Walden University ScholarWorks Walden Dissertations and Doctoral Studies 2016 Relationship Between Hospital Performance Measures and 30-Day Readmission Rates Henry M. Carter Walden University Follow this and additional works at: http://scholarworks.waldenu.edu/dissertations Part of the Business Commons This Dissertation is brought to you for free and open access by ScholarWorks. It has been accepted for inclusion in Walden Dissertations and Doctoral Studies by an authorized administrator of ScholarWorks. For more information, please contact ScholarWorks@waldenu.edu. Walden University College of Management and Technology This is to certify that the doctoral study by Henry Carter has been found to be complete and satisfactory in all respects, and that any and all revisions required by the review committee have been made. Review Committee Dr. Cheryl Lentz, Committee Chairperson, Doctor of Business Administration Faculty Dr. Howard Schechter, Committee Member, Doctor of Business Administration Faculty Dr. Reginald Taylor, University Reviewer, Doctor of Business Administration Faculty Chief Academic Officer Eric Riedel, Ph.D. Walden University 2016 Abstract Relationship Between Hospital Performance Measures and 30-Day Readmission Rates by Henry M. Carter MBA, American InterContinental University, 2009 BS, Alabama State University, 1991 Doctoral Study Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Business Administration Walden University October 2016 Abstract Medical errors occur at the prescription step due to lack adequate knowledge of medications by the physician, failure to adhere to policies and procedures, memory lapses, confusion in nomenclature, and illegible handwriting. Unfortunately, these errors can lead to patient readmission within 30 days of dismissal. Hospital leaders lose 0.25% to 1% of Medicare’s annual reimbursement for a patient readmitted within 30 days for the same illness. United States, lawmakers posited the use of health information technology, such as computerized physician order entry scores systems (CPOES), reduced hospital readmission, improved the quality of service, and reduced the cost of healthcare. Grounded in systems theory, the purpose of this correlational study was to examine the relationship between computerized physician order entry scores, medication reconciliation scores, and 30-day readmission rates. Archival data were collected from 117 hospitals in the southeastern region of the United States. Using multiple linear regression to analyze the data, the model as a whole did not significantly predict 30-day hospital readmission rate, F (2, 114) = 1.928, p = .150, R2 = .033. However, medical reconciliation scores provided a slightly higher contribution to the model (β = .173) than CPOES (β = .059. The implications for positive social change included the potential to provide hospital administrators with a better understanding of factors that may relate to 30-day readmission rates. Patients stand to benefit from improved service, decreased cost, and quality of healthcare. Relationship Between Hospital Performance Measures and 30-Day Readmission Rates by Dr. Henry M. Carter MBA, American InterContinental University, 2009 BS, Alabama State University, 1991 Doctoral Study Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Business Administration Walden University October 2016 Dedication I dedicate this doctoral study to the most beneficent and merciful Creator; without Him, nothing is possible. I also would like to thank my family: my wife Sharronda Blackman-Carter for your love and support. I would like to thank my in-laws Robert and Cleo Blackman for your support. To my children, Jontavious Carter, Kenya Carter, Shanice Carter, Myesha Carter, and Saladin Carter, I strive to be an example for you, for you are my legacy. I dedicate this doctoral study to my late mother and father, Bernice Carter and Henry C. Wallace, and to my siblings, Jeffrey Carter, Marilyn Carter, and C. Chandon Carter, and to my aunts Virginia Carter- Bennett, Marian Carter-Baxter, and Willie Lou Carter Acknowledgments I would like to thank the doctoral committee for assistance with this study: Chairperson Dr. Cheryl Lentz, Second Committee Chairperson Dr. Howard B. Schechter, URR, Dr. Reginald Taylor, and Dean Dr. Freda Turner. Dr. Lyn Walden, you are an awesome person, and I could not have asked for a more knowledgeable and caring guide. In addition, I would like to acknowledge Dr. Moussa Berete for his support throughout this study and Winston Williams and Diane Baker-King for their friendship. I would like to acknowledge my colleagues at Atlanta Technical College for their words of encouragement. Table of Contents List of Tables ...................................................................................................................... v List of Figures .................................................................................................................... vi Section 1: Foundation of the Study..................................................................................... 1 Background of the Problem ................................................................................................. 1 Problem Statement ................................................................................................................ 2 Purpose Statement ................................................................................................................. 2 Nature of the Study ............................................................................................................... 3 Method .................................................................................................................................... 3 Design ..................................................................................................................................... 3 Research Question ................................................................................................................ 4 Hypotheses ............................................................................................................................. 4 Theoretical Framework ........................................................................................................ 4 Definition of Terms .............................................................................................................. 5 Assumptions........................................................................................................................... 5 Limitations ............................................................................................................................. 6 Significance of the Study ..................................................................................................... 7 Contribution to Business Practice ....................................................................................... 7 i Implications for Social Change ........................................................................................... 7 A Review of Professional and Academic Literature ........................................................ 8 Organization of the Review ................................................................................................. 9 Application to the Applied Business Problem .................................................................. 9 General System Theory ...................................................................................................... 12 30-Days Readmission ......................................................................................................... 14 Computerized Physician Order Entry Scores (CPOES) ................................................ 28 Medication Reconciliation Scores .................................................................................... 31 Summary .............................................................................................................................. 33 Section 2: The Project ....................................................................................................... 35 Purpose Statement ............................................................................................................... 35 Role of the Researcher ....................................................................................................... 35 Participants ........................................................................................................................... 36 Research Method and Design ............................................................................................ 36 Method .................................................................................................................................. 37 Research Design .................................................................................................................. 37 Population and Sampling ................................................................................................... 38 ii Ethical Research .................................................................................................................. 41 Data Collection .................................................................................................................... 41 Description of the Data....................................................................................................... 42 The Scales of Measurement ............................................................................................... 43 Scores and Meaning ............................................................................................................ 44 Strategies Use to Address Validity ................................................................................... 45 Process for Completing Hospital Safety Survey ............................................................. 45 Availability of Raw Data ................................................................................................... 46 Data Collection Technique ................................................................................................ 46 Data Analysis Technique ................................................................................................... 47 Explanation of Data Cleaning and Screening Procedures ............................................. 49 Explanation for Addressing Missing Data ....................................................................... 49 Interpretation of Inferential Results .................................................................................. 53 Statistical Software and Version ....................................................................................... 55 Study Validity ...................................................................................................................... 55 Internal Validity .................................................................................................................. 55 External Validity ................................................................................................................. 56 iii Transition and Summary .................................................................................................... 57 Section 3: Application to Professional Practice and Implications for Change ................. 59 Introduction .......................................................................................................................... 59 Presentation of Findings ..................................................................................................... 59 Applications to Professional Practice ............................................................................... 66 Implications for Social Change ......................................................................................... 67 Recommendations for Action ............................................................................................ 67 Recommendations for Further Research .......................................................................... 68 Reflections ........................................................................................................................... 