To examine the relationships among Electronic Health Record (EHR) adoption and adverse outcomes and satisfaction in hospitalized patients.
Materials and Methods
This secondary analysis of cross-sectional data was compiled from four sources: (1) State Inpatient Database from the Healthcare Cost Utilization Project; (2) Healthcare Information and Management Systems Society (HIMSS) Dorenfest Institute; (3) Hospital Consumer Assessment of Healthcare Providers and Systems Survey (HCAHPS) and (4) New Jersey nurse survey data. The final analytic sample consisted of data on 854,258 adult patients discharged from 70 New Jersey hospitals in 2006 and 7,679 nurses working in those same hospitals. The analytic approach used ordinary least squares and multiple regression models to estimate the effects of EHR adoption stage on the delivery of nursing care and patient outcomes, controlling for characteristics of patients, nurses, and hospitals.
Advanced EHR adoption was independently associated with fewer patients with prolonged length of stay and seven-day readmissions. Advanced EHR adoption was not associated with patient satisfaction even when controlling for the strong relationships between better nursing practice environments, particularly staffing and resource adequacy, and missed nursing care and more patients reporting “Top-Box,” satisfaction ratings.
This innovative study demonstrated that advanced stages of EHR adoption show some promise in improving important patient outcomes of prolonged length of stay and hospital readmissions. Strongly evident by the relationships among better nursing work environments, better quality nursing care, and patient satisfaction is the importance of supporting the fundamentals of quality nursing care as technology is integrated into practice.
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 acute-care 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 multi-site 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.
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.
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).
Nursing-sensitive PSIs that were examined included: (a) PSI 2 death in low-mortality diagnostic related groups; (b) PSI 4 failure to rescue; (c) PSI 13 postoperative sepsis; (d) PSI 7 central venous catheter-related blood stream infection; and (e) PSI 8 postoperative hip fracture. These PSIs were selected following review of empirical definitions (including reliability and minimum bias, coding and construct validity, area level or provider level metric), empirical performance indicators occurring at rates sufficient to detect a difference, and literature review and theoretical rationale indicating they are sensitive to nursing care (HCUP, 2012a, 2012b). Early hospital readmission was operationally defined as an all-cause readmission to the same New Jersey hospital facility from which the patient was discharged within seven days (HCUP, 2012a, 2012b). Prolonged length of stay (PLOS) identifies the distribution point at which the discharge rate declines after the daily discharge rates peak (Silber et al., 2009). The daily patient discharge rate was calculated as 1/LOS (length of stay) consistent with previous work (Silber et al., 1999, Silber et al., 2009). The prolongation point for hospital discharges, or day of deceleration, was identified by Kernel-Density plots constructed for the discharge rates by each Major Diagnostic Categories and defined as the day after the prolongation point. In these data, therefore, the patient’s hospital stay is considered prolonged if it exceeds the prolongation point (day of hospitalization), identified for each Major Diagnostic Categories by the Kernel–Density plots. All data were examined at the hospital level and therefore expressed as PSI rates per 1,000 discharges or percentage of patient readmissions or PLOS per hospital.
Patient satisfaction was operationally defined as the hospital level average “Top-Box” score from the HCAHPS hospital rating measure (CMS, 2012b). HCAHPS is a national, standardized database of patients’ hospital experiences in short-term, acute care hospitals. The 27-item survey includes categories on communication with doctors and nurses, responsiveness of hospital staff, pain management, cleanliness and quietness of the hospital environment, and medication and discharge instructions. It is reported as a set of ten measures including 6 summary measures, 2 single items, and 2 global ratings. HCAHPS “Top-Box” is defined as the most positive response to the HCAHPS survey questions, including the response “9” or “10” for the overall hospital rating item (CMS, 2012b). Individual patient responses are aggregated to the hospital level by HCAHPS following risk-adjustment for patient mix and mode of administration (CMS, 2012b).
Nurses. The nursing practice environment was measured using the Practice Environment Scale of the Nursing Work Index (PES-NWI), a 5 domain, 31-item 4-point Likert-type (ranging from strongly disagree to strongly agree) instrument that asks nurses to characterize the presence of features in their work environment. Subscales from the PES-NWI used in this study include: nurse participation in hospital affairs, nursing foundations for quality care, nurse manager ability, leadership, and support of nurses, staffing and resource adequacy and collegial nurse-physician relations (Lake, 2002). Published internal consistency coefficients (Cronbach’s alphas) for these subscales range from .71 to .84 and validity of this measure is extensively supported in the literature (Gajewski et al., 2010; Lake, 2002; Lake, 2007; Lake & Friese, 2006; Liou & Cheng, 2009).
