discharge instructions (10%), core objective 8 electronic copy of re cords to patients (10%), core objective 9 CPOE (6%), and menu objective 7 submit electronic immunization data to registries (5%). In summary, the results of the HIT Adoption analysis indicate several significant relationships that may influence Florida hospitals level of HIT adoption including urban location, system affiliation, and the presence of a CMIO on staff. The sensitivity analysis confirmed two of these relationships, system affiliation and the presence of a CMIO on staff but not the urban ve rsus rural relationship. Additionally, the sensitivity analysis indicated that medium size hospitals (151 to 350 beds) are associated with high adoption of HIT. Di fferences in the two analyse s are based on the statistical method where Poisson regression i s examining the differences at each count level while the logistic regression examined the differences of hospitals who achieved 15 or more MU objectives versus hospitals that achieved less than 15 MU objectives Quality Measures Analysis The third and m ain objective of this study is to assess the relationship between hospitals achieving the MU objectives and quality of care using the CMS Hospital Compare quality measures The Quality Model test s each CMS Hospital Compare measure individually with HIT ado ption (MUSum) and result s in 10 separate analyses

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80 Bivariate Statistics The CMS Hospital Compare quality measures indicate the percentage that each hospital is able to achieve for all qualified patients with pneumonia or heart failure They are calculated in percentage terms with the numerator as the sum of all eligible cases during the reporting period where the recommended care was provided and the denominator is the sum of all eligible cases (QualityNet, 2010). Table 6 6 provides a description of the di stribution of each of the Hospital Compare measures which shows low variability with many of the measures having small ranges and mean percentages in the high 90s (left skewed distributions). Bivariate analysis were performed to test the association betwe en each Hospital Compare measure and the total number of MU objectives met (MUSum) As reported in Table 6 7 these results indicate that there is a statistically positive significant relationship between MUSum and four pneumonia measures: PNIAT Initial A ntibiotic Timing (p = 0.02 ), PNIAS Appropriate Initial Antibiotic Selection (p = 0.01), PNPVS Pneumococcal Vaccination Status (p = 0.0 2 ) and PNSC Smoking C essation advice/counseling (p = 0.02) A statistically significant relationship was also foun d between MUSum and one heart failure measure e valuation of left ventricular systolic function (HFLVSF) with a p value of < 0.01 Multivariate Analysis A multivariate analysis was performed on e ach CMS Hospi tal Compare measure The control variables in t hese analyses are similar to the hospital characteristics variables in the HIT Adoption model. Control variables include hospital ownership status, system affiliation, geographical location, hospital size (beds) nurse staffing to bed ratio, and operating margin The IT staff ratio and presence of CMIO were not

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81 included in the analysis since theoretically, IT staff and having a Chief Medical Information Officer should not have a direct relationship with hospi tals providing the appropriate standard of care for pneumonia and heart failure patients Furthermore, bivariate analysis with these two variables and the quality measures showed no significant correlation. The multivariate models employed GLM with a binomial family and a logit link due to the propor tional nature of the dependent variables. The dependent variables, the CMS Hospital Compare measures are calculated in percentage terms with the numerator as the sum of all eligible cases during the reporting period where the recommended care was provided and the denominator is the sum of all eligible cases. Therefore, for each case, it is a binary variable where the patient received the recommended care or they did not. Tests for linearit y and model fit showed heteroskedacity for all of the quality measu res and some non normality of residuals for a few of the models especially at the tail ends of the distribution s (see Figures A 1 through A 20 in Appendix ) T herefore robust standard errors were used because the models do not meet standard assumptions The Hosmer Lemeshow goodness of fit test provided evidence of linearity for all of the quality measures analyses ( all Chi square p values were not significant ) A summary of the results of all ten multivariate analyses of MUSum with each CMS Hospital Com pare measure are presented in Table 6 8 and show a marginally significant positive association between MUSum and three of the pneumonia quality measures: PNIAT Initial Antibiotic Timing (OR = 1.03, p = 0.08 ), PNPVS

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82 Pneumococcal Vaccination Status ( OR = 1.05, p = 0.05 ), and PNSC Smoking C essation advice/counseling (OR = 1.09, p = 0.0 6 ) One heart failure measure, HFLVSF e valuation of left ventricular systolic function ( OR = 1.08, p=0.01 ) shows a significant positive association The odds ratio in th is analysis is interpreted as one additional MU objective met by a hospital is associated with an X change in the odds of a given patient receiving the care specified by the quality measure For example, using the HFLVSF results with an odds ratio of 1.08 a hospital having one additional MU objective met result s in a n 8% increase in the odds that a given heart failure patient in that hospital will receive the prescribed standard of care for the evaluation of the left ventricular systolic function The pr obability of receiving the recommended care for HFLVSF is 98%, which is the odds of .98/(1 .98) or 49 to 1 Therefore, if we increase the odds by 8% we get 52.9 resulting in the odds of getting the recommended care for HFSLVSF is now 52.9 to 1 for a given heart failure patient The complete multivariate regression results for each measure is provided in Table s 6 9 and 6 10 and show covariates that have a significant positive impact on a majority of the quality measures : urban location for six of the measur es (PNIAS, PNPVS, PNBC, PNSC, HFAIARB, HFSC ), for profit ownership for eight of the measures (PNIAT, PNIAS, PNPVS, PNIVS, PNSC, HFAIARB, HFDI, HFSC) affiliation with a hospital system for all pneumonia measures and two heart failure measures HFLVF, HFDI) and operating margin for six of the measures (PNIAT, PNIAS, PNPVS, PNIVS, PNBC, HFLVSF ). The nurse staffing ratio has a negative significant association with all but two measures (PNPVS, HFLVSF) which implies that as the nurse staffing increases, the abi lity to m eet these measures decreases

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83 The model was revised to include the expanded versions of the control variables bed size (small, medium and large hos pitals) and system affiliation ( local and national) similar to those used in the HIT Adoption mode l and a summary of these analyses is presented in Table 6 11. It show ed a marginally significant positive association between MUSum and three of the quality measures: PNPVS Pneumococcal Vaccination Status (OR = 1.05, p = 0.10 ), and PNSC Smoking C essat ion advice/counseling ( OR = 1.09, p = 0.0 8 ) and HFLVSF e valuation of left ventricular systolic function ( OR = 1.08, p = 0.01 ) The addition of four more variables in the models may have resulted in a decrease in the power to detect significant differen ces The results of the complete regression analyses are presented in Tables 6 12 and 6 13 and show covariates that have a significant positive impact on a majority of the quality measures include : seven measures for affiliation with a regional system ( PNIAT, PNIAS, PNPVS, PNIVS, PNSC, HFLVSF, HFDI ), and affiliation with a national system for all bu t one measure ( HFAIARB ) The addition of the expanded hospital size and affiliation variables reduced some of the associations with the quality measures but s ystem affiliation (whether regional or nationa l) remained strongly predictive of quality Sensitivity Analysis Sensitivity analysis was performed on the quality models by converting the quality measures into binary variables and testing their association with MUSum and covariates Two separate analyses were performed using different cutoff points One analysis examined only those who reported 100% on the quality measure and the other analysis used those who reported 99% or above on the measure. The 100% c utoff point analysis led to four of the ten quality measures having less than 20 cases positive for this measure and three of the logistic regression models did not achieve model

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84 significance (Prob> Chi 2 of 0.05) due to separation Separation occurs when o ne or more predictors perfectly predict the outcome. Therefore the cutoff point of 99% or above was used. The results of the analysis are provided in Table 6 14 The results showed a significant positive association between MUSum a nd three of the qualit y measures: PNIAT ( OR = 1.19, p = 0.07 ), HFLVSF ( OR = 1.12, p = 0.06 ) and HFAIARB ( OR = 1.18, p = 0.02) and a negative association with HFDI ( OR = 0.89, p = 0.06). The regression model for HFSC did not achieve model significance due to a separation issue with two variables. A targeted analysis was performed to specifically test the MU objectives related to CP OE and CDS S to determine if there is a relationship with the Hospital Compare quality measures. Table 6 15 shows that CPOE has a significant posit ive association with two quality measures, PNIAT ( OR = 1.69, p< 0.01 ) and PNIAS ( OR = 1.52, p= 0.01) while CDSS has a negative association with PNIVS ( OR = 0.58 p= 0.09) and HFDI ( OR = 0.40, p= 0.01). Specified Models Two quality measures were selected for a more detailed analysis: initial a ntibiotic s election for Pneumonia (PNIAS) and evaluation of the left ventricular systolic function (HFLVSF). Particular MU objectives were identified for each quality measure and summed into one variable to maintain power in the regression analysis. Tables 6 16 and 6 17 provide the results for these analyses. For the PNIAS model, the specified MU objectives show a significant positive association with the PNIAS quality measure ( OR = 1.07, p value = 0.03). However, the HLVSF specified model did not show a significant association with the quality measure even though the original analysis with all of the MU objectives showed positive results.