70 Summary and Study Conclusions ..................................................................................... 70 References ......................................................................................................................... 59 Appendix A: Certificate of Completion .......................................................................... 101 Appendix B: Data User Agreement ................................................................................ 102 Appendix C: IRB Approval Number ............................................................................. 105 iv List of Tables Table 1. Summary of Sources Used in the Literature Review ...........................................15 Table 2. State Stratified Sample Size at .80 and .99 Power ...............................................46 Table 3. Descriptive Statistics (N = 117) ...........................................................................68 Table 4. Required vs. Actual Stratified Minimum Sample Size at .80 ..............................69 Table 5. Regression Analysis Summary for Variables Predicting 30-Day Readmission Rate (n = 117) ............................................................................................................70 v List of Figures Figure 1. Inpatient Preventable Adverse Drug Events .............................................................36 Figure 2. Power as a Function of Sample Size ..........................................................................46 Figure 3. Normal Probability Plot (P – P) for Regression Standardized Residuals (n = 117) ...67 Figure 4. Scatterplot of the standardized residuals (n = 117) ...................................................67 vi 1 Section 1: Foundation of the Study The focus of this study was on the relationship between computerized physician order entry scores, medication reconciliation scores, and 30-day readmission rates. Hospital leaders lose revenue from Medicare when doctors readmit a patient within 30 days for the same illness (McCormack et al., 2013). The aim of the study was to provide hospital leaders with the insight that may help to reduce 30-day readmission rates and potentially improve the quality of care (Fletcher, 2013). Reducing healthcare cost improves access to healthcare for society (McNair & Luft, 2012). Background of the Problem Given the increasing healthcare costs in many countries, researchers and lawmakers focus on reducing hospital readmission as one way to improve patient outcomes and reduce the readmission rate (McHugh, Carthon, & Kang, 2010). Medicare inpatients who return to the hospital within 30 days of discharge account for $17 billion in annual Medicare spending within the United States (Shulan, Gao, & Moore, 2013). In response to this increasing cost, lawmakers in the United States developed financial penalties for hospital administrators with high readmission rates. These financial penalties have a negative impact on profitability (McCormack et al., 2013; McHugh, Berez, & Small, 2013). The Southeastern region of the United States has the highest readmissions rates compared to other regions (Anderson, Golden, Jank, & Wasil, 2012). Some hospital leaders use tools to promote efficiency and reduce bureaucracy (Shulan et al., 2013). Computerized physician order entry scores and medication reconciliation scores remain factors in determining hospitals’ profit margins (P. Lee, 2 Andrade, Mastey, Sun, & Hicks, 2014). Understanding the relationship between computerized physician order entry scores, medication reconciliation scores, and 30-day readmission could be an initial step for hospital leadership’s consideration for seeking to improve business performance and reduce financial losses. Problem Statement Hospitals’ readmissions remain a significant performance indicator and source of revenue for hospitals in the United States (Gerhardt et al., 2013). Hospital leaders lose 0.25 to 1% of Medicare’s annual reimbursement for a patient readmitted within 30 days for the same illness (McCormack et al., 2013). The general business problem is that U.S. Medicare-eligible hospital leaders experience a loss of profitability when 30-day readmissions occur. The specific business problem is that some hospitals leaders do not know the relationship between computerized physician order entry scores, medication reconciliation scores, and 30-day readmission rates. Purpose Statement The purpose of this quantitative correlational study was to examine the relationship between computerized physician order entry scores, medication reconciliation scores, and 30-day readmission rates. The independent variables included computerized physician order entry scores and medical reconciliation scores. The dependent variable was the 30-day readmission rate. The target population included Medicare-eligible hospitals located in Alabama, Georgia, and Florida. The Southeastern region of the United States has the highest readmissions rates compared to other regions 3 (Anderson et al., 2012). The implications for positive social change included the potential for the sustainability of Medicare-eligible hospitals. Nature of the Study Method The quantitative method suited the needs for this study because the purpose of this study was to analyze numerical data and to generalize findings to a larger population. The focus of a qualitative researcher is to understand the beliefs, experiences, and perspectives of study participants (Zachariadis, Scott, & Barrett, 2013). Furthermore, qualitative researchers rely on collecting and analyzing non-measurable data (Richardson, Abraham, & Bond, 2012). Therefore, the qualitative method did not suit the needs of this study. Mixed methods researchers collect and analyze both qualitative and quantitative data, which can be complex and time-consuming (Venkatesh, Brown, & Bala, 2013). Therefore, a mixed method did not suit the needs for this study. Design Researchers use correlation designs to examine relationships between variables (Sparks & Pan, 2010). A multiple linear regression designs suited the needs of this study because the focus of this study was to examine the relationship between the predictor variables, computerized physician order entry scores and medication reconciliation scores, and the dependent variable, 30-day readmission rates. Researchers recommended designs such as experimental and quasi-experimental designs when the study focus is to assess cause and effect (Handley, Schillinger, & Shiboski, 2011). The focus of this research study was to examine the strengths and direction of any relationships. Therefore, 4 an experimental or quasi-experimental design did not suit the needs of this study, because I did not attempt to influence the variables. Research Question What is the relationship (if any) between computerized physician order entry scores, medication reconciliation scores, and 30-day readmission rates for Medicareeligible hospitals in the states of Alabama, Georgia, and Florida? Hypotheses H10: There is no statistically significant relationship between computerized physician order entry scores, medication reconciliation scores, and 30-day readmission rates of Medicare-eligible hospitals in the states of Alabama, Georgia, and Florida. H1a: There is a statistically significant relationship between computerized physician order entry scores, medication reconciliation scores, and 30-day readmission rates for Medicare-eligible hospitals in the states of Alabama, Georgia, and Florida. Theoretical Framework Systems theory first appeared in the literature in 1936 (Roussel, Swansburg, & Swansburg, 2006). Von Bertalannffy (1972) theorized that general systems theory could be useful for management research. Von Bertalannffy characterized the system by the nonlinear interactions of constituent components and interactions (as cited in Walonick, 1993). Adherents of system theory look at the world as subsystems where each system includes defined boundaries (von Bertalannffy, 1972). General systems theorists and researchers often link systems thinking to the study of change management models (Roussel et al., 2006). The system theoretical framework appears appropriate for this 5 study because of the various segments of the healthcare system (Shaw, 2014). Computerized physician order entry scores and medication reconciliation scores are part of hospital systems. Therefore, the systems theory met the needs of this study as the theoretical framework. Definition of Terms Computerized physician order entry scores (CPOES): The provider’s use a computer to enter medication orders into a database (Centers for Medicare & Medicaid Services [CMS], 2014). Medication reconciliation: This concept refers to the process of comparing a patient's medication orders, to all of the medications the patient has been taking (Conklin, Togami, Burnett, Dodd, & Ray, 2014). Readmission: This concept refers to a patient returning to the hospital for a prior acute care admission within a specified time interval (Goldfield et al., 2008). Assumptions Assumptions are factors or principles that researchers accept that exist, but are without verification or evidence, and are out of the researcher’s control (Al-Habil, 2011). One assumption of this study was archival records included relevant data, which could have influenced the relationship between the variables (Lam, 2010). In addition, an assumption in quantitative research is that the results of a study using one particular group could apply to other similar groups (Al-Habil, 2011). 6 Limitations Limitations refer to the potential weakness of the study (Lam, 2010). The intent of this quantitative correlational study was to examine how two independent variables, which included CPOES and medication reconciliation scores, related to a dependent variable, which was the hospital 30-day readmission rate. Archival data from the Center for Medicare Services were appropriate for this study. Archival data are any data collected prior to the beginning of the research study (C. Jones, 2010). The weaknesses associated with this study were the usage of archival data, which included data that was collected by the standards of the university’s IRB (C. Jones, 2010) and data were collected by people who did use current data collection methodologies (Cheng, Goldschmied, Deldin, Hoffmann, & Armitage, 2015). The variables mediated or moderated the relationship between the predictor and dependent variable (Lam, 2010). Delimitations Delimitations refer to factors that define the boundary and limit the scope of a given study (Castro, Garcia, Cavazos, & Castro, 2011). The scope of this study encompassed Medicare eligible hospitals in the Southeastern United States. The following states comprise the Southeastern region of the United States: (a) Alabama, (b) Georgia, and (c) Florida (U.S. Census Bureau, 2012). The Southeastern region has the highest readmissions rates compared to other regions (Anderson et al., 2012), which is why I included these states for study. Data reflected the period of January 1, 2013 to December 31, 2013. The results of this study only apply to Medicare eligible hospitals. In addition, the results do not apply to other regions in the United States. 7 Significance of the Study The significance of this study is that the findings might give hospital administrators insight potentially to reduce 30-day readmission rates and potentially improve the quality of care. Hospital readmission remains a significant quality measure of hospitals in the United States (Gerhardt et al., 2013). The goal of this study was to gain increased understanding of the potential and critical relationship between CPOES, medication reconciliation scores, and 30-day readmission rates for Medicare-eligible hospitals in Alabama, Georgia, and Florida. This study represents an initial step in considering possible solutions to hospital readmission issues in the United States. Contribution to Business Practice Providing hospital leaders with information regarding the relationship between CPOES, medication reconciliation scores, and hospital 30-day readmission rate may improve the hospital's best practices (Bradley et al., 2012). Understanding this relationship could allow hospital leaders to develop plans to improve performance, and reduce financial losses (McNair & Luft, 2012). Medication reconciliation and CPOE scores have the potential to improve patient safety and avoid a number of medication errors (Zhivan & Diana, 2012). Implications for Social Change The implications for positive social change include the potential to provide data to hospital administrators and other hospital leaders that can aid hospital leaders to improve service to patients. The implications for positive social change include the potential for hospital administrators and other hospital officials to improve service to patients. 