Nurses were also asked to report if any activities, from a set of 12 necessary care activities were left undone during their last shift due to lack of time. The activities included: (1) adequate surveillance (directly observation/monitoring) of patients, (2) teaching patients or family, (3) preparing patients and families for discharge, (4) providing comfort/talk with patients, (5) adequately document nursing care, (6) administering medications on time, (7) skin care, (8) oral hygiene, (9) pain management, (10) treatment and procedures, (11) coordinating care and (12) developing or updating nursing care plans. Construct validity of this measure has been demonstrated in that scores have been found to be associated in the theoretically expected direction with RN staffing, quality of care, and frequency of adverse events in hospitals (Sochalski, 2001; 2004). Nurses’ reports of the work environment and missed nursing care, although collected at the individual nurse level, are customarily aggregated to produce a hospital-level metric as was done in this study (Aiken, Cimiotti, Sloane, Flynn & Neff, 2011).
Hospitals. EHR adoption data were obtained from the 2006 HIMSS Analytic Database. HIMSS annually surveys a sample of U.S. nonfederal acute care hospitals including independent hospitals and those within a healthcare delivery system. Providing data on more than 5,100 hospitals, the HIMSS database is the most comprehensive collection of information technology data and has been used in previous research on health IT (Kazley & Ozcan, 2008; McCullough, Casey, Moscovice, & Prasad, 2010). EHR adoption was operationally defined as a hospital’s total cumulative score on the Electronic Medical Record Adoption Model scale (EMRAM) ranging between 0-4 (Table 1) where a higher score indicates more advanced adoption of technology (HIMSS, 2015).
Control variables. The potential confounding variables hypothesized to affect patient outcomes included nurse staffing levels, nurse education, hospital size, teaching status, high technology status (defined as facilities with open-heart surgery, major organ transplant services, or both), and geographic categories (Aiken, Clarke & Sloane, 2002; Aiken, Clarke, Sloane, Sochalski & Silber, 2002; Appari, Johnson & Anthony, 2013; Elnahal, Joynt, Bristol & Jha, 2011; Himmelstein, Wright & Woolhandler, 2010). These data were derived from the New Jersey Nurse Survey and originally obtained from the American Hospital Association Annual Survey. Patient risk-adjusted covariates were extracted from the State Inpatient Databases (SIDS) and include age, sex, race, insurance type, and ICD9-CM primary and secondary diagnosis codes. The AHRQ risk adjustment method, based on the Elixhauser method, was employed and includes a comprehensive set of 30 comorbidities (Elixhauser, Steiner, Harris & Coffey, 1998).
Data management and analysis. Prior to analysis, all datasets were aggregated to the hospital level. The final analytic sample included 854,258 patients and 7,679 nurses in 70 New Jersey hospitals. The relationship between potentially confounding variables (control variables) and their respective dependent variables were examined using bivariate Pearson or Spearman correlations, as determined by the Shapiro-Wilk test of normality. Those showing significant relationships (p < .05) were retained for inclusion in the multivariable models as control variables. The presence of multicollinearity was identified by variance inflation factor diagnostics (VIF >10). In such cases, only one variable was included from the set of correlated variables. Following these steps, the number of variables retained in all multivariable models was based on rules for regression modeling (Harrell, 2001). Because nurse, patient, and EHR data were clustered in hospitals, appropriate statistical methods for analyzing clustered data were employed (Wears, 2002).
Data were assessed for outliers and missing data. Data on the key variables EMRAM, nursing practice environment, missed nursing care, PSIs and PLOS were available for 70 New Jersey hospitals. In 2006, 51 hospitals submitted readmissions data, and two were excluded from the readmission models due to incomplete data. All 41 hospitals that submitted HCAHPS data were included in the patient satisfaction models. Ordinary least squares and multiple regression models were used in the analytic approach to estimate the effects of EHR adoption stage on the delivery of nursing care and patient outcomes and controlled for characteristics of patients, nurses, and hospitals. Simple unadjusted OLS regression models were used, followed by adjusted models using the retained control variables identified by the steps previously described. These models were assessed for heteroscedasticity, run with robust standard errors (Huber-White) if indicated, and residuals were examined. Data were analyzed using STATA/MP 12.1 software. The level of significance for testing was set at .05 and standardized coefficients (β) are reported.
Patient, Nurse, and Hospital Characteristics
The final analytic sample and unit of analysis was 70 New Jersey hospitals; data was available from 854,258 patients and 7,679 nurses. The majority of study patients were male (59 percent), white (66 percent), and insured (83 percent) with an average age of 59 years. The most common comorbidities were chronic pulmonary disease (15.5%), uncomplicated diabetes (15.4 percent) and fluid and electrolyte disorders (15.2 percent). Slightly more than half of the nurses held specialty certification (52 percent), nearly half earned a BSN degree (44%), and they cared for, on average, six patients per shift. The majority of the 70 hospitals included in this study were below EMRAM Stage 3 (63 percent), had 250 beds or more (52 percent), were not high technology (75 percent), and were either non-teaching (46 percent) or minor teaching hospitals (43 percent).