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85 Table 6 1 Survey respondents and population c haracteristics Hospital Chara cteristic All Florida acute care hospitals (N=211) Survey respondents (N=161) Survey non respondents (N=50) Location Urban 85.3% 87.0% 80.0% Rural 14.7% 13.0% 20.0% System Affiliation Affiliated 75.8% 76.4% 68.2% Not Aff iliated 24.2% 23.6% 31.8% Size Small (150 beds or less) 35.2% 32.9% 40.1% Medium (151 to 350 beds) 40.0% 37.9% 43.6% Large (>350 beds) 24.8% 29.2% 16.3% Ownership For profit 42.9% 36 .6% 38.1% Non profit 57.1% 63 .4% 51.9% Chief Medical Information Officer Yes Not available 62.7% Not available No Not available 37.3% Not available Other Hospital Characteristic s (mean) Operation Margin 0.03 ^ 0.03 0.03 IT staff per bed ratio Not availa ble 0.12 Not available Nurse staff per bed ratio 1.28 ^ 1.20 1. 32 ^Sample sizes vary due to missing data (N= 187 for operation margin, N= 178 for n urse staff per bed ratio)

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86 Table 6 2 Bivariate statistics: hospital c haracteristics by MUSum Hospital Characteristic MuSum (mean) P Value Location Urban 15.36 <0.0 1 Rural 10.47 System Affiliation Affiliated 15.46 <0.0 1 Not Affiliated 12.36 Size Small (150 beds or less) 13.24 Medium ( 151 to 350 beds) 16.43 <0.0 1 Large (>350 beds) 14.81 0.65 Ownership For profit 15.26 0.48 Non profit 14.56 Chief Medical Information Officer Yes 15.8 <0.0 1 No 12.9 Hospital Characteristic Correlation ( r ) P value Operation Margin .209 0.0 1 IT staff per bed ratio .109 0. 1 8 Nurse staff per bed ratio .156 0.0 6 Note: bivariate analysis were done with Poisson regression except for operating margin, IT staff ratio and Nurse staff ratio (Pearson cor relation)

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87 Table 6 3. Poisson regression e stimates for Total MU objectives m et (MUSum) Two models were performed one with system affiliation (yes/no) and one with system affiliation i n 3 categories (none, regional, national). Variable IRR Std. Error P Value Size Small (150 beds or less) Reference Medium (151 to 350 beds) 1.05 0. 07 0. 51 Lar ge (>350 beds) 0.93 0.0 7 0. 31 Ownership Non profit Reference For profit 0.99 0.1 0 0.9 2 System Affiliation Not Affiliated Reference Affiliated 1.22 0.07 <0.0 1 Regional 1.20 0. 08 <0.01 National 1.30 0. 13 <0.01 Location Rural Reference Urban 1.32 0.1 3 <0.0 1 Chief Medical Information Officer No Reference Yes 1.16 0.06 <0.0 1 Operation Margin 1. 06 0.2 2 0.8 0 IT staff per bed ratio 1.01 0. 23 0.9 7 Nurse staffing to bed ratio 1.04 0.04 0.3 2

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88 T able 6 4 Poisson regression e stimates for core MU objectives m et (MUCore) Variable IRR Std. Error P Value Size Small (150 beds or less) Reference Medium (151 to 350 beds) 1.04 0. 10 0. 65 Large (>350 beds) 0.91 0.0 9 0. 32 Ownership Non profit Reference For profit 0.9 7 0.1 3 0. 81 System Affiliation Not Affiliated Reference Affiliated 1.20 0.10 0.02 Regional 1.18 0. 10 0.0 5 National 1.23 0. 16 0.11 Location Rural Reference Urban 1.3 8 0.1 8 0.0 1 Chief Medical Information Officer No Reference Yes 1. 09 0.0 8 0. 22 Operation Margin 1.0 8 0. 30 0. 79 IT staff per bed ratio 0 99 0 .32 0.9 9 Nurse staffing to bed ratio 1.04 0.05 0. 49 Two models were performed one with system affiliation (yes/no) and one with system affiliatio n in 3 categories (none, regional, national).

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89 Table 6 5 Logistic r egressi on e stimates for b inary MUSum Variable OR Std. Error P Value Size Small (150 beds or less) Reference Medium (151 to 350 beds) 3 5 5 2 33 0. 05 Large (>350 beds) 0. 68 0. 44 0. 55 Ownership Non profit Reference For profit 1 48 1 29 0. 65 System Affiliation Not Affiliated Reference Affiliated 3.04 1.50 0.03 Regional 2 39 1 31 0. 11 National 6 00 5 16 0.0 4 Location Rural Reference Urban 1. 3 2 1 06 0. 73 Chief Medical Information Officer No Reference Yes 3 20 1 56 0.0 2 Operation Margin 0 36 0. 70 0. 60 IT staff per bed ratio 1 8 29 57 72 0. 36 Nurse staffing to bed ratio 1. 1 0 0. 37 0. 78 Two models were performed one with system affiliation (yes/no) and one with system affiliation in 3 categories (none, regional, national).

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90 Table 6 6 Dependent variable c haracteristics Variable N Mean Minimum Maximum Std. Dev. Total MU objectives met (MUSum) 161 14.72 0 22 4.32 Total Core MU objectives met (MUCore) 161 8.26 0 13 2.60 Pneumonia Initial Antibiotic Timing (P N IAT) 127 95.64 74 100 4.6 2 Pneumonia Appropriate Initial Antibiotic Selection (PNAIS) 127 94.56 71 100 5.08 Pneumonia Pneumococcal Vaccination Status (PNPVS) 127 95.50 36 100 7.50 Pneumonia Influenza Vaccination Status (PNIVS) 127 94.13 47 100 8.10 Pneumonia Blood Cult ures Performed in the Emergency Department (PNBC) 127 96.28 77 100 4.19 Pneumonia Smoking cessation advice/counseling (PNSC) 123 99.30 81 100 2.17 Heart Failure Evaluation of left ventricular systolic function (HFLVSF) 128 98.44 76 100 4.15 Heart Fa ilure ACE inhibitor or ARB for left ventricular systolic dysfunction (HFAIARB) 122 95.93 81 100 4.88 Heart Failure Discharge instructions (HFDI) 128 90.95 40 100 10.77 Heart Failure Smoking cessation advice/counseling (HFSC) 102 99.55 95 100 2.40

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91 Table 6 7 Bivariate statistics: Hospital Compare m easures and MUSum Dependent Variable Coeff. Robust Std. Error P Value PNIAT 0.05 0.02 0.02 PNAIS 0.05 0.02 0.01 PNPVS 0.10 0.04 0.02 PNIVS 0.06 0.04 0.11 PNBC 0. 03 0.02 0.15 PNSC 0.18 0.07 0.02 HFLVSF 0.16 0.04 <0.0 1 HFAIARB 0.04 0.02 0.10 HFDI 0.03 0.03 0.35 HFSC 0.03 0.06 0.61 Table 6 8 Summary of GLM regression estimates for Hospital Compare measures Dependent Va riable OR Robust Std. Error P Value PNIAT 1.03 0.02 0.08 PNAIS 1.02 0.02 0.19 PNPVS 1.05 0.03 0.05 PNIVS 1.02 0.03 0.40 PNBC 1.01 0.02 0.45 PNSC 1.09 0.05 0.06 HFLVSF 1.08 0.03 0.01 HFAI ARB 1.03 0.03 0.22 HFDI 1.00 0.02 0.92 HFSC 1.01 0.06 0.86 Note: These results represent 10 separate statistical models one for each quality measure All GLM regressions used a binomial distribution with logit l ink.

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92 Table 6 9 GLM regression estimates for Pneumonia quality measures Variable PNIAT PNIAS PNPVS PNIVS PNBC PNSC MUSum 1.03 (0.02)* 1.02 (0.02) 1.05 (0.03)** 1.02 (0.03) 1.01 (0.02) 1.09 (0.05)* Urban Location 1.27 (0.39) 1.98 (0.42)*** 2.47 (1.11)** 1.91 (0.76) 2.02 (0.45)*** 7.90 (4.65)*** Size (Beds) 1.00 (0.00)** 1.00 (0.00) 1.00 (0.00) 1.00 (0.00) 1.00 (0.00)** 1.00 (0.00)** For profit Ownership 1.96 (0.37)*** 1.54 (0.26)** 2.02 (0.75)* 2.06 (0.76)* 1.42 (0.41) 5.45 (3.47 )** Affiliation with Hospital System 1.66 (3.97)** 2.01 (0.33)*** 2.46 (0.75)*** 2.44 (0.71)*** 1.55 (0.40)* 4.45 (2.09)*** Operation Margin 6.84 (1.97)*** 2.93 (1.81)* 16.32 (15.9)*** 7.13 (5.47)** 3.71 (2.51)** 0.51 (1.09) Nurse staffing to bed ratio 0.83 (0.07)** 0.81 (0.08)** 0.83 (0.12) 0.77 (0.07)** 0.79 (0.11)* 0.67 (0.15)* Results reported as Odds Ratio (robust std error) Significance levels: p < 0.10, ** p < 0.05, *** p < 0.01 Table 6 10 GLM regression estimates for heart failure quality measures Variable HFLVSF HFAIARB HFDI HFSC MUSum 1.08 (0.03)** 1.03 (0.03) 1.00 (0.02) 1.01 (0.06) Urban Location 2.10 (1.14) 2.12 (0.68)** 1.02 (0.55) 4.13 (2.76)*** Size (Beds) 1.00 (0.00) 1.00 (0.00) 1.00 (0.00) 1.00 (0.00) For profit Ownership 1.18 (0.55) 2.64 (0.81)*** 2.17 (0.63)** 9. 6x10 5 (5.0x10 6 )*** Affiliation with Hospital System 2.74 (1.13)** 1.41 (0.36) 2.22 (0.54)*** 1.78 (1.14) Operation Margin 26.56 (0.01)** 3.94 (4.70) 1.74 (1.72) 0.82 (1.46) Nurse staffing to b ed ratio 0.72 (0.16) 0.81 (0.09)* 0.70 (0.08)*** 0.48 (0.06)*** Results reported as Odds Ratio (robust std error) Significance levels: p < 0.10, ** p < 0.05, *** p < 0.01