8 Hospital administrators may be able to reduce the cost of healthcare for Americans (McNair & Luft, 2012). Reduced readmission rates may also improve the quality of healthcare (Fletcher, 2013). A Review of Professional and Academic Literature The literature includes research on various aspects of hospital readmissions (Cornett & Latimer, 2011). This literature review includes current and previous studies that surround hospital 30-day readmission, CPOES, and medication reconciliation scores. Topics covered in this literature review include an overview of hospitals and the healthcare industry in the financial performance of hospitals in the United States, 30-day readmission, CPOES, and medication reconciliation scores. In the process of finding resources for this literature review, I used the Walden University online library and the DeKalb County Public Library in Lithonia, Georgia. Education Source Complete, Business Source Premier, SAGE full-text, and ProQuest central served as search engines in the review. Relevant keywords for this study is as follows: healthcare, hospitals, readmission, CPOES, computerized physician order entry scores, and medication reconciliation scores. The search included only peer-reviewed articles, dissertations, and seminal books. A filtered search for studies published after 2009 occurred, except for searches on persistence theories. As shown in Table 1, more than 88% of the references for this study included peer-reviewed sources published after 2010. Table 1 also displays a summary of the types of sources used in this review. 9 Table 1 Summary of Sources Used in the Literature Review Reference Type Total Less than 5 years Greater than 5 years % Peer-Reviewed Journals 89 87 2 Non Peer-Reviewed Journals 4 4 0 Dissertations 1 1 0 Books 3 2 0 Websites 3 3 0 Total 100 97 3 87% Organization of the Review The first section of this literature review includes an overview of general systems theory, which is the theoretical framework of the study. The second section includes an overview of hospitals and the healthcare industry in the United States. The third section includes the indicators and drivers of hospitals’ financial performance. The last three sections cover the three variables used in the study. The review closes with a summary of reviewed professional and academic literature. Application to the Applied Business Problem The purpose of this study was to examine the relationship between CPOES, medication reconciliation scores, and 30-day readmission. Reducing hospital 30-day readmission remains a top priority for U.S. policymakers (Gerhardt et al., 2013). Hospital 30-day readmission is an important indicator of healthcare quality and costs in the United 10 States (Fletcher, 2013). In the 21st century, CMS introduced many initiatives with the aim of reducing the cost and improving the quality of healthcare in the United States (Bradley et al., 2012). A part of these initiatives included tracking and publishing the 30day readmission rate for Medicare eligible hospitals (Bradley et al., 2012). Policymakers ruled to penalize hospitals with high readmission rates to hold hospitals accountable for their readmission rates (Shulan et al., 2013). In 2010, the U.S. Congress passed The Affordable Care Act of 2010, which allowed CMS under the Hospital Readmissions Programs to cut payment for hospitals with high readmission rate. The act started in the fiscal year 2013 (Shulan et al., 2013). The initial scope of the program was to focus on three conditions: (a) heart failure, (b) acute myocardial infarction, and (c) pneumonia (Weiss, 2013). The objective was to reduce Medicare payments by up to 1% in 2013 and up to 3% in 2015 for hospitals with readmissions rate higher than the expected risk-adjusted rate (Averill, Goldfield, & Hughes, 2013). In 2014, 2,225 hospitals among the 3,359 Medicare eligible hospitals in the United States faced $227 million reductions in Medicare payment because of high 30-day readmission (Healthcare Financial Management, 2013). Because of these policies, hospital 30-day readmission remains an important driver of hospitals’ financial performance, as well as a key performance indicator (Fletcher, 2013; Gerhardt et al., 2013). Although scholars and practitioners continue to recognize the importance of hospital 30-day readmission, many hospitals leaders continue to struggle to find the best 11 way to reduce their readmission rates (McHugh et al., 2013). The current literature lacks effective models for predicting hospital 30-day readmission (Shulan et al., 2013). Patients often return to the hospital because of the poor quality of care during the initial hospitalization; therefore, improving care quality could reduce hospital readmissions (Spaulding & Raghu, 2013). The use of CPOES could improve the quality of care, which in turn, could reduce hospital readmission (Fletcher, 2013; Spaulding & Raghu, 2013). CPOES referred to the electronic entry of a medical order by the prescriber (Spaulding & Raghu, 2013). The historical manual entry and processing by physicians were associated with various issues such as (a) lack adequate knowledge of medications by the prescriber; (b) failure to adhere to policies and procedures; (c) memory lapses; (d) confusion in nomenclature; and (e) illegible handwriting (Spaulding & Raghu, 2013). To address these issues, the leaders at Thomas Jefferson University Hospital used their information system capabilities to develop CPOES (Weiss, 2013).). The goal of this 8year project was to increase efficiency and safety at the hospital (Weiss, 2013). Following the lead by Thomas Jefferson University Hospital, other hospitals in the United States started adopting CPOES (Zhivan & Diana, 2012). The benefits of CPOES include the improvement of the overall hospital productivity (E. Ford, Huerta, Thompson, & Patry, 2011). Despite the potential benefits, various safety, cost, and adoption issues limit the success of CPOES in hospitals (Vartian, Singh, DeBakey, Russo, & Sittig, 2014; Wang & Huang, 2012; Wright et al., 2013). In addition to CPOES, medical reconciliation represents a relevant driver of care quality and hospital productivity (Laugaland, Aase, & Barach, 2012; Ripley & Vieira, 12 2013). Medication reconciliation scores refer to the process of creating a patient’s current medication list and comparing that list to the patient’s previous medications (Ripley & Vieira, 2013). In 2005, the Joint Commission on Healthcare included medication reconciliation scores as a national patient safety goal to address medication errors (Vogenberg & DiLascia, 2013). Because of the various implementation and adoption challenges, the Joint Commission on Healthcare suspended scoring hospitals on medication reconciliation scores between 2009 and 2011. Then in July 2011, the U.S. government lifted the suspension and reintroduced the third national patient safety goal (Vogenberg & DiLascia, 2013). Medication reconciliation scores have the potential to reduce hospital readmission by improving patient safety and avoiding a number of medication errors. These medication errors include (a) drug interaction, (b) drug duplications, and (c) drug omissions (Benson & Snow, 2012; J. Lee, Tollefson, Daly,& Kielb, 2013; Hoisington, 2012; Hume & Tomsik, 2014; Laugaland et al., 2012; Walker, 2012a). General System Theory As the boundaries faded between systems, general system theory attracted the attention of scholar and practitioners (Lier & Hardjono, 2011). In 1936, von Bertalannffy (1972) theorized that general system theory could be useful for management research. Von Bertalannffy characterized the system by the nonlinear interactions of constituent components and interactions. Adherents of system theory examine the world as subsystems with each system having defined boundaries (von Bertalannffy, 1972). General systems theorists and researchers often link systems thinking to the study of 13 change management models (Roussel et al., 2006). The systems theoretical framework appeared appropriate for this study, because of the various segments of the healthcare system (Shaw, 2014). System thinking entails complex linkages and outcomes between organizational activities (von Bertalannffy, 1972). Often, unique and unknown advantage points exist that modulate organizational staff performance and outcomes (Senge, Carstedt, & Porter, 2001). Hieronymi (2013) argued that system thinking is necessary to understand interlinked organizational structures. Computerized physician order entry and medication reconciliation are a part of hospital systems with unknown relationships with Medicare 30-day readmission rates, which indicated that systems theory appeared suitable as a theoretical framework for this study. Although popular, general system theory has some limitations (Hieronymi, 2013). Users of the concepts of the general system theory suggested many unsolved challenges (Valentinov, 2012). Researchers should use the theory to understand how systems link. In addition, to assess the performance of an organization using general system theory, the researcher should assess the effects of inputs, transformations, outputs, and interrelationships (Valentinov, 2012). In the 1950s, Dorothy Johnson presented one of the earliest theories of nursing. Johnson based this theory on a general system theory. The theory focuses on nursing practice as an external force to preserve the organization of the patient’s behavior by means of imposing regulatory mechanisms by providing resources while the patient 14 experienced stress (Hieronymi, 2013). Johnson’s (1990) theory emphasized the regulated balance between interdependent functional subsystems within a system (Glenister, 2011). 30-Days Readmission The 30-day readmission rate is the admission of a patient to a hospital within 30 days after discharge (Gerhardt et al., 2013). Readmission might seem to be good news for hospitals. The more patients return to the hospital; the more hospitals gain additional revenues (Bazzoli, Fareed, & Waters, 2014). This situation might not always be true because readmission is not good news for healthcare payers. Some readmissions are out of the control of hospital staff while other readmissions remain avoidable. Healthcare payers might not be happy to pay for avoidable readmission. Avoidable readmissions decrease readmissions in the inpatient setting (Segal, Rollins, Hodges, & Roozeboom, 2014). The 30-day-readmission data collection period was January 1, 2013, to December 31, 2013. Readmission is a concern for all healthcare stakeholders because high readmission rates represent a poor outcome of the transitions from a hospital bed to the community (Fletcher, 2013). Tracking the number of unplanned readmissions of patients is an important metric for evaluating hospitals’ quality of care in the United States. Increasingly, readmission exists as a performance indicator of hospitals in the United States (Mark et al., 2013). An increase in 30-day readmission rate leads to poor health outcomes and high healthcare cost (Freymann Fontenot, 2014). The CMS uses the 30-day readmission rate as the standard benchmarking metric of hospitals in the United States 15 (Shulan et al., 2013). The CMS considers 30-day readmission rates of 80th percentile or lower as optimal rates (McHugh et al., 2010). Hospital management works around the 30-day readmission rate by readmitting patients under the classification of observation (Macy et al., 2012). The technical difference has to do with Medicare reimbursement. Patients readmitted within a 30-day period for the same illness cause the hospital to lose 25% to 1% of Medicare reimbursements (McCormack et al., 2013). Consistency in the designation of patients under observation status among hospitals and payers may be necessary to compare quality outcomes and costs, as well as optimize models of pediatric observation care (Macy et al., 2012). Since