Introduction The promise of advanced technology to transform healthcare is underway. We are in an exciting and dynamic period of discovery, and importantly generating knowledge that informs and impacts healthcare organizations, healthcare workers and ultimately patient outcomes. Our innovative study adds to this body of knowledge by examining important and untested relationships. The purpose of this study was to examine the relationships among electronic health record (EHR) adoption stage and hospitalized patients’ satisfaction and adverse outcomes (i.e., Patient Safety Indicators [PSIs], readmissions, length of stay and prolonged length of stay [PLOS]) while accounting for important organizational and nurse factors. Background and Significance Adverse events in hospitalized patients increase patient morbidity and mortality and are costly to individuals, hospitals, and society. A report by the Institute of Medicine (IOM) identified the top 100 healthcare research priorities for the nation; leading the list is research aimed at improving patient safety and the quality of care (IOM, 2009). Yet, despite an increased focus on patient safety since the release of the IOM report To Err is Human there has been minimal improvement in patient safety (IOM, 2001; Leape, et al., 2009; Wachter, 2010a, 2010b). Perhaps most disturbing are findings from a recent large, landmark study which indicated that, despite national attention and substantial resource allocation, there has been no reduction in the rate of preventable adverse inpatient events over the last several years (Landrigan et al., 2010). In fact, the rate of preventable harm to patients has remained relatively stable at 40.2 adverse events per 1,000 patient days (Landrigan et al., 2010). These sustained rates of inpatient adverse events are detrimental to individuals, hospitals, and society, costing our healthcare system more than 4.4 billion dollars per year (U.S. Department of Health and Human Services (DHHS), 2010a). Tolerance with this status quo is waning. Payers, regulators, insurers and consumers are demanding the delivery of safe healthcare with positive outcomes. Consumer concern became evident in a seminal 2006 national survey of public perspectives on ways to improve healthcare in which 42 percent of respondents reported experiencing inefficient, poorly coordinated or unsafe care in the prior two years (Schoen, How, Weinbaum, Craig & Davis, 2006). Concern remained evident in a 2011 international survey in which up to 25 percent of U.S. respondents reported experiencing an actual error in care (Schoen et al; 2011). Importantly, a consequence of low quality healthcare and poor work environments is decreased patient satisfaction (Kutney-Lee et al., 2009; Mitchell & Shortell, 1997; Schubert et al., 2008). The confluence of these factors has led to a demand for healthcare reform. In response to this demand, the Affordable Care Act (ACA) of 2010 established the Hospital Value Based Purchasing (VBP) program, a Center for Medicare and Medicaid Services (CMS) initiative that rewards acutecare hospitals with incentive payments for the quality of care provided. VBP places 2 percent of hospital Medicare reimbursement at risk by metrics of quality, outcomes, and experiences of care. Reimbursement associated with patient satisfaction is 30% of the at-risk base diagnosis-related group (DRG) operating payment. The ACA affects payment for inpatient stays in 2,985 U.S. hospitals (CMS, 2013). To further support healthcare improvement the American Recovery and Reinvestment Act (ARRA) of 2009 includes a provision for the Health Information Technology for Economic and Clinical Health (HITECH) Act (CMS, 2012a, CMS, 2012b). The belief that health information technology (IT) will foster healthcare reform is supported by a $35 billion federal investment for HITECH programs, including demonstration of Meaningful Use (MU), (US DHHS, 2010b, Office of the National Coordinator (ONC), 2010). MU goals were designed to occur in stages. The first phase, Stage 1 Meaningful Use (2011-2012), focuses on data capture and sharing. The second phase, Stage 2 (2013-2014), advances stage 1, and includes advanced clinical processes and clinical decision support, and focuses on demonstrating health system improvement through wider adoption and process improvement. The third phase, Stage 3 (2015), focuses on transforming health care through health IT. Finally, beyond 2015, a learning system of transformed health care will be realized (ONC, 2010). Organizations that accept Medicare and Medicaid dollars are eligible to participate in the Electronic Health Record (EHR) incentive programs and receive EHR incentive payments beginning with a $2 million base payment, with over $5 billion paid to date (CMS, 2012b). Eligible hospitals that do not minimally demonstrate MU Stage 1 will be subject to Medicare penalty payment adjustments in 2015 (CMS, 2012b, US DHHS, 2010a, HIMSS, 2015). Fully meeting MU Stage 1 objectives includes three of five stages of EHR adoption (Appari, Johnson & Anthony, 2013; Garets & Davis 2006; Jha et al., 2009), (Table 1). Hospitals at EHR Stage 0 may have some clinical systems in place but are considered rudimentary and do not have all three basic ancillary systems installed. Hospitals at EHR Stage 1 have adopted all three core ancillary department information systems (laboratory, radiology, pharmacy). Hospitals at EHR Stage 2 have adopted all of EHR Stage 