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93 Table 6 11 Summary of GLM regression estimates for Hospital Compare measures (ex panded model) Dependent Variable OR Robust Std. Error P Value PNIAT 1.02 0.02 0.28 PNAIS 1.01 0.02 0.42 PNPVS 1.05 0.03 0.10 PNIVS 1.01 0.03 0.60 PNBC 1.01 0.02 0.77 PNSC 1.09 0.05 0.08 HFLVSF 1.08 0.03 0.01 HFAIAR B 1.03 0.03 0.20 HFDI 1.00 0.02 0.86 HFSC 1.00 0.06 0.99 Note: These results represent 10 separate statistical models one for each quality measure

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94 Table 6 12 GLM regression estimates for Pneumonia quality measures (expanded model) Variable PNIAT PNIAS PNPVS PNIVS PNBC PNSC MUSum 1.02 (0.02) 1.01 (0.02) 1.05 (0.03)* 1.01 (0.03) 1.01 (0.02) 1.09 (0.05)* Location Rural Reference Reference Reference Reference Reference Reference Urban 1.16 (0.35) 1.82 (0.46)** 1.88 (1.02) 2.04 (1.14) 1.34 (0.41) 4.43 (3.05)** Size Small (150 beds or less) Reference Reference Reference Reference Reference Reference Medium (151 to 350 b eds) 0.87 (0.18) 1.04 (0.22) 1.11 (0.44) 0.77 (0.34) 1.01 (0.30) 1.16 (1.49) Large (>350 beds) 0.69 (0.13)* 0.87 (0.17) 0.92 (0.37) 0.66 (0.27) 1.05 (0.33) 1.14 (0.70) Ownership Non profit Reference Reference Reference Reference Reference Reference For profit 1.26 (0.33) 1.14 (0.22) 1.47 (0.92) 1.38 (0.92) 0.94 (0.43) 3.71 (2.42)** System Affiliation Not Affiliated Reference Reference Reference Reference Reference Reference Affiliated Regional 1.45 (0.31)* 1.82 (0.33)*** 2.17 (0.74)** 2.16 (0.61)* 1.30 (0.30) 4.25 (2.02)*** National 3.07 (0.83)*** 2.99 (0.79)*** 4.43 (2.81)** 4.20 (3.01)** 3.05 (1.53)** 20.79 (21.73)*** Operation Margin 7.61 (5.12)*** 2.84 (1.84) 14.65 (17.42)** 7.62 (6.91)** 3.67 (3.64) 0.45 (0.92) Nurse staffing to bed r atio 0.85 (0.08)* 0.84 (0.08)* 0.85 (0.12) 0.79 (0.07)** 0.78 (0.12) 0.71 (0.16) Results reported as Odds Ratio(robust std error) Significance levels: p < 0.10, ** p < 0.05, *** p < 0.01

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95 Table 6 13 GLM regression estimates for heart failure quality m easures (expanded model) Variable HFLVSF HFAIARB HFDI HFSC MUSum 1.08 (0.03)*** 1.03 (0.03) 1.00 (0.02) 1.00 (0.06) Location Rural Reference Reference Reference Reference Urban 1.26 (0.74) 2.16 (1.00)* 1.27 (0.71) 5.36 (5.33)*** Size Small (150 beds or less) Reference Reference Reference Reference Medium (151 to 350 b eds) 2.72 (1.12)** 1.00 (0.41) 0.83 (0.27) 1.00 (1.13) Large (>350 beds) 2.65 (1.13)** 1.03 (0.38) 0.74 (0.22) 0.78 (0.85) Ownership Non profit Reference Reference Reference Reference For profit 1.04 (0.65) 2.57 (1.38)* 1.53 (0.61) 2 3 x10 5 (1.4x10 5 ) *** System Affiliation Not Affiliated Reference Reference Reference Reference Affiliated Regional 2.36 (0.96)** 1.39 (0.40) 2.05 (0.54)* 1.67 (1.07) National 4.06 (2.82)** 1.43 (0.81) 3.11 (1.25)** 4 .2 x10 5 (2.6x10 5 )*** Operation Margin 24.38 (29.91)*** 3.86 (4.43) 1.84 (1.79) 0.87 (1.66) Nurse staffing to bed ratio 0.91 (0.21) 0 .81 (0.09)* 0.72 (0.08)* 0.50 (0.07)*** Results reported as Odds Ratio(robust std error) Significance levels: p < 0.10, ** p < 0.05, *** p < 0.01

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96 Table 6 14 Logistic regression estimates for Binary CMS hospital quality measures 99% and above Varia ble OR Std. Error P Value PNIAT 1.19 0.11 0.07 PNAIS 1.19 0.14 0.13 PNPVS 1.04 0.08 0.66 PNIVS 1.02 0.06 0.76 PNBC 1.10 0.07 0.17 PNSC 1.06 0.08 0.42 HFLVSF 1.12 0.07 0.06 HFAIARB 1.18 0.09 0.02 HFDI 0.89 0.05 0.06 HFSC 1.04 0.09 0.68 Regres sion model not significant Table 6 15 CPOE and CDSS a nalysis with CMS hospital quality measures CPOE CDSS Variable OR Std. Error P Value OR Std. Error P Value PNIAT 1.69 0.30 <0.01 0.96 0.23 0.86 PNAIS 1.52 0.23 0.01 1.08 0.25 0.73 PNPVS 1.24 0.29 0. 35 0.58 0.20 0.11 PNIVS 1.09 0.26 0. 72 0.58 0.19 0.09 PNBC 0.94 0.17 0.7 2 0.84 0.19 0.44 PNSC 1.43 0.68 0.46 0.89 0.55 0.86 HFLVSF 1.26 0.43 0.49 1.25 0.51 0.58 HFAIARB 1. 41 0.36 0.18 1.19 0.55 0.70 HFDI 1.39 0.32 0.16 0.40 0.14 0.01 HFSC 0.53 0.31 0.28 1.05 1.07 0.88 Note: GLM regressions using a binomial distribution with logit link.

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97 Table 6 16 Specified model for PNIAS Variable OR Std. Error P Value PNIAS Specific MU o bjectives 1.07 0.03 0.03 Urban Location 1.85 0.38 <0.0 1 Size (Beds) 1.00 0.00 0.30 For Profit Ownership 1.55 0.26 0.01 Affiliation with Hospital System 1.95 0.33 <0.0 1 Ope ration Margin 3.15 1.93 0.06 Nurse staffing to bed ratio 0.80 0.07 0.01 Note: GLM regressions using a binomial distribution with logit link. Table 6 17 Specified model for HFLVSF Variable OR Std. Error P Value HFLVSF Specific MU o bjectives 1.1 2 0.17 0.45 Urban Location 2.71 1.36 0.05 Size (Beds) 1.00 0.00 0. 16 For profit Ownership 1. 31 0. 68 0. 60 Affiliation with Hospital System 3 22 1 .3 9 0.0 1 Operation Margin 24 39 28 66 0.0 1 Nurse staffing to bed ratio 0.8 5 0. 1 8 0. 45 Note: GLM regressions using a binomial distribution with logit link.

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98 Figure 6 1. Total n umber of MU o bjectives met by Florida hospitals Figure 6 2. Percent of each MU o bjective s met by Florida hospitals

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99 Figure 6 3. Residual versus fitted plot after Poisson regression Figure 6 4. Percent of each MU Objective met by low and high HIT Adopters

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100 CHAPTER 7 DISCUSSION AND CONCL USIONS Summary and Interpretation of Results The primary objective of this study is to asse use in terms of the CMS Stage 1 MU objectives and whether meeting these objectives is associated with better quality of car e. The specific objectives included determining the lev el of attainment of MU objectives among Florida hospitals ; identify ing hospital ; and assess ing the relationship between hospitals achieving the MU objectives and quality of care. MU Objectives Met Based on the Flor ida Hospital Health Information Technology Survey which was performed during August/September 2010 immediately after the release of the CMS Stage 1 MU Objectives requirements only two hospitals at that time reported that they were able to achieve all of t he core measures and at least five of the menu measures to (Table 7 1) The mean number of MU objectives met by Florida hospitals participating in the survey was 14.7 and the median was 17 with a mini mum of 0 (1 hospital) and a maximum of 22 (two hospitals) Over 90% of the hospitals reported that they had basic EHR features in use including the ability to store demographic information and laboratory results, generate patient lists with specific condi tions, and have security controls in their HIT systems. Hospitals were least likely to achieve the following MU objectives: the ability to produce electronic discharge instructions and an electronic copy of records to patients computerized physician orde r entry and submit electronic immunization data to

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101 registries This data provides a glimpse into the phased n ature of HIT adoption in hospitals and the need for a comprehensive strategy for widespread adoption and use of health information technology. Sin ce this survey was implemented, CMS has released information on providers who met the MU objectives and received the Medicare EHR incentive payments for 2011 (CMS, 2012). In Florida, forty seven out of 211 eligible hospitals qualified to receive the incen tive payments including one of the two hospitals that reported that they were able to meet the MU objectives in the 2010 Florida HIT Survey. Many of the hospitals who reported meeting at least 15 MU objectives (mean number) in the survey were able to qua lify for the first year incentive payment. Of the hospitals that qualified 89% were located in an urban area, 85% were affiliated with a hospital system (mostly in a national system such as HCA or HMA ) and 68% were for profit organizations (Table 7 2) H IT Adoption The HIT Adoption analysis examined key hospital organizational characteristics that may be associated wi th higher levels of MU objectives met Previous studies have examined levels of HIT adoption in hospitals or examined a limited number of t he MU objectives that were based on interpretation of older data that were similar to the MU objectives but little primary research has been done to date. This analysis uses 2010 survey data that specifically questioned hospitals about their ability to m eet the CMS Stage 1 MU objectives. The results indicate several significant positive relationships including urban location of hospitals aff iliation with a hospital system and the presence of a CMIO on staff System affiliation was further categorized t o examine the differences between