the 1960s, the U.S. healthcare spending grew from 6% of GDP in 1965 to 17% in 2011 (D. Kessler, 2011). Moreover, researchers project healthcare spending to be 26% in 2035 (Baicker & Goldman, 2011). In 2013, Medicare spending was approximated $2.8 trillion, or $8,915, per person (CMS, 2014). Researchers estimated that approximately $17 billion of the total Medicare spending related to unnecessary readmission (Shulan et al., 2013). Researchers also estimated that Medicare spending would grow an average rate of 6.8% annually from 2015 to 2021 (Fletcher, 2013). Although the United States has the highest healthcare spending in the world, the healthcare outcome in the United States remains the worst among other industrial countries (Davis, Schoen, & Stremikis, 2010; Morley, Bogasky, Gage, Flood, & Ingber, 2014). The United States reported higher readmission rates in various health conditions in comparison to other developed countries (Joynt & Jha, 2011). Approximately 20% of Medicare inpatients return to the hospital 16 within 30 days of discharge (Shulan et al., 2013). To face this high spending and low outcome, The Patient Protection Affordable Care Act of 2010 developed new incentives to reduce 30-day readmissions (McHugh et al., 2013). Hospitals with high 30-day readmission rates could lose up to 3% of their Medicare reimbursement by 2015 (McCormack et al., 2013). In 2013, approximately two-thirds of American hospitals faced such penalties (Harvath, Hilu, Nemana, & Sairamesh, 2013). The priority of any healthcare system is to improve the quality and reduce the cost of healthcare, thereby reducing avoidable readmission (McNair & Luft, 2012). Reducing 30-day readmission is a priority of scholars, hospital leaders, healthcare payers, and lawmakers in the United States (Gerhardt et al., 2013). In the 21st century, this issue became the main issue of the national debate about the healthcare quality and the performance of hospitals (McCormack et al., 2013). In 2011, lawmakers estimated that avoidable 30-day admissions remained at 76% (Fletcher, 2013). Hospital leaders vigorously worked to reduce their readmission rates (McHugh et al., 2010). Furthermore, multiple local and nationally based organizations engaged in helping hospitals to reduce their 30-day readmission (Shulan et al., 2013). Despite the increasing interest of scholars and practitioners on hospital 30-day readmission, evidence on best practices to reduce readmission is limited (McHugh et al., 2013). Several predictive models exist for hospital readmission; however, many of these models perform poorly (Shulan et al., 2013). The results showed that factors, such as communication between patients and healthcare providers, coordination of the afterdischarge, and quality of care during initial hospitalization, are significant drivers of 30- 17 day readmission (Fletcher, 2013). The main reasons for readmission are the lack of coordination and poor care delivery (Ketterer, Draus, McCord, Mossallam, & Hudson, 2014). In an experimental study conducted at an inner city academic teaching hospital, Ketterer et al. (2014) examined the effect of a discharge process on hospital readmission. The sample consisted of two groups of participants. The first group of participants received an additional education discharge intervention, and the second group, the control group, received a standard discharge process. The intervention included the assignment of a nurse who served as discharge advocate and a clinical pharmacist whose responsibility was to communicate with the patients on the third day of the discharge. The responsibility of the discharge advocate was to coordinate the discharge and an after hospital care plan, which included educating patients about their medications, their medical condition, and to ensure that the patients were aware of all their follow-up doctor appointments. The nurse and a clinical pharmacist identified and addressed all risks of unplanned readmissions for patients in the intervention group. Participants in the control group did not receive any additional education. The results of Ketterer et al.’s (2014) study indicated that patients in the intervention group had less risk of readmission than did patients in the control group. In a 2014 quasi-experimental study, Warden, Freels, Furuno, and Mackay (2014) confirmed the important role of a pharmacist in reducing 30-day readmissions. Ketterer et al. and Warden et al. showed that the relationship between patients and various parties involved in the care system plays an important role in minimizing 30-day readmission. Although 18 these two studies contributed to the debate of 30-day readmission, their limitations lie in the fact that they focused only on the discharge process. A significant number of readmission could decrease by ensuring that patients understand their plan of care (O’Leary et al., 2010). In the process of evaluating how well patients understand their plan of care, O’Leary et al. interviewed physicians and hospitalized patients from an urban academic hospital. The sample included 241 patients and 233 physicians. Patient interviews consisted of asking about knowledge of their physician and nurse names, as well of their primary health condition, changes in their medication, their expected hospitalization length, their planned tests and procedures, and their physician consultants. Physicians responded to the same questions during the interview. The results of O’Leary et al.’s (2010) study indicated that although 32% of patients identified their physicians by name and 60% identified their nurse by name, only 11% knew the role of their physicians. Among the patients, only 25% knew their discharge date (Scott et al., 2012). In addition, 65% of patients did not know their primary medical conditions, 48% ignored their planned tests, 10% did not know their planned procedure, 61% were not aware of changes in their medications, and 52% did not know their physician consultants (O’Leary et al., 2010). Helping patients understand their plan of care will likely improve the quality of care and reduce unplanned readmission (Fletcher, 2013). Although O’Leary et al. demonstrated that patients lacked knowledge of their plan of care and suggested that reducing this lack might reduce unplanned 19 readmission, the results did not show the direct relationship between the understanding plan of care and 30-day readmission. The main theme of the study is the lack of knowledge patients have about the processes of the hospital. Higher overall patient satisfaction and satisfaction with discharge planning are associated with lower 30-day risk-standardized hospital readmission rates after adjusting for clinical quality (Boulding, Glickman, Manary, Schulman, & Staelin, 2011). This study could have been stronger if O’Leary et al. (2010) addressed how understanding the plan of care could impact hospital 30-day readmission. Understanding the plan of care could provide hospital leaders with an opportunity to address 30-day readmission. Hospital unitization, which is the separation of bed units, is another significant driver of readmission (Anderson et al., 2012). Using data from 7,800 surgeries performed in 2007, Anderson et al. (2012) investigated the issues of readmission at large academic hospitals in the United States. The results indicated that highly unitized units had higher readmission rates (Fletcher, 2013). Researchers estimated that additional beds used at the time of discharge increased the likelihood of readmissions (Anderson et al., 2012). In general, patients discharged from highly unitized post-operative units returned within 72 hours (Anderson et al., 2012). High readmission represents poor outcomes (Fletcher, 2013). Some hospitals attempted to work around 30-day readmission rates by classifying patients as observation status, which would not count the patient as being a readmitted patient (Macy et al., 2012). In an experimental study conducted at an inner city academic 20 hospital, Ketterer et al. (2014) concluded that patients who received support from a discharge advocate had lower readmissions than those who received a standard discharge. Using data from the MarketScan Multistate Medicaid Claims Database, Mark et al. (2013) examined the predictors of behavioral health patients. Data were from 2004 to 2009. The sample of the study consisted of the hospital with a minimum of 25 readmissions per year. The median readmission rate for behavioral health patients was 11% (Mark et al., 2013). The results indicated increased follow-up with discharged behavioral health patients is likely to decrease the likelihood of readmission. Mark et al.’s results also indicated that the length of the first admission correlates with lower readmission risk. Increasing the length of hospital stay could decrease the risk of readmission for behavioral health patients, which might not be true for another type of patients (J. Ford, Algert, Morris, & Roberts, 2012). Similarly, DeLia, Jian, Gaboda, and Casalino (2014) showed that post-discharge follow-up could decrease the risk of readmission. Data for the study of DeLia et al. (2014) consisted of Medicare claims data from 2007 to 2008. Participants were patients with an index admission for health conditions including heart failure, acute myocardial infarction, and pneumonia (J. Ford et al., 2012). J. Ford et al. (2012) investigated changes in the length of postnatal hospital stay by delivery type, hospital type, the concurrent maternal readmission rates, and the reasons for this readmission. J. Ford et al. used data from 597,475 mothers’ birth admissions and 19,094 readmissions in the 6-weeks post-birth in New South Wales from 2001 to 2007. The two delivery types used included vaginal delivery and Caesarean. The 21 two types of hospitals were private and public hospitals. The study outcomes were the postnatal length of stay and the readmission rate per 100 deliveries. The results indicated that the length of postnatal stay decreased for both types of birth in both types of hospitals from 2001 to 2007 (J. Ford et al., 2012). In conclusion, decreasing the length of postnatal stay does not increase the risk of readmission (J. Ford et al., 2012). Friedman, Jiang, Steiner, and Bott (2012) investigated the impact of the type of Medicare plan on hospital 30-day readmission. Friedman et al. used data from the Agency for Healthcare Research and Quality in 2006 for five states. The two types of Medicare plans included Medicare Advantage plans and the standard fee-for-service program. The results indicated patients with Medicare Advantage plans had a lower risk of readmission than did patients with the standard fee-for-service program (Vertrees, Averill, Eisenhandler, Quain, & Switalski, 2013. Patients with Medicare Advantage plans were younger and less ill than were patients with the standard fee-for-service program (Friedman et al., 2012). The results of the study indicated that patients with the standard fee-for-service program had a lower likelihood of readmission (Friedman et al., 2012). Friedman et al.’s (2012) study provided insight; however, DeLia et al. (2014) failed to explore the reasons why the chances of readmission of these two groups of patients are different. Postdischarge follow-up could decrease the risk of readmission (Boulding et al., 2011). Many hospitals in the United States adopted care management technologies to reduce readmission; however, these technologies were expensive and ineffective (Vertrees et al., 2013). An effective readmission-prevention technology should be able to 22 (a) provide an accurate prediction of risk; (b) synthesize and transform data into actionable insight; (c) focus on activities with higher impact; and (d) bridge care and communication within the organization (Harvath et al., 2013). Information technology (IT) includes likelihood to improve performance through many aspects including increased productivity and better customer experience (S. Dewan & Ren, 2011). The impact of 30-day readmission rates on hospital’s financial performance. As part of the Affordable Care Act (ACA), the U.S. Congress directed the Centers for Medicare and Medicaid Services (CMS) to penalize hospitals with worse than expected 30-day readmission rates (Joynt & Jha, 2012). According to a 2009 study by the Center for Medicare Services, nearly 20% of Medicare beneficiaries admitted to a hospital within 30 days after discharge at an annual cost of $17 billion. Causes of avoidable readmissions include hospital-acquired infections and other complications; premature discharge; failure to coordinate and reconcile medications; inadequate communication among hospital personnel, patients, caregivers, and community-based clinicians; and poor planning for care transition (Berenson, Paulus, & Kalman, 2012). Financial performance is an important metric of any business including hospitals. The ability to grow financially is a key performance factor in hospital managers’ efforts to attract well-qualified healthcare professionals and provide a high quality of care (Singh, Wheeler, & Roden, 2012). Profitable hospitals could retain and reinvest their revenue (Dilwali, 2013). Profitable hospitals attract well-qualified healthcare professionals (Kaufman, 2013). 