1 applications and additionally have features such as clinical data and decision support systems, clinical data repository and may be health information exchange capable. Hospitals at EHR Stage 3 have adopted all of EHR Stage 1 and EHR Stage 2 applications as well as nursing and clinical documentation, order entry management and features such as electronic medication administration record application and picture archive and communication systems. MU Stage 2 includes hospitals at EHR Stage 4 that achieved all the preceding stages and have Computerized Physician Order Entry (CPOE) and advanced clinical decision support (clinical protocols). This classification is based on the HIMSS Electronic Medical Record Adoption Model (EMRAM) and the taxonomy developed by an expert consensus panel (Garets & Davis 2006; Jha et al., 2009). Undoubtedly, these Acts have challenged hospital administrators as they appraise the evidence and formulate how to direct valuable human and material resources in efforts to meet the provisions of both the ARRA and the ACA. The use of health IT is one promising system-level initiative that may improve provider performance and interdisciplinary communication, reduce adverse patient events, and ultimately improve patient satisfaction with care (Elnahal, Joynt, Bristol & Jha 2011; Himmelstein, Wright & Woolhandler, 2010; Staggers, Weir & Phansalkar, 2008). Some evidence suggests that technology does enhance communication and decision-making and positively impacts provider performance and a variety of patient outcomes, including patient satisfaction (DesRoches, Miralles, Buerhaus, Hess & Donelan, 2011; Elnahal et al., 2011; Kazley, Diana, Ford & Menachemi, 2012; Kutney-Lee & Kelly, 2011). However, an evidence report published by the Agency for Healthcare Research and Quality (AHRQ) concluded too few studies link organizational structures and care processes with outcomes when examining the positive effects of EHR (Shekelle, Morton, & Keeler, 2006). Despite widespread attention and funding, major gaps in the evidence persist, including exploring the influence of EHRs across differing organizational climates, using relatively small samples of hospitals, and the absence of any multisite studies to disentangle the complex relationships among EHR, the delivery of nursing care, and patient outcomes. By leveraging existing databases, this study addressed these important gaps in the empirical literature by exploring the relationships among EHR adoption stage, patient satisfaction, and adverse patient outcomes while accounting for the important features of the nursing practice environment, such as management support, teamwork and communication, and staffing, in a sample of 70 New Jersey hospitals. Objective The purpose of this study was to examine the relationships among electronic health record (EHR) adoption stage and hospitalized patients’ satisfaction and adverse outcomes (i.e., Patient Safety Indicators [PSIs], readmissions, length of stay and prolonged length of stay [PLOS]). Materials and Methods A secondary analysis of cross-sectional data was conducted, including the following measures compiled from four sources: ( 1) adverse patient events and PSIs using PSI algorithm (version 3.1) from the Healthcare Cost and Utilization Project, State Inpatient Database; ( 2) patient satisfaction survey data from Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS), Centers for Medicare and Medicaid Services (CMS) data; ( 3) EHR adoption stage using the EMR Adoption Model (EMRAM) scale from the Healthcare Information and Management Systems Society (HIMSS) Dorenfest Institute, (Garets & Davis, 2008); and ( 4) nurse practice environment scores using the Practice Environment Scale-Nursing Work Index (PES-NWI), (Lake, 2002), and missed nursing care scores from the New Jersey nurse survey data. All study data were from 2006, with the exception of HCAHPS data with a release date of March 2008 which captures data from July 2006 through June 2007. These years were selected so the data was contemporaneous with the unique nursing variable dataset collected only in 2006. The databases were merged using unique hospital level identifiers. The study design included adult patients admitted to New Jersey hospitals and nurses employed in those same hospitals. Individuals under the age of 21 were excluded from this study as the focus of the study was adult patients and nurses who are typically older than 21 years. No gender, racial or ethnic groups were excluded. Ethics Approval The Institutional Review Board of Rutgers, The State University of New Jersey approved this study. Data Sources and Variables Patients. Patient adverse events were derived from the 2006 New Jersey State Inpatient Database, which contains inpatient discharge abstracts and more than 100 clinical and nonclinical data elements such as facility identification number, patient demographics, admission and discharge information, payment source, total charges, and length of stay. In addition, International Classification of Diseases, 9th edition, Clinical Modification (ICD-9-CM) codes are recorded for both the principal diagnosis and principal surgical procedures. An expanded number of diagnosis and procedure codes and clear demarcation of presenting and secondary (comorbid) diagnoses are unique and important features of the discharge data that permit enhanced risk adjustment (Healthcare Cost and Utilization Project (HCUP), 2012a).