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102 local/regional systems and national systems. Results indicate that hospitals in a regional system have 20% more MU objectives than non affiliated hospitals while hospitals in a national system have 30% more MU objectives t han non affiliated hospitals. These findings are consistent with the hypotheses while o ther organizational characteristics tested were not related to MU objectives met ( hospital size, ownership status, operating margin, and IT staff ratio). Furthermore, the results from the CMS EHR incentive program participation in 2011 for Florida hospitals confirm that urban hospitals and those affiliated with a hospital system especially a national system, are more likely to meet the MU Stage 1 MU objectives (Table 7 2) Previous research supports the findin g that urban hospitals are likely to be more advanced in the implementation of health information technology (Fonkych & Taylor, 2005; Jha et al. 2009; Kazley & Ozcan, 2007) Urban hospitals are generally larger t han rural hospitals and have more access to trained IT staff, capital, and equipment. A wide variety of HIT vendors are also more available in the urban areas as well as vendor technical support Hospitals that are part of a hospital system, whether regio nal or national, are more likely to have a greater number of MU objectives met than stand alone hospitals. This is consistent with previous research that examined hospital system affiliation including the positive impact that these systems have even on ru ral hospitals ( Hikmet et al., 2008; Kazley & Ozcan, 2007; Li et al., 2008; Wang et al., 2002 ). It is speculated that hospitals in multi hospital systems have an advantage over independent hospitals in HIT capacity because of the greater availability of cap ital and group purchasing access to shared HIT resources and technical expertise available throughout the system

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103 The positive association of the presence of a CMIO in meeting the CMS MU objectives is a unique finding to this study and a new contribution to the literature. Previous studies have shown that physician buy in and input in the EHR selection and ( Boonstra & Broekhuis 2010; Goroll et al, 2009; Saathoff, 2005) but no othe r studies have been identified that examined the p resence of CMIOs in hospitals and their influence in successful HIT adoption or meeting the CMS MU objectives CMIOs that provide strong physician leadership and first hand knowledge of the flow of clinica l information can play an integral part in the effective adoption of EHRs by physicians in hospitals The sensitivity analysis that was performed examined the hospital characteristics of high achievers in meeting the MU objectives ( at least 15 or more obje ctives ). The analysis confirmed two of the original findings, affiliation with a hospital system and the presence of a CMIO on staff but not the urban location of a hospital. Additionally, the sensitivity analysis indicated that medium size hospitals (151 to 350 beds) are associated with high adoption of HIT. This finding is consistent with hospitals affiliated with a national system since the majority of these hospitals are medium size community hospitals (151 to 350 beds) Previous studies have shown tha t l arger organizations tend to have more health information technology Fonkych & Taylor ( 2005 ) found that hospitals with greater than 100 beds had more HIT capabilities than smaller hospitals while Kazley & Ozcan ( 2007) found that larger hospitals (tho se that exceeded the mean number of 229 hospital beds in the U.S.) had higher rates of HER adoption Impact on Quality The third objective of this study is to assess the relationship between hospitals achieving the Stage 1 MU objectives and quality of care using the CMS Hospital

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104 Compare quality measures for pneumonia and heart failure. The analysis provided mixed results with three of the six pneumonia quality measures and one of four heart failure measure s show ing a significant positive, a lthough weak asso ciation with MUSum (number of MU objectives met) They include PNIAT Initial Antibiotic Timing PNPVS Pneumococcal Vaccination Status, PNSC Smoking Cessation advice/ counseling, and HFLVSF E valuation of Left Ventricular Systolic F unction The CMS H ospital Compare quality measures had low variability and were highly skewed to the left with mean values in the 90% range which makes it difficult to detect large or significant differences of the effect of EHR adoption on hospital quality. However, these results are consistent with prior research including McCullough et al. (2010) whose findings showed a statistically significant relationship between EHR adoption levels and higher rates of pneumococcal vaccination. Jones et al. (2010) found that hospital s that maintained a basic EHR over a three year period had statistically significant improvements in their quality scores for heart failure, AMI and pneumonia measures as compared to hospitals with no EHRs. Additionally, Kazley and Ozcan (2008) study sh owed a statistically significant positive relat ionship for hospitals with EHRs as compared to hospitals without EHRs for one of the heart failure measures the assessment of left ventricular function for congestive heart failure patients. A s ensitivity a nalysis was performed by converting the quality measures into binary variables (cutoff point at 99%) and testing their association with MUSum and covariates. The results confirmed two of the four significant results in the original quality model (PNIAT and HFLVSF ) and showed a significant positive association between MUSum and one additional quality measure : HFAIARB

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105 Further analysis of two major components of HIT, CPOE and CDSS were tested to evaluate their impact on the hospital quality measures The r esults show that CPOE has a significant positive association with two q uality measures, PNIAT a nd PNIAS while CDSS has a significant negative association with PNIVS and HFDI The MU objective for CPOE is specifically for medication ordering which explains how it can be positively associated with the pneumonia quality measures for initial antibiotic timing (PNIAT) and selection (PNIAS) The CDSS MU objective is very broad and only measures whether the hospital has at least one clinical decision support rul e in place (such as an alert, reminder, diagnostic support or clinical guideline). Furthermore, there is a wide variety of sophistication and capabilities of CDSS applications in hospitals and physicians may not always comply with patient care recommendat ions provided by CDSS which may impact the analysis Overall, this study provided select evidence that using EHRs in the manner described by the CMS MU objectives may improve the process of care delivered in hospitals and ultimately may improve the quali ty of care. More evidence is needed, especially as hospitals move to the CMS Stage 2 MU objectives which are more rigorous and require a higher level of use of EHRs. Limitations Several study limitations are noted. First, data collected through self repo rted surveys may not accurately reflect the extent that EHRs and other health information technology are actually used in the hospitals that participated in the survey. Survey responses by hospital administration were estimates and could not be verified. Additionally, some of the survey questions regarding the extent of achieving a MU

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106 objective did not precisely match the thresholds delineated in the CMS Stage I MU requirements. Second, not all hospitals adopt and implement HIT applications in the same m anner and the study only looked at what was in use at the time of the state HIT survey. It is likely that some of the hospitals surveyed had adopted EHRs several years ago and are more proficient in using them while other hospitals may have only recently i nstalled EHRs and are still in the implementation phase The Florida Hospital HIT Survey did not capture the temporal nature of HIT adoption or implementation process which can vary greatly by hospital. However, by measuring the use of EHRs versus the pre sence of an EHR system mitigates this issue. Third, there may be many other organizational, environmental, or patient related variables that were not included in this study that may have influenced the dependent variables, both with the HIT Adoption Model and Quality Model. For instance, if hospitals are implementing EHRs because of an unobserved variable, such as local competition, or have other quality improvement initiatives underway that may influence the outcomes of pneumonia and heart failure patients may bias the findings of this study Furthermore, EHR use is not randomly assigned among hospitals and it is possible that hospitals that are high HIT adopters are also high quality care providers regardless of their HIT use. Fourth, the survey represent ed 161 hospitals which limited the power of the statistical analysis and the number of variables that could be included in the analysis. Furthermore, the low variability of the CMS quality measures may affect the ability to detect statistically significan t differences between hospitals.

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107 Lastly, the study was based on a sample of Florida hospitals and may not be representative of all U S hospitals. However, Florida is one of the most populated states in the country and has a large number of hospitals and a high percentage of Medicare patients. Policy Implications Understanding the factors or characteristics that may influence hospital adoption of EHRs can guide policymakers in the distribution of funding to incentivize providers to adopt and meaningfully u se EHRs. As demonstrated in this study as well as what has been reported by CMS for the first year of the EHR incentive program for Florida hospitals urban hospitals and hospitals affiliated with health systems, especially national systems are more likely to be meeting the Stage1 MU objectives (Table 7 2) It is apparent that the national hospital chains have made implemen ta t ion of certified EHRs at their facilities a strategic and financial priority. However, this also demonstrates the need for addition al resources and technical assistance to unaffiliated hospitals, especially rural hospitals that may not have the financial or technical ability to implement EHRs. Without additional assistance, there will be the potential to widen the digital divide. The federal government has allocated some additional funding for rural hospitals, especially critical access hospitals, to receive additional resources from the Regional Extension Centers which were set up across the country to predominately assist physicians in the selection and implement ation of EHRs However, no specific funds have been ear marked for hospitals that a re not critical access hospitals that may need additional assistance to meet the MU objectives especially independent hospitals Furthermor e, these hospitals may lack both the financial and human resources in the

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108 lo ng term to maintain and upgrade systems once the incentive program has expired. CMS should consider a tiered payment structure whereby hospitals that do not have a n EHR during the initial phase of the program receive additional years to catch up to be able to fully participate in the incentive program or where they can qualify for the program if they meet only a subset of the MU objectives. From a managerial perspective, t he comple xity of H IT implementation not only requires significant financial investment b y hospitals but it also invol ves many levels of personnel, system interaction training, and management Hospitals reported that they were least likely to achieve the CPOE MU objective as well as providing electronic discharge instructions and providing electronic copies of medical records to patients. T he CPOE objective has been cited to be a major hospital challenge since it not only requires sophisticated HIT systems but it crosses numerous hospital departments and levels of hospital staff, including physicians. The implementation of CPOE requires significant training, testing and monitoring to ensure proper use of this effective tool where the benefits to patient safety and quality is well established (Kuperman and Gibson, 2003; Ammenswerth et al., 2008; Reckmann et al., 2009). Additionally, t his study suggests that h aving physician leadership especially a CMIO may lead to more successful implementation and use of EHRs an d increase the ability to meet the CMS MU objectives One of the dangers of the EHR incentive program is hospitals rushing into the decision in purchasing an EHR without thorough analysis of how the system works and how it fits with the current workflow. H aving physician leadership in the process of selection and implementation of an EHR provides