23 Hospitals are high revenue generators in the United States; however, high revenue might not necessarily be an indicator of high financial performance. American hospitals generate total revenue of $1.068 trillion (Statistics Brain, 2013). Approximately, 92% of this revenue serves to cover operating expenses (Smith, Bradley, Bichescu, & Tremblay, 2013). Not-for-profit hospitals enjoy the tax exemption benefit that for-profit hospitals do not receive. In 2012, not-for-profit hospital received $12.6 billion in tax exemptions (Rubin, Singh, & Jacobson, 2013). New U.S. government regulations increased the need for hospital leaders to invest in new technologies (Smith et al., 2013). Analysis of data from 567 U.S. hospitals shows that IT had swift and even patient flow, which in turn improved revenues (Kaufman, 2013). Interestingly, the improvement in financial performance is not at the expense of quality, because similar effects of IT and patient flow in improvements in the quality of patient care exist (Devaraj, Ow, & Kohli, 2013). The use of healthcare IT received a significant enhancement in the United States because of the American Recovery and Reinvestment Act (ARRA) of 2009 (Detmer, 2010). Hospitals in the United States increasingly adopt electronic medical record (EMR) systems because of new federal regulations. The intent of this adoption is to improve the quality of healthcare in the United States; however, hospital leaders are unsure of the potential impact of EMR on their financial performance (Kazley et al., 2011). Smith et al. (2013) compared the financial performance of a set of hospitals with sophisticated EMR systems to the financial performance of a set of the similar hospital with less sophisticated EMR systems. The goal was to examine the relationship between 24 IT governance and hospitals’ financial performance (Kazley et al., 2011). The results indicated that hospitals with sophisticated EMR systems could be more profitable than those hospitals without these systems (Kazley et al., 2011). Despite the value of IT, investing in IT assets only does not improve financial performance (Kohli, Devaraj, & Ow, 2012). Business leaders should combine investment in IT with other business capabilities, such as working relationship with senior leaders to drive financial performance (Karahanna & Preston, 2013). In a study involving 81 hospitals in the United States, Karahanna and Preston showed the nature of the relationship between the chief information officer (CIO) and top management team (TMT) was a significant driver of hospitals’ strategic alignment and financial performance. Various metrics exist to measure hospitals’ financial performance. The total profit margin is one of the most popular indicators of a hospital’s financial performance (Cleverly, Song, & Cleverly, 2012). Total profit margin refers to a hospital’s overall profitability per unit of revenue earned (Singh et al., 2012). Another, popular indicator of a hospital’s financial performance includes the operating margin (Cleverley et al., 2012). The operating margin measures profitability with respect to operating activities, which include patient care services (Kirby, 2012). A third commonly used indicator of hospital financial performance is a free cash flow, which focuses on a hospital cash inflow and outflow rather than accounting earnings (Cleverley et al., 2012). Some scholars argued that free cash flow defines financial performance more closely than reported income does because managers can manipulate reported income 25 (Singh et al., 2012). Thirty-day readmission impacts the financial performance of hospitals (Zhivan & Diana, 2012). Examining the errors in the process of computerized physician order entry and medication reconciliation could possibly provide the hospitals with metrics that will better measure hospitals’ financial performance (Kirby, 2012). Effective revenue cycle management is a significant driver of a hospital’s financial performance (Singh et al., 2012). Revenue cycle management is the process of managing payments and revenue generation (Murphy, Rosenman, McPherson, & Friesner, 2011). In a study involving data from 1,397 not-for-profit hospitals in the United States, Singh et al. (2012) examined the relationship between effective revenue cycle management and hospitals’ financial performance. The results indicated that an effective revenue cycle management could drive four financial metrics including (a) operating profit margin, (b) total profit margin, (c) free cash flow, and (d) equity capital. Technology is another important driver of hospitals’ financial performance (S. Dewan & Ren 2011; Karahanna & Preston, 2013; Kohli et al., 2012). Business leaders recognize the value of IT not only as an enabler of business strategy,but also as a driver of financial performance (Karahanna & Preston, 2013). Investment in IT will likely drive financial performance through many aspects including increased productivity and better customer experience (S. Dewan & Ren, 2011). Managers realized the value of the IT investment at various levels including (a) operation, (b) process, and (c) market (Yayla & Hu, 2011). Business leaders make a rational decision on IT investment when they can quantify and justify the contribution of IT to firm performance (Kohli et al., 2012). 26 In a case study involving non-publically traded hospitals in the United States, Kohli et al. (2012) examined the influence of IT on firm financial performance. The results indicated that the IT investment had more statistically significant influence on firm market value measures than on accounting performance measures (S. Dewan & Ren 2011; Karahanna & Preston, 2013; Kohli et al., 2012). Few hospitals could survive without their information systems being a part of patient care (Dos Santos, Zheng, Mookerjee, & Chen, 2012). The impact of IT on financial performance may be a function of the portfolio of IT applications used, as well as the assimilation and use of IT in the organization (Setia, Setia, Krishnan, & Sambamurthy, 2011). Setia et al. (2011) identified two dimensions of IT assimilations and use including IT applications architecture spread and IT applications architecture longevity. Next, Setia et al. examined how these two dimensions affect hospitals’ financial performance. The results indicated that although the two dimensions could drive hospitals’ financial performance, IT applications’ architecture longevity has a significant influence (Karahanna & Preston, 2013). Scholars identified the nature of hospitals’ relationship with their suppliers as an important driver of hospitals’ financial performance (Germain, Davis-Sramek, Lonial, & Raju, 2011). Using survey data from the top executive of 740 hospitals in the Midwestern United States, Germain et al. examined the relationship between relational supplier exchange and hospitals’ financial performance. The results showed two types of relationships based on the responsiveness of the hospital (Moussa, 2013). Relational supplier exchange had a positive relationship with financial performance for high 27 responsive hospital and a neutral relationship with a low responsive hospital (Kohli et al., 2012). Human capital flow is another significant driver of hospitals’ financial performance (Reilly, Nyberg, Maltarich, & Weller, 2014). Given the negative effect of employee turnover on firm financial performance, scholars and practitioners pay more attention to various ways of retaining employees (Dong, Mitchell, Lee, Holtom, & Hinkin, 2012). Employee retention became an important performance indicator and financial performance driver for many organizations (Moussa, 2013). Many organizations fail to retain half of their employees for more than 5 years (Bagga, 2013). The cost of hiring and training a new employee ranges from 25% to 500% of the annual salary of the employee (Ballinger, Craig, Cross, & Gray, 2011). Employee turnover could affect patient ratification, which in turn, could affect the hospital’s financial performance (Reilly et al., 2014). Nurses’ voluntary turnover has a significant negative effect on patient outcome (Ellenbecker & Cushman, 2012). In addition to turnover, employees’ work-life balance has a significant effect on various hospital outcomes including financial outcome (Avgar, Givan, & Liu, 2011). In addition to all these drivers, hospital readmission rate became an important driver of hospitals’ financial performance (Gerhardt et al., 2013). Given the high cost related to hospital readmission, a hospital leader contends with reducing their 30-day readmission. In 2015, hospitals with 30-day readmissions faced penalties including losing up to 3% of their Medicare reimbursement (McCormack et al., 2013). Medicare started in 2010 to withhold reimbursement to hospitals for readmissions occurring within 24 hours 28 of discharge (Allaudeen, Vidyarthi, Maselli, & Auerbach, 2011). These penalties could have a negative impact on a hospital’s financial performance. Historically, the only determinant of hospitals’ reimbursement was the amount of care they provide (Fletcher, 2013). Computerized Physician Order Entry Scores (CPOES) The process of medication use includes a variety of activities involving various health care professionals and various steps, which include prescription, transcription, administration, and monitoring (E. Ford et al., 2011). This process presents various opportunities for medical errors. Often, medical errors happen at the prescription step because of multiple reasons (E. Jones & Furukawa, 2014). Among other issues, these reasons include a lack adequate knowledge of medications by the prescriber, failure to adhere to policies and procedures, memory lapses, confusion in nomenclature, and illegible handwriting (Spaulding & Raghu, 2013). The use of CPOES could decrease errors in medication use (E. Ford et al., 2011). CPOES refer to the electronic entry of a medical order by the prescriber (Spaulding & Raghu, 2013). With the use of CPOES systems, healthcare professionals can access patient records, and clinical decision supports in real-time (Spaulding & Raghu, 2013). The use of CPOES could improve the quality of care, which is turn, could reduce hospital readmission (Fletcher, 2013; Spaulding & Raghu, 2013). Furthermore, with the rising cost of healthcare cost in the United States, lawmakers believed the use of health IT, such as CPOES systems, could improve the quality and reduce the cost of healthcare (Zhivan & Diana, 2012). 