EHR Adoption Stage and Adverse Outcomes
The unadjusted effect of testing the relationship between EHR adoption stage and the patient outcome of readmission within seven days was significant ( R2 = .09, F (1, 47) = 4.70, p = .03). Bivariate correlations did not significantly identify any potential confounders that required additional testing using adjusted models. However, the Breusch-Pagan test demonstrated evidence of heteroscedasticity (p < .01); thus, the model was conservatively estimated with robust standard errors and was not significant (p = .06) (Table 2).
The unadjusted effect of testing the relationship between EHR and PLOS was not significant (R2 = .003, F (1, 68) = 0.21, p = .65). However, when adjusting for control correlates of PLOS (patient comorbidity, patient age, nurse staffing, and hospital technology status), the adjusted effect was significant (R2 = .462, F (4, 63) = 6.54, p < .01), with EHR adoption stage being a significant contributor (β = -.21, p = .03). For every standard deviation unit (SD = 1.39) increase in EHR adoption stage, PLOS decreased by .21 standard deviation units (SD = 0.05). EHR adoption stage was not a significant predictor of other adverse outcomes.
EHR Adoption Stage and Patient Satisfaction
There was a statistically significant relationship between EHR adoption level and patient satisfaction in acute care hospitals in one of the ten patient satisfaction outcomes: “yes, given discharge information.” The model included the control variables of patient race, being insured, and nurse staffing. Findings indicate that the overall model was significant (R2 = .04, F (4, 36) = 7.56, p < .01), with EHR adoption stage significantly contributing to this outcome (β = -.31, p = .02). However, this finding was in the inverse direction and indicated that higher EHR adoption stages were predictive of lower percentages of patients who responded “yes, given discharge information” (Table 3).
As the primary purpose of the study was to examine the effect of EHR adoption levels on patient outcomes, and because the evident and strong relationship among the nursing practice environment and missed nursing care and outcomes might confound this relationship, these variables were controlled to isolate the effect of EHR. Models were constructed to examine with greater precision whether advanced EHR technology is positively related to patient satisfaction outcomes by controlling for the statistically significant effects of the nursing practice environment and missed nursing care. The nursing practice environment dimension of staffing and resource adequacy was specifically tested secondary to the evident relationship between this dimension of the nursing work environment and patient satisfaction. Results indicate that higher EHR adoption stages were predictive of one satisfaction outcome, lower percentages of patients who respond “yes, given discharge information” (β = -.27, p < .05) when the strong relationships among the nursing practice environment and missed care and satisfaction were held constant (Table 4).
Findings from this study suggest that an inverse relationship exists between EHR adoption stage and the patient outcomes of PLOS and readmissions. The findings of this study did not suggest that increased EHR adoption stages are related to decreased adverse outcomes of PSIs outcomes or increased patient satisfaction. Additional analysis was conducted to examine if the strong relationships among the nursing work environment, missed nursing care and patient satisfaction were confounding the effect of EHR on patient satisfaction outcomes. Notably, in the final adjusted models only one satisfaction outcome, the patient response of “being given discharge information,” reached the level of statistical significance. This relationship, however, was in the opposite direction of that theorized.
These important relationships have not been tested in prior studies, and as such these novel findings may indicate that at the EMRAM stages 0-4 of EHR adoption, the patient satisfaction benefit is tempered by staffing and resource adequacy. There is little to no extant theoretical or empirical support for this unexpected finding. One possible explanation is that the relationship between EHR and satisfaction may be moderated by insufficient resources, which in the presence of new technology has the effect of reducing workflow and time efficiencies (Huber, 1990; Poissant, Pereira, Tamblyn & Kawasumi, 2005). Methodologically, it is unknown if achievement of these EHR adoption stages is new in these settings; consequently the impact on nursing processes of care and workflow is unknown. In order to optimize the positive effect of EHR on patient outcomes, organizational strategies and resources must be committed to ease and guide the transition to this technology (Huber, 1990; Walker et al., 2008). Although this study accounted for organizational factors that may serve as indicators of available resources (teaching status, hospital size, geographic location, and technology status), the comprehensive nature and extent of the organizational strategy to implement EHR technology was unknown.
Importantly, these findings suggest that sufficient staffing and resources, as rated by the nurses, are essential for advanced EHR adoption and patient reported outcomes of satisfaction. These findings are consistent with extant literature and may also suggest that patient benefits of advanced technology will only be realized in context of sufficient human resources (Furukawa, Raghu & Shao, 2011; Jha et al., 2009; Kazley & Ozcan, 2007; Walker et al., 2008). These novel findings warrant further investigation.