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109 well as provide a champion/thought leader for the adoption of HIT by the med ical staff. As the U.S. embarks on the early stages of EHR adoption th is study has shown a modest impact of the CMS Stage 1 MU objectives on hospital quality. It is believed that as more hospitals become gr eater. CMS has published the proposed rule for the Stage 2 MU criteria in March, 2012 which hospitals will be required to implement by 2014 (CMS, 2012). CMS is raising the bar or threshold for most measures and moving some of the optional or menu objecti ves to core or required objectives Stage 2 contains most of the existing Stage 1 core and menu objectives while adding new objectives for patient access to health information (to engage patients and families in their healthcare) and increasing expectatio ns for health information exchange and da ta transfer among providers The Stage 2 objectives also significantly changes the CDSS objective from a requirement to use one decision support "rule" to the use of clinical support "interventions" associated with a high priority health conditions. This stronger objective may have a greater impact on outcomes and allow for better measu rement in future analysis of CDSS Furthermore, it is believed that higher thresholds in the Stage 2 objectives w ill provide better evidence of improved care as demonstrated by the two tiered study performed by Spencer Jones and his colleagues (2012) for the CPOE MU measure and its corresponding Stage 1 and Stage 2 thresholds. Overall, the Stage 2 MU objectives are expected to be a better the access of timely health information by providers and patients, reduce duplication of effort, and enhance the provision of higher quality of care.

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110 In conclusion, s ome researchers and policy makers have questioned the national effort to mandate the use of EHRs and the massive financial commitment to make it a reality especially due to the lack of consistent evidence that such technology impr oves the quality of care or reduces healthcare cost s (Hoffman & Podgurski, 2011; Himmelstein et al., 2010, DesRoches, et al., 20101). However, there is strong evidence to support that health information technology has been associated with several important benefits, including the potential to improve patient safety through electronic prescribing, and improve adherence to evidence based care through clinical decision support (Ammenswerth et al., 2008; Damiani et al., 2010; Garg et al., 2005; Reckmann et al, 2009). This study addresses an important gap in the literature, especially regarding the CMS MU objectives and their implementation as a path to standardize and measure the impact on patient care and adds to the evidence regarding the effect of health inf ormation technology use on health outcomes. Additional analysis that is being considered for this data set is to group MU objectives into major policy priorities or constructs such as testing all of the MU objectives related to medication safety and admin istration or all MU objectives related to exchanging information either from the provider to the patient or provider to provider with the CMS quality measures This analysis may provide insight on what are the major factors driving the increase in quality for these measures. Future research should include an analysis of all hospitals in the U.S. that have qualified for the CMS EHR incentive program studies under the Stage 1 MU criteria and test their performance on the CMS Hospital Compare process measures followed by a similar analysis for the Stage 2 criteria to see if there are improvements over time.

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111 Additionally, to address the phased nature of HIT adoption studies that examine the leng th of time certain HIT functionalities such as CPOE or EHRs are in place are needed to measure if long term adoption and use bring about improvements in qual ity measures over time. Furthermore, due to the high proportions reported in the CMS Hospital Compare measures as well as the emphasis on process versus outcome s with these measures other quality measures such as readmission rates, mortality due to medication errors, and patient satisfaction need to be investigated Finally more research is necessary to understand the mechanism on how EHRs can improve care and reduce costs in order to provide guidance to hospitals and policy makers as they implement a nationwide, interoperable health information system

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112 Table 7 1 Florida hospitals meeting meaningful use 2010 Survey Results Number Hospitals who reported ach ieving all core objectives 2 Hospitals who reported achieving at least 5 menu objectives 136 Hospitals who met all requirements for meaningful use 2 Table 7 2 Florida hospitals receiving CMS EHR incentive payments 201 1 Hospital Characteristi c Hospitals receiving payments (n=47) All Eligible Hospitals (n=211) Location Urban 89% 85% Rural 11% 15% System Affiliation Affiliated 85% 76% Not Affiliated 15% 24% Ownership For profit 68% 43% Non profit 32% 57% Source: CMS Hospital Recipients of Medicare EHR Incentive Program Payments thru Dec. 31, 2011 Retrieved at www.cms.gov/Regulations and Guidance/Leg islation/EHRIncentivePrograms/ DataAndReports.html

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113 APPENDIX A F LORIDA HOSPITAL INFO RMATION TECHNOLOGY S URVEY AHCA Hospital Information Technology Survey 2010 Thank you for taking the time to complete this survey. Before you begin, please read th e following introductory information: order to understand their current Information Technology (IT) systems and plans for acquiring, implementing, up grading and achieving meaningful use of certified Electronic Health Record (EHR) technology. Hospital systems that include multiple acute care facilities should complete one survey for each acute care facility, but need not complete the survey for non acu te care facilities. Unless otherwise specified, all questions should be answered in reference to the IT systems and services provided by the inpatient and emergency departments of this acute care facility only and not outpatient specific IT systems or ser vices. This survey is designed to be completed by the person(s) who is (are) most knowledgeable of the Officer (CMIO), or other relevant administrator

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114 P ART ONE Respondent and Hospital Information Hospital Name: _______________________________________________________________ County in which hospital resides: ________________________________________________ AHCA Hospital Number: _______________________________________________________ Yes Yes No Respondent Name: _______________________________________________________________ Respondent Email: _______________________________________________________________ Respondent Phone #: ______________________ _______________________________________ Respondent Position: _____________________________________________________________ CEO CIO IT Director CFO COO CMIO CNO Other Please specify. ____________________ Pag e 2

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115 PART TWO ARRA Electronic Health Record (EHR) Incentive Payment Program s The American Recovery and Reinvestment Act (ARRA) will offer Medicare and Medicaid incentive payments beginning in Fiscal Year (FY) 2011 (October 1, 2010 September 30, 2011) t o hospitals that meet certain criteria related to the acquisition, implementation and meaningful use of Electronic Health Records (EHRs). Hospitals may be eligible for incentives through both Medicare and Medicaid. Medicaid EHR Incentive Program Hospita ls for which 10% of encounters (defined as inpatient discharges plus emergency department visits) are attributable to Medicaid patients will be eligible for Medicaid incentives. In FY 2011, is your hospital likely to meet this 10% Medicaid patient volume? Yes No Unsure Does your hospital plan to seek EHR incentives through the Medicaid program? Yes No Unsure IF PRIOR QUESTION = NO SKIP TO PAGE 6 Page 3

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116 Based on your current knowledge of the incentive program rules, in what fiscal year is your hospital most likely to first quality for Medicaid Stage 1 incentives (please select only one)? In their first year of participation in the Medicaid program, hospitals can qualify in multiples ways, in cluding acquiring, have been specified by CMS, with a certified EHR. FY 2011 FY 2012 FY 2013 FY 2014 FY 2015 FY 2016 Hospital is not likely to meet the qualifying requirements in any of the above years. How is your hospital most likely to qualify for Medicaid incentives in its first year of participation? By acquiring and installing certified EHR technology By implementing (commencing ut ilization) certified EHR technology By upgrading (expanding) currently implemented certified EHR technology By attesting to the Stage 1 Meaningful Use measures with an existing EHR Unsure Page 4

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117 Using the scale pr ovided, please rate the level of concern your hospital has for each item as it relates to its ability to meet EHR Meaningful Use criteria. Very Concerned Not at all concerned Unsure of which system(s) to purchase / how to upgrade curr ent systems Implementation costs for hardware and software Decreased productivity Lack of available expertise to support systems implementation processes Workflow management concerns Information security and privacy issues Lack of interoperability between information systems Lack of staff acceptance and support for EHR or other clinical systems Internet access or connectivity issues Lack of EHR value in terms of improved clini cal quality and outcomes Lack of EHR value in terms of improved operational or administrative efficiency User training costs Please specify any other concerns: _____________________________________________________________________ ___________________ ______________________ ______ _____________________________________________________________________________________ _______________________________ SKIP TO PAGE 7 Page 5

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118 Why does your hospital plan NOT to seek i ncentives through the Medicaid EHR incentive payment program? (Please select all that apply) Was unaware of the program Do not expect to meet patient volume eligibility requirements Unsure of what system(s) to purchase / how to upgrade current systems V alue of moving to meaningful EHR use is insufficient to offset costs Other (please check box and specify below) ____________________________________________________________ Page 6

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119 Medicare EHR Incentive Program All acute care hospitals, including Cri tical Access Hospitals (CAHs), that serve Medicare patients in the state of Florida will be eligible for Medicare incentives. To first qualify for Medicare incentives, hospitals must attest to achieving the Stage 1 "Meaningful Use" objectives which have b een specified by CMS, with a certified EHR. Based on your current knowledge of the incentive program rules, in what fiscal year is your hospital most likely to achieve the Stage 1 objectives for the first time (please select only one)? FY 2011 FY 2012 FY 2013 FY 2014 FY 2015 FY 2016 Hospital is not likely to meet Stage 1 objectives in any of the above years. IF PRIOR QUESTION = Hospital is not likely to meet Stage 1 objectives in any of the above years SKIP TO PAGE 9 Page 7