29 CPOE systems allowed physicians to enter orders directly into a computer rather than handwriting them (Spaulding & Raghu, 2013). By design, CPOE can eliminate illegible handwriting; avoid transcription errors; improve response time, accuracy, and completeness; in addition, CPOE can improve coordination of care (Coustasse et al., 2013). The ordering stage of medications is where most medication errors and preventable ADEs occur (E. Ford et al., 2011). Using data from 1,014 acute care hospitals in the United States, Spaulding, and Raghu (2013) examined the impact of the use of CPOES systems on cost and the quality of medication management process. Data sources for the study included Health Care Information and Management Systems Society Analytics, Computerized Maintenance Management System, and American Hospital Association. Despite the known benefit of CPOES usage, the adoption of CPOES systems remains a challenge (Catapano, 2012). In 2011, only 21.7% of hospitals implemented CPOES systems successfully (Alfano, 2013). This adoption rate reached 50% in 2013 (Thompson, 2014). According to Catapano (2012), factors, such as governance structures, engaged collaboration on CPOES, and project management skills, are important determinants of COPE adoption. Various hospital characteristics, such as ownership, location, financial performance, and economies of scale, are significant drivers of CPOES adaption (Zhivan & Diana, 2012). Other driving factors of CPOES adoption include reimbursement policies, characteristics of the insurance market, and competition (Zhivan & Diana, 2012). Alfano (2013) added that the first step in improving physician adoption of CPOES was to develop and implement order sets. 30 A study led by Dr. David Bates, Chief of General Medicine at Boston’s Brigham and Women’s Hospital, demonstrated that CPOE reduced error rates by 55%, from 10.7 to 4.9 per 1000 patient days (Leung et al., 2012). Researchers conducted a multicenter retrospective cohort study conducted in six community hospitals with 100 to 300 beds in Massachusetts during a 20-month observation period (January 2005 to August 2006) to access the cost of adverse drug events (Hug, Keohane, Seger, Yoon, & Bates, 2012). The researchers estimated that implementation of CPOE systems at all non-rural United States’ hospitals could prevent three million adverse drug events each year (Hug et al., 2012). Each year in hospitals in the United States, serious preventable medication errors occur in 3.8 million inpatient admissions and cost $16.4 billion (Leapfrog, 2012 ) (see Figure 1). Errors, such as incorrect dosing, mislabeled drug allergies, harmful drug interactions or dispensing problems, are frequent, and the harm they cause can be significant, even resulting in death. Medication errors are also extremely expensive, costing approximately $4,300 per error (Leapfrog, 2014). Figure 1. Inpatient preventable adverse drug events 31 Boston’s Brigham and Women’s Hospital demonstrated that CPOE reduced error rates by 55% (Hug et al., 2012). A subsequent study showed rates of serious medication errors fell by 88%. The prevention of errors attributed to the CPOE system’s structured orders and medication checks (Leung et al., 2012). Another study conducted at Latter Day Saints Hospital demonstrated a 70% reduction in antibiotic-related ADEs after implementation of decision support for these drugs (Leapfrog, 2014). Medication Reconciliation Scores On average, every hospitalized patient is a victim of at least one medication error every day (Walker, 2012a). Among all the patient safety errors, medication errors are the most common (Ripley & Vieira, 2013). Medication errors lead to at least one death per day and 1.3 million injuries per year in the United States (Tootelian, Negrete, & Skhal, 2010). Approximately 40% of medication errors are a result of lack of adequate reconciliation (J. Lee et al., 2013). These errors occur at various stages from patient admission to discharge. On average, 22% of avoidable medication errors occur at the admission stage, 66% occur during transfer, and 12% occur at the discharge stage (Conklin et al., 2014). Furthermore, 20% of medication errors result in harm to patients (J. Lee et al., 2013). Medication errors lead to one out of five injuries or deaths in hospitals (Ripley & Vieira, 2013). Empirical evidence indicated the use of medical reconciliation processes helps to avoid these medication errors (Conklin et al., 2014; Ripley & Vieira, 2013; Walker, 2012a). An effective medication reconciliation score will save medication use by patient and reduce medication errors in the United States (Daly & Lee, 2013). 32 Medication reconciliation scores referred to the process of creating a patient’s current medication list and comparing that list to the patient’s previous medications (Ripley & Vieira, 2013). The purpose of medication reconciliation scores is to help avoid a number of medication errors such as drug interaction, duplications, and omissions (Benson & Snow, 2012; J. Lee et al., 2013). Medication reconciliation scores have the potential of reducing hospital readmission (Hoisington, 2012; Walker, 2012b). Medication reconciliation scores improve patient safety and reduce the risk of readmission (Laugaland et al., 2012). Effective medication reconciliation scores and a good patient education strategy remain effective ways of reducing hospitals’ readmission rates (Hume & Tomsik, 2014). Taking charge of medication reconciliation scores is the pathway to reducing the readmission rate (Walker, 2012b). Pharmacists performed medication reconciliation evaluating 20 interventions. The evaluation revealed that in 17 of the 20 interventions, most unintentional discrepancies identified had no clinical significance (Laugaland et al., 2012). Medication reconciliation alone probably does not reduce post discharge hospital utilization, but may do so when bundled with interventions aimed at improving care transitions (Kwan, Lo, Sampson, & Shojania, 2013). Despite the known benefice of medication reconciliation, hospital leaders struggle to implement medication reconciliation scores processes (A. Lee, Varma, Boro, & Korman, 2014). Maintaining an accurate list of medications in primary care facilities remains challenging (Stewart & Lynch, 2014). A. Lee et al. (2014) argued that reviewing electronic medical records to obtain pharmacist medication histories plays an important 33 role in obtaining an accurate list of a patient’s medications. A successful medical reconciliation process should include an interview with patients and use other sources of information including nursing facilities, pharmacies, and physician offices (Sen, Siemianowski, Murphy, & McAllister, 2014). A meta-analysis of 22 studies focusing on medication history discrepancies found that 10 to 16% of patients had at least one medication history error at hospital admission (Leapfrog, 2014). Many of these medication history errors occur upon admission to or discharge from a clinical unit of the hospital (Ripley & Vieira, 2013). The frequencies of medication reconciliation errors are 20% of ADEs within hospitals. The medication reconciliation process is an effective preventability strategy for the reduction of medication errors and (J. Lee et al., 2013). Summary In summary, the literature includes an increasing number of studies on the various issues surrounding hospitals’ performance. The 30-day readmission is a significant driver of a hospital’s financial performance (Fletcher, 2013). In addition to a significant driver of hospitals’ financial performance, the 30-day readmission rate remains an important performance indicator of hospitals in the United States (Gerhardt et al., 2013). Despite the importance of hospitals’ 30-day readmission rate, hospital leaders still struggle to find the best way to reduce their readmission rate (McHugh et al., 2013). Many models allow for predicting hospital readmission; however, the majority of these models perform poorly (Shulan et al., 2013). 34 The purpose of this quantitative correlational study was to examine the relationship between CPOES, medication reconciliation scores, and 30-day readmission rates for hospitals in the states of Alabama, Georgia, and Florida. The lack of a predictive model for hospital 30-day readmission represents an important gap in the literature. In Section 1, I established the foundation for the study. Section 2 includes an expansion about the discussion of the problem statement, purpose, method, and design. Finally, Section 2 includes an explanation of data analysis, data collection, population and sampling, and ethics. 35 Section 2: The Project In Section 1, I covered the foundation of the study. Section 2, includes the steps necessary to conduct the study. These steps include the purpose of the study, the role of the researcher, the participants, the research method, and the research design. In addition, this section includes the population and sampling, the ethical research, the data collection, the analysis process, and validity. Purpose Statement The purpose of this quantitative correlational study was to examine the relationship between CPOES, medication reconciliation scores, and 30-day readmission rates. The independent variables were CPOES and medical reconciliation scores. The dependent variable was 30-day readmission rates. The target population included Medicare-eligible hospitals located in Alabama, Georgia, and Florida. The Southeastern of the United States has the highest readmissions rates compared to other regions (Anderson et al., 2012). The implications for positive social change included the potential for the sustainability of Medicare-eligible hospitals. Role of the Researcher Researchers encounter various ethical and legal challenges in every step of their research (Watts, 2011). An important role of the researcher is to comply with all the applicable legal requirements and codes to conduct a research study (van Deventer, 2009). As a researcher, I abide by all ethical and legal standards of the Belmont report, avoided personal bias, and respected participants’ right at every stage of the study. On July 12, 1974, the U.S. Congress signed the National Research Act into law thereby 36 creating the National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research (Fiske & Hauser, 2014). The National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research published the Belmont Report to summarize, the basic ethical principles identified by the National Commission in the course of its deliberations (Dahlöf, 2013). In this study, the usage of archival data does not include any personal information or identification of participants. Works of the U.S. government are in the public domain and did not require permission to reuse (CMS, 2014). My intent was to abide strictly by these mandates. No relationship existed with any of the hospitals, and I had no prior association with the topic of this study. In addition, all researchers must receive permission from Walden University’s Institutional Review Board (IRB) before collecting data. Participants Hospitals in this study included the population of Medicare-eligible hospitals in the Southeastern United States. The southeastern region comprises the states of Alabama, Georgia, and Florida. CMS provides access to data for all Medicare-eligible hospitals (CMS, 2014). Because of the usage of archival data, no direct relationship exists with Medicare-eligible hospitals. This type of study aligns with stratified sampling (Hays & Wood, 2011). Stratified sampling requires accurate information about the population (Belanger et al., 2013). Research Method and Design When conducting their studies, researchers have the choice to use quantitative, qualitative, or mixed-method of research (Venkatesh et al., 2013). The nature of this 37 study aligns with the concepts of quantitative research method with a correlational design. This subsection includes the justification of a quantitative method and correlational design as the most appropriate research method and design for this study. Method In alignment with the problem and purpose statements, and after consultation with my mentor, the quantitative method was appropriate for this study. Quantitative researchers use measurable data to examine relationships between variables (Rozin, Hormes, Faith, & Wansink, 2012). Quantitative research helps us to understand phenomena by collecting and analyzing numerical data (Griffiths, & Norman, 2013). Quantitative data collection approach can bring breadth to a study by helping researchers gather data about different aspects of a phenomenon from many participants (Venkatesh et al., 2013). Researchers use a qualitative method to understand the beliefs, experiences, and perspectives of study participants (Zachariadis et al., 2013). Furthermore, qualitative researchers rely on collecting and analyzing non-measurable data (Richardson et al., 2012). I used measurable data collected from many participants to determine if there was a relationship between CPOES, medication reconciliation scores, and 30-day readmission rates. Therefore, the quantitative method suited the needs for this study. Research Design Correlational researchers focus on the relationship between variables (Sparks & Pan, 2010). The purpose of using correlations in research is to determine the relationship if any, of variables (R. Kessler & Glasgow, 2011). Researchers use correlational design to determine whether an increase or decrease in one variable corresponds to increase or 38 decrease another variable (Ben-Natan et al., 2014). A correlational design suits the needs of the study because the goal of this study was to examine the relationship between the independent variables CPOES, and medication reconciliation scores and a dependent variable, 30-day readmission rates. Researchers recommended designs such as experimental and quasi-experimental designs when the study focus is to assess cause and effect (Handley et al., 2011). The focus of this research study was to examine the relationship, if any, between variables; thus, the experimental and quasi-experimental designs did not suit the needs of the study. Population and Sampling The targeted population for this study included Medicare-eligible hospitals in Alabama, Georgia, and Florida. I retrieved data for Medicare-eligible hospitals from the Medicare governmental hospital compare, and hospital safety scores database (CMS, 2014). This type of selection aligns with stratified random sampling. Stratified sampling is a probabilistic sampling method in which the researcher selects participants from a target population based on their fit with the purpose of the study, inclusion, and exclusion criteria (Hays & Wood, 2011). The geographical region for this study was Alabama (20%), Georgia (70%), and Florida (10%) and consisted of Medicare-eligible hospitals (Leapfrog, 2104). Therefore, the sample reflected this geographical stratification demography. The advantages of using stratified sampling are that the researcher focuses on the priority subpopulations, ignoring the less relevant subpopulations (Goodman, Cryder, & Cheema, 2013). Stratified sampling allows the use of different sampling techniques for 39 different subpopulations. Using this method considerably improves the overall accuracy of the hypotheses and result (Wu, 2013). If population density varies within a region, stratified sampling will ensure accuracy within different parts of the region (Esfahani & Dougherty, 2014). The first disadvantage of using stratified sampling is the selection of inappropriate stratification variables. The second disadvantage of stratified sampling is the data will not be useful when there are no identical or similar categories or groups (Ye, Wu, Huang, Ng, & Li, 2013). Stratification sampling requires accurate information about the population and is an expensive form of sampling (Belanger et al., 2013). Researchers use the G*Power 3.1.9 software program to determine the needed sample size for conducting the data analysis (Faul, Erdfelder, Buchner, & Lang, 2009). I conducted a power analysis, using G*Power version 3.1.9 software, to determine the appropriate sample size for the study. An a priori power analysis, assuming a medium effect size (f = .15), a = .05, indicated a minimum sample size of 68 participants was required to achieve a power of .80. Increasing the sample size to 146 would increase power to .99. Therefore, the study consisted of 117. Table 2 indicates the minimum sample size stratification breakdown based upon .80 and .99 power. 40 Figure 2. Power as a function of sample size Table 2 State Stratified Minimum Sample Size at .80 and .99 Power Power Level State .80 .99 Alabama 14 29 Georgia 48 102 Florida 6 15 68 146 Total Non-probability sampling is a sampling technique where the samples do not give all the individuals in the population equal chances of being selected (Callegaro et al., 2014). An advantage of the non-probability method is the tremendous degree of flexibility in setting inclusion probabilities for elements of the sampling frame (Zaman, 41 Rangavajhala, McDonald, & Mahadevan, 2011). When discussing the non-probability method, researchers cannot draw inferences about the larger population (Jiang, Ni, Han, & Tao, 2014). Ethical Research Researchers should address ethical challenges at every stage of their studies (Watts, 2011). Walden University requires doctoral students to complete the training for protecting human research participants and obtain approval from the IRB before proceeding to data collection. The proof of completion for protecting human research participants training is located in Appendix A. After applying for and obtaining approval from Walden University’s IRB; data collection commenced. In this study, no interaction with the participants occurred. The data collection process did not involve interviews or surveys; therefore, no informed consent was necessary. I stored electronic files in True Crypt and will destroy the hard copies after 5 years. Data Collection Data collection in this study did not involve collecting data directly from participants. I did not use any specific data collection instrument to collect data. Archival data exist for the independent variables of CPOES and medication reconciliation scores in the Medicare governmental database of hospitals safety scores. Archival data exist for the dependent variable of 30-day readmission in the U.S. Medicare governmental database of hospital-compare. In 2011, the Leapfrog group published The Leapfrog Hospital Survey, which assessed hospital performance based on national performance measures. The survey 42 measures a hospital’s progress toward implementing a CPOE system and the efficacy of that system in alerting prescribers to common medication errors. These measures are of interest to the patients and hospital administrators. Hospital administrators use these data to determine the progress made in providing quality, improving safety practices, and identifying proficiency of patient care. The Leapfrog Hospital Survey measured the data needed for the independent variables. Description of the Data The term CPOE refers to the electronic entry of a medical order by the prescriber (Spaulding & Raghu, 2013). Medication reconciliation scores represent comparing a patient’s current medication list to the patient’s previous medications list (Ripley & Vieira, 2013). The purpose of medication reconciliation scores is to help avoid a number of medication errors such as drug interaction, duplications, and omissions (Benson & Snow, 2012; J. Lee et al., 2013). The score is a composite made up of 28 different national patient safety measures publicly reported as an A, B, C, D or F letter grade (Leapfrog, 2012) CPOE. Computerized physician order entry (CPOE) errors occur when the providers use a computer and improperly enters medication orders into the database (CMS, 2014). Computer physician order entry system is a prescription ordering systems that interpret data at the time medications are ordered (Fletcher, 2013; Spaulding & Raghu, 2013). With CPOE, physicians enter orders into a computer rather than on paper. Orders integrated with patient information include laboratory and prescription data, which staff automatically checks for potential errors or problems (Leapfrog, 2014). 43 Medication reconciliation. Medication errors are common occurrences in hospitals (Leapfrog, 2014). Sometimes these errors happen when patient moves or many people care for him or her. If the entire care team does not know which medications and the dosages the patient received, a medical error could be made, which could cause the patient to suffer (Austin et al., 2014). The most severe medication mistakes might even cause a patient to die. Staff members always check with each other to be sure medical personnel they know which medications and dosage a patient are taken. The hospital staff also uses computerized systems to keep track of a patient’s medications (Leapfrog, 2014). A higher score for the process/structure measures may be because the measures are compliant with best practices in patient care. The Leapfrog Group collected the CPOES scores and medication reconciliation scores with the Leapfrog Hospital Survey (J. Lee et al., 2013). The 30-day readmission rate was also collected by the Leapfrog Group (Leapfrog, 2014). The Scales of Measurement The Leapfrog survey measured the two independent variables of CPOE scores and medication reconciliation scores. Both variables reflected the ordinal scale of measurement. Ordinal refers to order in measurement (Cagnone & Monari, 2013). An ordinal scale of measurement allows comparisons of the degree to which two subjects possess the dependent variable (Gadermann, Guhn, & Zumbo, 2012). The statistics used with ordinal are in non-parametric groups (Norman, 2010). Therefore, the ordinal measure suited the needs of this study. 44 Administration The Leapfrog Group provided Medicare-eligible hospitals with a voluntarily administered survey (Leapfrog,2014) The hospital CEO/Chief Administrative Officer received an introductory letter requesting the hospital’s participation in the survey online during the first week of April 2016. The hospital administrator also received a 16-digit security code to log into the online survey tool. The Leapfrog Hospital Survey is free and open to hospitals from April 1st to December 31st of each year. The annual Leapfrog Hospital Survey assesses hospital safety, quality, and efficiency based on national performance measures (Brooke et al., 2012). These measures and safety practices are of specific interest to health care purchasers and consumers, and cover a broad spectrum of hospital services, processes, and structures (Shahian et al., 2012). These measures also provide hospitals with the opportunity to benchmark the progress they are making in improving the safety, quality, and efficiency of the care they deliver. Scores and Meaning The scores for CPOE and medication reconciliation are an ordinal scale of measure ranging along a continuum between 0 and 100 (Leapfrog, 2014). A numerical score is assigned to CPOE for each performance category from the Leapfrog Hospital Survey in the following manner: fully meets standards = 100 points, substantial progress = 75 points, some progress = 50 points, willing to report = 25 points (Leapfrog, 2014). For the purposes of this study, the following Likert scale will be used: 3 = fully meets standards, 2 = substantial progress, 1 = some progress. A higher score represents a higher degree of compliance. 45 A numerical score is assigned to medication reconciliation for each performance category. Hospitals receive either a fully meets standard, substantial progress, some progress, or willing to report (Austin et al., 2014). Again, a higher score represents a higher degree of compliance. For the purposes of this study, the following Likert-type scale was used: 3 = fully meets standards, 2 = substantial progress, 1 = some progress. Strategies Use to Address Validity The strategy used to address the validity of the instrument was construct validity. In 2011, the Leapfrog group invited nine national experts to develop a composite score to evaluate patient safety in hospitals throughout the United States (Leapfrog, 2014). The work involved defining a conceptual framework for the score, assigning a weight to the measure, standardizing scores across measure different types, and identifying methods for dealing with missing data (CMS, 2014). The panel recommended that Leapfrog includes publicly reported measures from national data sources in the score. The panel excluded state reported and regionally reported measures, because of the variations in measures specifications, data collection, and availability that would prevent a consistent comparison across hospitals (Austin et al., 2014). Process for Completing Hospital Safety Survey Leapfrog instrument administration focused on measuring and publicly reporting on hospital performance through the annual Leapfrog Hospital Survey (Leapfrog, 2014). The survey is a trusted, transparent, and evidence-based national tool in which more than 1400 hospitals voluntarily participate at no charge. The instrument measures the 46 independent variables and dependent variable and meets the need for this study (CMS, 2014). Availability of Raw Data The Center for Medicaid and Medicare services makes raw data available from the hospitals safety score database (CMS, 2014). The public has access to data from the Center for Medicaid and Medicare services without written request. I will retain a copy of the raw data used