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120 Using the scale provided, please rate the level of concern your hospital has for each item as it relates to its ability to me et EHR Meaningful Use criteria. Very Concerned Not at all Concerned Not at all concerned Unsure of what system(s) to purchase / how to upgrade current systems Implementation costs for hardware and software Decreased productivity Lack of available expertise to support systems implementation processes Workflow management concerns Information security and privacy issues Lack of interoperability between information systems Lack of staff acceptance and support for EHR or other clinical systems Internet access or connectivity issues L ack of EHR value in terms of improved clinical quality and outcomes Lack of EHR value in terms of improved operational or administrative efficiency User training costs Please specify any other concerns: _______________________ _________________________________________________________________________________________ _________________________________________________________________________________________________________________ SKIP TO PAGE 10 Page 8

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121 Why d oes your hospital plan NOT to seek incentives through the Medicare EHR incentive payment program? (Please select all that apply) Was unaware of the program Unsure of what system(s) to purchase / how to upgrade current systems Value of moving to meaningf ul EHR use is insufficient to offset costs Other (please check box and specify below) ____________________________________________________________ Page 9

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122 lans, expectations, or concerns relating to the ARRA EHR Medicaid or Medicare incentive programs, or about the questions in this section, please share them here: ___________________________________________________________________________________ __________ _________________________________________________________________________ ___________________________________________________________________________________ ___________________________________________________________________________________ _________________ __________________________________________________________________ ___________________________________________________________________________________ ___________________________________________________________________________________ ________________________ ___________________________________________________________ ___________________________________________________________________________________ ___________________________________________________________________________________ _______________________________ ____________________________________________________ __________________________________________________________________________ _________ Page 10

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123 PART THREE: Information Technology ( IT ) I nfrastructure and Planning How ma ny total IT FTEs are employed by or contracted by your hospital? _____________ Does your hospital have a Chief Medical Information Officer (CMIO) or another employee with a clinical background who participates in the selection, development and implementa tion of health IT? Yes No, but we are planning to develop such a role in the next year No, and we are NOT planning to develop such a role in the next year ning, conferences or workshops) related to clinical information technology? Yes No, but we are planning to develop these budget items in the next year No, and we are NOT planning to develop these budget items in the next year Unsure Does y of care information technology? Yes No, but we are planning to develop these budget items in the next year No, and we are NOT planning to develop these budget items in the next year Unsure of care information technology? Yes No, but we are planning to develop these budget items in the next year No, and we are NOT plannin g to develop these budget items in the next year Unsure Page 11

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124 What change do you clinical systems, including EHRs? Increase Decrease No change Unsure Does your hospital have an IT planning committee? Yes No, but we are planning to develop in the next year No, and we are NOT planning to develop in the next year Unsure Does your hospital have a current IT strategic plan? Yes No, but we are planning to develop in the next year No, and we are NOT planning to develo p in the next year Unsure IF PRIOR QUESTION DOES NOT = YES SKIP TO PAGE 1 4 Page 12

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125 guidance from administrators who also developed the overall strategic plan)? Yes No Unsure Page 13

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126 three IT priorities over the next three years? 1. _______________________________________________ _____________ 2. _____________________________________________________________ 3. _____________________________________________________________ Page 14

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127 If you have any additional comments about IT personnel, plannin g, infrastructure, or about the questions in this section, please share them here: ___________________________________________________________________________________ ___________________________________________________________________________________ _______ ____________________________________________________________________________ ___________________________________________________________________________________ ___________________________________________________________________________________ ______________ _____________________________________________________________________ ___________________________________________________________________________________ ___________________________________________________________________________________ _____________________ ______________________________________________________________ ____________________________________________________________________ __ ______ ______ Page 15

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128 PART FOUR Clinical Information System Functionality and Use Please answer the questions in Part 4 by thinking about computer systems installed in this emergency departments only Do not consider any outpatient services or services provided by affiliated clinics or facilities unless you are explicitly asked about these in a particular question. Which of the following computerized ancillary systems are currently implemented (either independently or integrated) in your hospital? (Please select all that apply) Radiology Pharmacy La boratory To what extent are electronic access and review of radiology reports currently implemented for clinicians to use across your hospital? Not in Place and Not Considering Implementing Do Not Have Resources But Considering Implementin g Have Resources to Implement in the Next Year Beginning to Implement in at Least One Department Fully Implemented in at Least One Department Fully Implemented Across All Departments IF PRIOR QUESTION DOES NOT = Fully Implemented in at Least One Departm ent Fully Implemented Across All Departments SKIP TO PAGE 1 8 Page 16

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129 ter systems store laboratory results using LOINC (Logical Observation Identifier Names and Codes), or are they able to convert laboratory results to LOINC? LOINC is a universal standard for identifying clinical laboratory results. Yes No Unsure Page 17 Some hospitals operate in a hybrid paper electronic environment where clinicians can choose to document or review information on paper or electronically. In these cases, electronic systems are only used some of the time. Please estimate the approximate percentage of encounters (inpatient and emergency depa rtment) for which electronic access and review of radiology reports are actually used by clinicians when available. 0% (not used for any relevant encounters) 1 25% 26 50% 51 75% 76 99% 100% (Used for all relevant encounters)

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130 IF PRIOR QUESTION DOES NOT = Fully Implemented i n at Least One Department Fully Implemented Across All Departments SKIP TO PAGE 20 Page 18 To what extent is electronic nursing documentation currently implemented for clinicians to use across your hospital? Not in Place and Not Considering Implementing Do Not Have Resources But Consider ing Implementing Have Resources to Implement in the Next Year Beginning to Implement in at Least One Department Fully Implemented in at Least One Department Fully Implemented Across All Departments

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131 Page 19 Some hospitals operate in a hybrid paper electronic environment where clinicians can choose to document or review information on paper or electronically. In these cases, electronic systems are only used some of the time. Please estimate the approximate percentage of encounters (inpatient and emergency department) for which electronic nursing documentation is actually used by clinic ians when available. 0% (not used for any relevant encounters) 1 25% 26 50% 51 75% 76 99% 100% (Used for all relevant encounters)

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132 IF PRIOR QUESTION DOES NOT = Fully Implemented in at Least One Department Fully Implemented Across All Departments SKIP TO PAGE 22 Page 20 To what extent is electronic physician documentation c urrently implemented for clinicians to use across your hospital? Not in Place and Not Considering Implementing Do Not Have Resources But Considering Implementing Have Resources to Implement in the Next Year Beginning to Implement in at Least One Departmen t Fully Implemented in at Least One Department Fully Implemented Across All Departments

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133 Page 21 Some hospitals operate in a hybrid paper electronic environment where clinicians can choose to document or review information on paper or electronically. In these cases, electronic systems are only used some of the time. Please estimat e the approximate percentage of encounters (inpatient and emergency department) for which electronic physician documentation is actually used by clinicians when available. 0% (not used for any relevant encounters) 1 25% 26 50% 51 75% 76 99% 100% (Used f or all relevant encounters)

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134 IF PRIOR QUESTION DOES NOT = Fully Implemented in at Least One Department Fully Implemented Across All Departments SKIP TO PAGE 24 Page 22 To what extent is eMAR (electronic Medication Administration Record) currently implemented for clinicians to use across your hospital? Not in Place and Not C onsidering Implementing Do Not Have Resources But Considering Implementing Have Resources to Implement in the Next Year Beginning to Implement in at Least One Department Fully Implemented in at Least One Department Fully Implemented Across All Departments

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135 Page 23 Some hospitals operate in a hybrid paper electronic environment where clin icians can choose to document or review information on paper or electronically. In these cases, electronic systems are only used some of the time. Please estimate the approximate percentage of encounters (inpatient and emergency department) for which eMAR (electronic Medication Administration Record) is actually used by clinicians when available. 0% (not used for any relevant encounters) 1 25% 26 50% 51 75% 76 99% 100% (Used for all relevant encounters)

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136 support or clinical guideline) related to a high priority hospital condition? Yes No Unsure ems have the capability of generating lists of patients with a given condition (e.g., admitted patients with a diagnosis of pneumonia)? Yes No Unsure ctronic or paper form) when patients are transitioned to other providers or care settings? Yes No Unsure Page 24

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137 Does your hospital currently maintain at least some patient information in an electronic record? Yes No IF PRIOR QUESTION = NO SKIP TO PAGE 29 Page 25 imate percentage of your total patients received patient specific education resources through an EHR or other computerized systems. 0% (patient specific education resources generated for no patients) 1 25% 26 50% 51 75% 76 99% 100% ( patient specific ed ucation resources generated for all patients)

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138 Page 26 OUT OF ALL OF YOUR HOSPITAL please indicate the approximate percentage who have the following clinical information documented in an electronic record. 0% (not documented for any patients) 1 25% 26 50% 51 75% 76 99% 100% (Docume nted for all patients) Problem lists Medication allergy lists Demographic lists Medication lists

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139 Page 27 Instructions Please answer the following 5 questions by referring only to patients (inpatient and emergency department) for whom your hospital maintains an electronic record. Do not consider patients for wh om no information is maintained electronically. ) WHO HAVE AN ELECTRONIC RECORD, please indicate the approximate percentage who have vital signs recorded electronically and plotted over time. 0% (not documented for any patients who have electronic records) 1 25% 26 50% 51 75% 76 99% 100% (Documented for all patients who have electronic records) (INPATIENT AND EMERGENCY DEPARTMENT) WHO HAVE AN ELECTRONIC RECORD, please indicate the approximate percentage who have an indication of their advanced directive statu s documented electronically. 0% (not documented for any patients who have electronic records) 1 25% 26 50% 51 75% 76 99% 100% (Documented for all patients who have electronic records