in this study for 5 years in my password-protected computer and backed up on a password-protected hard drive and will destroy following the retention period. Data Collection Technique Data collection is an important aspect of any research study. Inaccurate data collection can affect the results of a study and can ultimately lead to invalid results (University of Wisconsin-Eau Claire, 2014). Data collection in this study included requesting and receiving raw data from the CMS database. The computer physician order entry scores and medication reconciliation scores are archival data with no identifiers collected by The Leapfrog Group from the Leapfrog Hospital Survey (Leapfrog, 2014). The survey measures a hospital’s progress toward implementing a CPOE system and the efficacy of that system in alerting prescribers to common medication errors (Zhang & Shaw, 2012). Advantages of survey data collection are: (a) numerous questions asked about a topic; (b) advanced statistical techniques utilized to analyze survey data to determine validity, reliability, and statistical significance; and (c) a broad range of data collected 47 (Jahedi & Méndez, 2014). Disadvantages of survey data collection are (a) closed-ended questions may have a lower validity rate than other question types, (b) question nonresponses data errors may exist, and (c) the number of participants who choose to respond to a survey question may be different from those who chose not to respond, thus creating bias (Hertlein, & Ancheta, 2014). The survey method is the preferred method of data gathering for research due to the various advantages, strengths and benefits (University of Wisconsin-Eau Claire, 2014). The advantages of using archival data are (a) the data already exist, (b) it is less expensive than doing the primary research, and (c) easier access to data (Zhang & Shaw, 2012). The disadvantages of using archival data include less control over the data, and there could be biases in the data (Dikolli et al., 2012). Another disadvantage of archival records includes data, which may influence the relationship between the variables (Lam, 2010). A pilot study was not necessary for this study, because of the use of existing secondary research data. Data Analysis Technique Data analysis is a process for obtaining raw data and converting it into information useful for decision-making by users (Linley & Hughes, 2013). Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, in different business, science, and social science domains (Dalal & Zickar, 2012). The focus of data analysis in this study was to seek the answer to the following research question: What is the relationship between CPOES, medication scores, and 30-day 48 readmission rates for Medicare eligible hospitals? I used multiple linear regression to analyze the data. Multiple linear regression is a statistical technique researchers use when the intent of the study is to predict a quantitative outcome response from more than one predictor variable (Sofowote, Bitzos, & Munoz, 2014). Multiple linear regression is a statistical technique that predicts values of a quantitative dependent variable from values of two or more independent variables (Holmes & Rinaman, 2014). Multiple linear regression remains a mainstay analysis in organizational research (Nimon & Oswald, 2013). Logistic regression was initially considered. Logistic regression measures the relationship between the categorical dependent variable and one or more independent variables, with varying scales of measurement, by estimating probabilities using a logistic function, which is the cumulative logistic distribution (Liu et al., 2014a). Logistic regression is the probability of the response taking a particular value model based on a combination of values taken by the predictors (Agras et al., 2014). The dependent variable in this study was a scale level of measurement with scale predictor variables. Therefore, logistic regression was not appropriate for this study. The descriptive statistics include frequencies, percentages, means, and standard deviations (Gravetter & Wallnau, 2008). A difference exists between descriptive statistics and inferential statistics. With descriptive statistics, researchers simply describe what is or what the data show. With inferential statistics, researchers try to reach conclusions that extend beyond the immediate data alone (Rapaport et al., 2014). Descriptive statistics includes observations regarding the distribution of data (Bettis, Gambardella, Helfat, & 49 Mitchell, 2014). Descriptive statistics also confirm whether hypothesis testing accepts or rejects the null hypotheses (Gravetter & Wallnau, 2008). Explanation of Data Cleaning and Screening Procedures The retrieval of archival data from the Centers for Medicare and Medicaid service suits the need of this study. Archival data are any data collected prior to the beginning of the research study (C. Jones, 2010). The secondary analysis of existing data is an increasingly popular method of enhancing the overall efficiency of the health research enterprise (Hui, 2014). Government data normally do not require data cleaning or screening (Wu, 2013). Explanation for Addressing Missing Data The Leapfrog Hospital Survey is a voluntary survey. Therefore, many hospitals may choose not to submit a survey (Leapfrog, 2014). The Leapfrog Group disallows scoring of hospitals who did not report on Leapfrog’s annual survey (Leapfrog, 2014). Because the missing values are unknown, I could not be 100% certain about the probability of missing data. With a t-test for Missing Completely at Random (MCAR), missing data mechanism determined missing data, but it was not very accurate (Doove, Van Buuren, & Dusseldorp, 2014). Many missing data methods assume that MCAR or Missing at Random (MAR) is a better mechanism (Grobler, Matthews, & Molenberghs, 2014). Missing not at Random (NMAR) data mechanism is the probability of a missing value depends on the variable that is missing (Goldstein, Carpenter, & Browne, 2014). NMAR data mechanism is appropriate for addressing missing data in this study. I addressed the missing data by eliminating the data file and selecting another file. 50 Assumptions Pertaining to the Statistical Analyses Several assumptions surround the use of multiple linear regression (Loomis, 2014). These assumptions are (a) outliers, (b) multicollinearity, (c) linearity, (d) normality, (e) homoscedasticity, and (f) independence of residuals (Dietz et al., 2014). Researchers must assess these assumptions and identify any statistical corrections utilized to combat these assumptions (Voyer & Voyer, 2015). I will now discuss these assumptions and then identify methods to combat the implication of severe violations of the assumptions. Outliers. A key assumption is data will not contain any severe outliers (A. Dewan, Corner, & Hashizume, 2014). The implications of an outlier may indicate bad data (Voyer & Voyer, 2015). If the outlying point is, in fact, erroneous, then the researcher should delete the outlying value from the analysis (Loomis, 2014). I assessed the existence of outliers by a visual inspection of the Normal Probability Plot (P-P) of the Regression Standardized Residual and scatterplot of the residuals. Multicollinearity. Multicollinearity is a condition where two predictor variables are highly correlated (Voyer & Voyer, 2015). Multicollinearity can result in misleading and unusual results, inflated standard errors, or reduced power of the regression coefficients that create a need for larger sample sizes (Moran et al., 2014). Multicollinearity was assessed by examining the Pearson Product Moment correlation coefficient between the predictor variables. 51 Linearity. Linearity defines the dependent variable as a linear function of the predictor (independent) variables (Loomis, 2014). When a violation occurs, all the estimates of the regression including regression coefficients, standard errors, and tests of statistical significance may be biased (Voyer & Voyer, 2015). Violation of this assumption threatens the meaning of the parameters estimated in the analysis (Voyer & Voyer, 2015). I assessed linearity by a visual inspection of the Normal Probability Plot (P-P) of the Regression Standardized Residual and scatterplot of the residuals. Normality. Multiple regression assumes that variables have normal distributions (Loomis, 2014). Various transformations are used to correct non-normally distributed (Hayes & Preacher, 2014). When assumptions are incorrect, multiple regression errors include normal distribution, and a plot of the values of the residuals will approximate a normal curve (Loomis, 2014). I assessed normality by a visual inspection of the Normal Probability Plot (P-P) of the Regression Standardized Residual and scatterplot of the residuals. Homoscedasticity. The assumption of homoscedasticity refers to the equal variance of errors across all levels of the independent variables (Voyer & Voyer, 2015). The assumption can lead to distortion of the findings and weaken the overall analysis and statistical power of the analysis, which results in an increased possibility of Type I error, erratic and untrustworthy F-test results, and erroneous conclusions (Voyer & Voyer, 2015). Homoscedasticity can be checked by visual examination of a plot of the standardized residuals by the regression standardized predicted value (A. Dewan et al., 52 2014). I assessed homoscedasticity by a visual inspection of the Normal Probability Plot (P-P) of the Regression Standardized Residual and scatterplot of the residuals. Independence of residuals. Independence of residuals refers to the assumption that residuals are independent of one another, which implies that subjects are responding independently (Loomis, 2014). When violations of the independence of errors occur, standard scores and significance tests will not be accurate, and there is increased the risk of Type I error (Voyer & Voyer, 2015). One way to diagnose violations of this assumption is through the graphing technique called boxplots in most statistical software programs (Hayes, & Preacher, 2014). The Normal Probability Plot (P-P) of the Regression Standardized Residual and Scatterplot allow access to outliers, normality, linearity, homoscedasticity, and independence of residuals. I assessed the independence of residuals by a visual inspection of the Normal Probability Plot (P-P) of the Regression Standardized Residual and scatterplot of the residuals. Bootstrapping was conducted to combat the possible influence of any assumptions. Bootstrapping is a statistical technique that allows assigning measures of accuracy to sample estimates (Dovonon, Goncalves, & Meddahi, 2013). Bootstrapping is often used as an alternative to statistical inference based on the assumption of a parametric model when that assumption is in doubt, or where parametric inference is impossible or requires complicated formulas for the calculation of standard errors (Luo, Atamturktur, & Juang, 2012). 53 Interpretation of Inferential Results SPSS output yielded various statistics requiring interpretation. Specific parameters to interpret were (a) R2, (b) F value, (c) Β, (d) SE B, (e) β, t, and sig. (p). In addition, appropriate bootstrap 95% confidence intervals were reported. R2. R2 is a numerical measure of how much variance in the dependent variable accounts for by the predictor variables (Sowinski et al., 2015). R2 can range from 0 to 1, where higher values represent more variance (Rahman, 2013). For example, an R2 value of .17 means the predictor variables account for 17% of the variance in the dependent variable. F. I used the F-ratio of the underlying ANOVA table along with its significance value (Sig. or p-value) to determine if the null hypothesis of the research was accepted or rejected (Norris, Plonsky, Ross, & Schoonen, 2015). The F-ratio provides the significance of all predictor variables; the associated p-value (Sig.), if less than 0.05, confirms the significance of the measure, and could warrant rejection of the null hypothesis (Räz & Sauer, 2015). Β. B is an unstandardized coefficient of the predictor variable (Choi, 2015). The negative or positive sign of the B value could validate the theory of the model. The value of the B value would predict by what factor the value o...
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