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140 Page 28 WHO HAVE AN ELECTRONIC RECORD AND ARE 13 YEARS OLD OR OLDER, please indicate the approximate percentage who have their smoking status recorded. 0% (not documented for any patients who have electronic records) 1 25% 26 50% 51 75% 76 99% 100% (Documented fo r all patients who have electronic records) which the following system functionality is currently supported. Ability to provide patie nts with an electronic copy of discharge instructions at time of discharge, upon request 0% (not supported for any patients who have electronic records) 1 25% 26 50% 51 75% 76 99% 100% (supported for all patients who have electronic records) OUT OF AL which the following system functionality is currently supported. Ability to provide patients with an electronic copy of their health information, u pon request 0% (not supported for any patients who have electronic records) 1 25% 26 50% 51 75% 76 99% 100% (supported for all patients who have electronic records)

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141 O UT OF ALL CLINICAL LAB TEST RESULTS ORDERED BY A N AUTHORIZED PROVIDER FOR PATIENTS (INPATIENT OR EMERGENCY) IN YOUR HOSPITAL, please indicate the approximate percentage for which the results are recorded electronically. Does your hospital conduct or review a security risk analysis and implement security updates as necessary in order to protect electronic health information? Yes No, but we are planning to do so i n the next year No, and we are NOT planning to do so in the next year Unsure Pa ge 29 0% (no results recorded electronically) 1 25% 26 50% 51 75% 76 99% 100% ( all resul ts recorded electronically ) is with the computer systems across the remainder of the hospital. Not applicable (no computer systems in this department) Not interfaced with any systems Interfaced with at least one systems Interfaced with all other hospital departments Emergency department Laboratory Pharmacy Radiology Billing/Patient registration Outpatient services

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142 Page 30 select only one) Best of b reed the hospital selects different HIT vendors based on their ability to meet the specific needs of a given hospital department. Best of suite the hospital selects vendors that emphasize integration among suites of applications (e.g., financial a pplications, clinical applications, revenue cycle applications), each suite of which is purchased from a different vendor. Single vendor all or most of the HIT systems at the hospital are supplied by a single vendor. Self developed / home grown technology Unsure Has your hospital recently changed (or plan to change) its information technology adoption strategy in response to the ARRA EHR meaningful use incentive program? Yes changed (or plan to change) to best of breed Yes changed (or plan to change) to best of suite Yes changed (or plan to change) to single vendor Yes changed (or plan to change) to self developed / home grown technology No Unsure

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143 Please sp no more than three vendors from whom you have purchased the most technology): Allscripts GE Healthcare NextGen Healthcare Information Systems Cerner Hea lthcare Mgmt Sys Prognosis Healthcare Information Systems CPSI Healthland Siemens Eclipsys Home grown/locally developed SOWSIA Healthcare Solutions, Inc. Epic Systems McKesson Other (please check box and specify below) eClinicalW orks Meditech __________________________________ Page 31

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144 r about the questions in t his section, please share them here: ___________________________________________________________________________________ ___________________________________________________________________________________ ____________________________________________________ _______________________________ ___________________________________________________________________________________ ___________________________________________________________________________________ ___________________________________________________________ ________________________ ___________________________________________________________________________________ ___________________________________________________________________________________ __________________________________________________________________ _________________ ___________________________________________________________________________________ ___________________________________________________________________________________ ________________________________________________________________________ __________ Page 32

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145 PART FIVE MEDICATION ORDERING AND ADMINISTRATION SYSTEMS IF PRIOR QUESTION DOES NOT = Fully Implemented in at Least One Department Fully Implemented Across All Departments SKIP TO PAGE 36 Page 33 To what extent is CPOE (Computerized Provider Order Entry) currently implemented for clinicians to use across y our hospital? Not in Place and Not Considering Implementing Do Not Have Resources But Considering Implementing Have Resources to Implement in the Next Year Beginning to Implement in at Least One Department Fully Implemented in at Least One Department Full y Implemented Across All Departments

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146 OUT OF ALL OF YO THEIR MEDICATION LIST, P lease indicate the approximate percentage who have at least one medication ordered through CPOE? Page 34 Some hospitals operate in a hybrid paper electronic environment where clinicians can choose to document or review information on paper or electronically. In these cases, electronic systems are only used some of the time. Please estimate the approximate percentage of encounters (inpatient and emergency department) for which CPOE (Computerized Provider Order Entry) is actually used by clinicians when available. 0% (not used for any relevant encounters) 1 25% 26 50% 51 75% 76 99% 100% (Used for all relevant encounters) NA (None of our Patient information is in an EH R) 0% (CPOE used for no patients) 1 25% 26 50% 51 75% 76 99% 100% (Used for all relevant encounters)

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147 drug allergy interaction checks enabled? Yes No Uns ure drug drug interaction checks enabled? Yes No Unsure drug formulary checks enabled? Yes No Unsure Page 35

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148 Yes No Unsure Do your hospit coding, Radio Frequency Identification)? Yes No Unsure omated dispensing devices? Yes No Unsure Page 36

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149 questions in this section, please share them here: ___________________________________________________________________________________ ___________________________________________________________________________________ ___________________________________________________________________________________ ______ _____________________________________________________________________________ ___________________________________________________________________________________ ___________________________________________________________________________________ _____________ ______________________________________________________________________ ___________________________________________________________________________________ ___________________________________________________________________________________ ____________________ __________________________________________ ________ ______ ______ Page 37

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150 PART SIX : Health Information Exchange (HIE) Page 38 r information (e.g. clinical messages) with any of the following? (Please check all that apply) Other affiliated hospitals Public health agency for syndromic surveillance Other unaffiliated hospitals Public health agency for reportable diseas es Affiliated outpatient practices or clinics Regional Health Information Organization (RHIO) Non affiliated outpatient practices or clinics Patients (e.g., via online portal, Personal Health Record or other) Commercial Laboratories D evice registry Retail Pharmacies Other (please check box and specifiy below): _______________________________________ Immunization registries (e.g., Florida SHOTS) None do not exchange data with any of the above Does a Regional Health I nformation Organization (RHIO) exist in your area? A RHIO is an organization that brings together health care stakeholders within a defined geographic area and governs the electronic exchange of health related information among providers for the purpose of improving health care. Yes No Unsure Would your hospital be interested in collaborating with a RHIO so that health information about your patients can be exchange d with other providers for treatment at the point of care? Yes (not curr ently collaborating with a RHIO) No Yes (currently collaborating with a RHIO) Unsure

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151 Page 39 If there is no RHIO in your area, would your hospital be interested in collaborating with a state level health in formation exchange so that health information about your patients can be exchanged with other providers for treatment at the point of care? Yes No Unsure Using the scale provided for each item, please indicate the level of concern your hospita l has regarding each item as it relates to sharing information electronically through a Health Information Exchange (HIE). Very Concerned Not at all concerned Current computer systems do not support HIE Limited staff resources to use HIE Limited broadband access / connectivity Patient data security and privacy breach concerns Practitioner privacy right to individual clinical judgment Practitioner resistance to engaging in HIE Risk of competitive disadv antage from engaging in HIE Lack of value in terms of clinical quality and outcomes Lack of value in terms of operational or administrative efficiency Managing user access rights and policies Please specify any other concerns: __ ____________________________________________________________________________________ ____________________ __________________________________________________________________________________________________________

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152 Page 40 Would your hospital be interested in receiving assistance from a Regional Extension Center (REC)? RECs are local ARRA funded centers developed to support the outreach, education, and technical assistance needed to help providers improve the quality of care they furnish by attaining or exceeding Meaningful Use criteria. Currently, four RECs are being implemented in Florida: The Center for the Advancement of Health IT, PaperFree Florida (University of South Florida), the South Flor ida Regional Extension Center, and The University of Central Florida College of Medicine. AHCA is considering funding Regional Extension Centers to help hospitals adopt, implement and upgrade certified EHR systems to be eligible for incentive payments for EHR Meaningful Use. Yes No Unsure

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153 Internet Connectivity How many Internet service connections does your hospital subscribe to? 0 connections 1 connection 2 connections More than 2 connections IF PRIOR QUESTION = SKIP TO PAGE 46 Page 41

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154 Please answer the next three questions about the Internet service connection your hospital is subscribed to. ( If your hospital has multiple connections, please describe the fastest of those connections .) What type of service is provided by this connection? (please check one) Dial up through a standard analog telephone connection. DSL Asymmetric with different ad vertised data transfer throughput rates upstream and downstream. DSL Symmetric with identical advertised data transfer throughput rates upstream and downstream. Other Copper Wireline, such as Ethernet over copper or T 1 dedicated lines. Cable modem, DOCS IS 3.0 that uses the latest version of Internet Protocol and allows the subscriber to use multiple downstream and upstream channels simultaneously. Cable modem, other than DOCSIS 3.0, that includes all other types of Internet service offered by a cable pro vider. Optical carrier or fiber that connects directly to the facility (does not include "fiber to the curb"). Satellite where information is beamed from a satellite to a dish at the facility. Unlicensed terrestrial fixed wireless that is point to point w ireless broadband transmitted from a tower on an unlicensed frequency. Licensed terrestrial fixed wireless that is point to point wireless broadband transmitted from a tower on a licensed frequency. Terrestrial mobile wireless that is broadband service thr ough a cellular/mobile telephone network. Electric power line, also known as broadband over power line (BPL) that is Internet service over existing low and medium voltage electric power lines. Don't know Other (please check box and specify below) ___ _________________________________________________________ Page 42

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155 Which of the following hospital functions are serviced by this connection (please check all that apply)? Administrative/billing Clinical Data backup associated with this service connection? Upstream Connection Speed Downstream Connection Speed Less than or equal to 200 kbps Greater than 200 kbps but less than 768 kbps Greater than or equ al to 768 kbps but less than 1.5 Mbps Greater than or equal to 1.5 Mbps but less than 3 Mbps Greater than or equal to 3 Mbps but less than 6 Mbps Greater than or equal to 6 Mbps but less than 10 Mbps Greater than or equal to 10 Mbps but less than 25 Mb ps Greater than or equal to 25 Mbps but less than 50 Mbps Greater than or equal to 50 Mbps but less than 100 Mbps Greater than or equal to 100 Mbps but less than 1 Gbps Greater than or equal to 1 Gbps Don't know Less than or equal to 200 kbps Greater t han 200 kbps but less than 768 kbps Greater than or equal to 768 kbps but less than 1.5 Mbps Greater than or equal to 1.5 Mbps but less than 3 Mbps Greater than or equal to 3 Mbps but less than 6 Mbps Greater than or equal to 6 Mbps but less than 10 Mbp s Greater than or equal to 10 Mbps but less than 25 Mbps Greater than or equal to 25 Mbps but less than 50 Mbps Greater than or equal to 50 Mbps but less than 100 Mbps Greater than or equal to 100 Mbps but less than 1 Gbps Greater than or equal to 1 Gb ps Don't know IF PAGE 40 QUESTION = SKIP TO PAGE 46 Page 43

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156 You indicated above that your hospital has multiple Internet service connections. Please answer the next three questions by describing the second fastest of yo What type of service is provided by this connection? (please check one) Dial up through a standard analog telephone connection. DSL Asymmetric with different advertised data transfer throughput rates upstream and downstream. DSL Symmetric with identical advertised data transfer throughput rates upstream and downstream. Other Copper Wireline, such as Ethernet over copper or T 1 dedicated lines. Cable modem, DOCSIS 3.0 that uses the latest version of Internet Proto col and allows the subscriber to use multiple downstream and upstream channels simultaneously. Cable modem, other than DOCSIS 3.0, that includes all other types of Internet service offered by a cable provider. Optical carrier or fiber that connects direct ly to the facility (does not include "fiber to the curb"). Satellite where information is beamed from a satellite to a dish at the facility. Unlicensed terrestrial fixed wireless that is point to point wireless broadband transmitted from a tower on an unli censed frequency. Licensed terrestrial fixed wireless that is point to point wireless broadband transmitted from a tower on a licensed frequency. Terrestrial mobile wireless that is broadband service through a cellular/mobile telephone network. Electric po wer line, also known as broadband over power line (BPL) that is Internet service over existing low and medium voltage electric power lines. Don't know Other (please specify) Which of the following functions are serviced by this connection (p lease check all that apply)? Administrative Clinical Data backup Page 44

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157 associated with this service connection? Upstream Connection Speed Downstream Connection Speed Less than or equal to 200 kbps Greater than 200 kbps but less than 768 kbps Greater than or equal to 768 kbps but less than 1.5 Mbps Greater than or equal to 1.5 Mbps but less than 3 Mbps Greater than or equal to 3 Mb ps but less than 6 Mbps Greater than or equal to 6 Mbps but less than 10 Mbps Greater than or equal to 10 Mbps but less than 25 Mbps Greater than or equal to 25 Mbps but less than 50 Mbps Greater than or equal to 50 Mbps but less than 100 Mbps Greater than or equal to 100 Mbps but less than 1 Gbps Greater than or equal to 1 Gbps Don't know Less than or equal to 200 kbps Greater than 200 kbps but less than 768 kbps Greater than or equal to 768 kbps but less than 1.5 Mbps Greater than or equal to 1.5 M bps but less than 3 Mbps Greater than or equal to 3 Mbps but less than 6 Mbps Greater than or equal to 6 Mbps but less than 10 Mbps Greater than or equal to 10 Mbps but less than 25 Mbps Greater than or equal to 25 Mbps but less than 50 Mbps Greater t han or equal to 50 Mbps but less than 100 Mbps Greater than or equal to 100 Mbps but less than 1 Gbps Greater than or equal to 1 Gbps Don't know Page 45

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158 If you have any additional comments about health information exchang e, Internet connectivity, or about the questions in this section, please share them here: ___________________________________________________________________________________ __________________________________________________________________________________ ___________________________________________________________________________________ ___________________________________________________________________________________ __________________________________________________________________________________ ______ _____________________________________________________________________________ ___________________________________________________________________________________ ___________________________________________________________________________________ _____________ ______________________________________________________________________ ___________________________________________________________________ ________ ______ __ Revised 8/8 /1 0 Page 46

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159 SUPPLEMENTAL STATIST ICAL DATA Figu re A 1. Q Q plot residual fitted GLM regression for PNIAT Figure A 2. Residual versus fitted plot after GLM regression for PNIAT

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160 Figure A 3. Q Q plot residual fitted GLM regression for PNIAS Figure A 4. Residual versus fitted plot after GLM regres sion for PNIAS

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161 Figure A 5. Q Q plot residual fitted GLM regression for PNPVS Figure A 6. Residual versus fitted plot after GLM regression for PNPVS

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162 Figure A 7. Q Q plot residual fitted GLM regression for PNIVS Figure A 8. Residual versus fitted plot after GLM regression for PNIVS

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163 Figure A 9. Q Q plot residual fitted GLM regression for PNBC Figure A 10. Residual versus fitted plot after GLM regression for PNBC

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164 Figure A 11. Q Q plot residual fitted GLM regression for PNSC Figure A 12. Residual versus fitted plot after GLM regression for PNSC

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165 Figure A 13. Q Q plot residual fitted GLM regression for HFLVSF Figure A 14. Residual versus fitted plot after GLM regression for HFLVSF

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166 Figure A 15. Q Q plot residual fitted GLM regression for HFAIARB Figure A 16. Residual versus fitted plot after GLM regression for HFAIARB

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167 Figure A 17. Q Q plot residual fitted GLM regression for HFDI Figure A 18. Residual versus fitted plot after GLM regression for HFDI

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168 Figure A 19. Q Q plot resi dual fitted GLM regression for HFSC Figure A 20. Residual versus fitted plot after GLM regression for HFSC

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169 L IST OF REFERENCE S Agency for Health Care Administration (AHCA) (2010). Hospital Beds and Services List July 2010. Retrieved August 2, 2010, f rom http://ahca.myflorida.com/MCHQ/CON_FA/Publications/Jul2010_HospitalBedsand ServicesList.pdf Agency for Healthcare Research and Quality ( 2012 ). Enabl ing Health Care Decisionmaking t hrough Clinical Decision Support and Knowledge Management Retrieved May 3, 2012, from: http://www.effectivehealthcare.ahrq.gov/ehc/products/278/919/EvidenceReport203 _Enabling Health Care Decisionmaking_FinalReport.pdf American Hospital Association (AHA). (2007). Continued Progress Hospital Use of Information Technology. Ret rieved October 23, 2010, from: http://www.aha.org/content/00 10/070227 continuedprogress.pdf American Hospital Association (AHA) (2011) 2009 Annual Survey of Hospitals. Chicag o, IL: Author. American Recovery and Reinvestment Act (ARRA) of 2009, Division A Title XIII. (2009). H.R.1. Retrieved January 11, 2011, from : http://www.govtrack.us/congress/billtext .xpd?bill=h111 1 Ammenwerth, E., Schnell Inderst, P., Machan, C., & Siebert, U. (2008). The effect of electronic prescribing on medication errors and adverse drug events: A systematic review. Journal of the American Medical Informatics Association,15 (5), 585 600. Anderson, G. F., Frogner, B. K., Johns, R. A., & Reinhardt, U. E. (2006). Health care spending and use of information technology in OECD countries. The United States is an outlier in both its health spending and its use of health information tec hnology. Health Affairs 25 (3), 819 831. Asch, S. M., McGlynn, E. A., Hogan, M. M., Hayward, R. A., Shekelle, P., Rubenstein, L., et al. (2004). Comparison of quality of care for patients in the Veterans Health Administration and patients in a national sa mple. Annals of Internal Medicine 141 (12), 938 945. Ash, J. S., Sittig, D. F., Dykstra, R., Campbell, E., & Guappone, K. (2009). The unintended consequences of computerized provider order entry: findings from a mixed methods exploration. International Jo urnal of Medical Informatics, 78 (suppl 1), S69 S76. health records. New England Journal of Medicine, 363 (6), 501 504.

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176 BIOGRAPHICAL SKETCH Lori Bilello received her MBA and MHS degrees from the University of Florida in 1987. After 20 years in the field of healthcare administration, community and program planning, program development and evaluation, she returned to the University of Florida to pursue her PhD in Health Services Research. Previously, she was the Executive Director of the Health Planning Council of Northeast Florida for 12 years. She also was the Director of Planning and Development for University Medical Center (now Shands Jacksonville). Her interests/focus in the doctoral program in Department of Health Services Research, Management and Policy at the University of Florida is health policy and program evaluation and is working on her dissertation that focuses on the impact of health information technology on hospital quality. As part of her research responsibilities in the doctoral program, she has been part of the Florida Medicaid Reform Pilot Project Evaluation T eam and has worked on the Organizational Analysis portion of the project as well as the CAHPS and ECHO surveys. As a consultant for the WellFlorida Council, she was the project manager for the 2010 Florida HIT Environmental Scan which resulted in providin g the primary data source for her dissertation.