The implications of the knowledge generated by this study are significant for patients, nurses, administrators, and policy makers, particularly in context of the shifting healthcare delivery and nursing practice landscape. Across the U.S., hospitals and nurses have made significant efforts to achieve higher MU stages. Despite marked progress , nearly 30 percent of U.S. hospitals that submitted data to HIMSS in 2015 remain at or below EMRAM middle stage (3) of adoption, equivalent to Meaningful Use Stage 1 objectives (Appari et al., 2013; HIMSS, 2015). In this 2006 study of New Jersey hospitals, 37% had achieved EMRAM stage 3; one-third of these achieving the next cumulative level of EMRAM stage 4. Thus, findings from this study are highly relevant and timely as we are in a period of rapidly accelerating advancement and adoption.
These EMRAM stages correspond to MU Stages 1 and 2; thus, there are significant and timely implications of these study findings for both current and future nursing practice and hospital payment. Though the data indicate progress as the majority of hospitals in the New Jersey 2006 baseline data were below EMRAM stage 3 while more recent 2015 national data indicates approximately 30 percent remain at or below stage 3, a possible critical point may not be reached to fully demonstrate the potential impact of EHR, not only in New Jersey but across the United States.
Importantly, achieving EMRAM stage 3, including nursing documentation which is the primary mechanism of electronic communication, is essential for safe transitions of care. As such, outcomes that are more sensitive to good communication and care transitions, such as readmissions, PLOS, and patient reports of “yes, given discharge information,” may conceivably be early indicators of the impact of advanced EHR adoption. Implications for hospital administrators and nursing practice are evident and congruent with best practice guidelines, suggesting the engagement of nursing staff in the development, use, and ongoing feedback of documentation systems and allocation of resources for ongoing training and systems evaluation and improvement are necessary to optimize the system (Blavin, Ramos, Shah & Devers, 2013). These findings suggest that multi-level interventions are required for improving patient care and outcomes. This study demonstrates that EHR adoption does have a positive, adjusted effect on PLOS, and it is theoretically plausible that, as features of advanced technology become embedded in hospitals and other healthcare organizations, positive benefits may extend to additional patient outcomes and institutional settings across the continuum of care (Huber, 1990; Powell-Cope et al., 2008).
In summary, the dual demands of the legislative provisions to implement health IT and improve quality outcomes may exacerbate the difficult decisions hospital administrators need to make regarding allocation of valuable resources. There is a strong financial incentive to integrate technology into the healthcare work environment and sound theoretical rationale to believe that through enhanced communication, improved data management and better transitions of care that EHRs will benefit patients and providers alike. Broader implications of these study findings for administrators suggest organizations that have strong fundamentals of quality nursing care in place may realize improved patient satisfaction outcomes that translate into real dollars through the VBP program. Additionally, implications of this study for administrators and policy makers suggest that meeting the demands of the ARRA and ACA may not be mutually exclusive. Rather, in an iterative manner, a supportive nursing work environment that is adequately staffed and resourced may improve patient satisfaction, leading to better organizational financial health. These fiscal resources can in turn be used by organizations to continue advancing EHR adoption, EHR implementation, and the transformation of health care in the U.S.
Despite these novel findings and important implications, several limitations of this study should be acknowledged. This study was cross-sectional and as such correlations, relationships, and associations between variables of interest were examined, but causality could not be ascertained. The precision of the PSI data was dependent on the documentation in the record and coding applied by trained medical coders; thus, discrepancies in data and accuracy could have existed at the hospital level (AHRQ, 2004, 2010; Zhan & Miller, 2003). Methodologically, patient responses cannot be linked temporally to specific hospital EHR adoption timelines. This study was designed to mitigate this possible limitation by including data from HCAHPS release date of March 2008, which captures data from July 2006 through June 2007. However, it remains unknown if this negative finding may in part reflect early EHR adoption and the attendant human factors and operational challenges thought to effect the use of this technology and subsequent proposed benefits, by nurses and patients alike (Powell-Cope et al., 2008). EHR data were obtained from HIMSS and patient satisfaction from HCAHPS; both are voluntary reporting systems, and as such these data were subject to self-selection bias. Finally, analysis at the hospital level limits sample size, and though the power analysis indicated the sample size was sufficient and significant effects identified, the sample size of hospitals in the patient satisfaction models may have been a limitation.
In this study of 70 acute care hospitals, higher levels of EHR adoption were significantly and independently associated with fewer incidences of PLOS and partially associated with lower rates of seven-day re-hospitalization. This is the first study to examine this relationship between EHR adoption stage and PLOS, thus extending this knowledge. These findings support the promising role of EHRs in improving patient outcomes. Importantly, however, findings also indicate that a supportive nursing practice environment, including the domain of adequacy of nursing staff and resources, is significantly and independently associated with lower levels of missed nursing care and higher levels of patient satisfaction, with and without adjusting for EHR levels. Thus, findings from this study indicate that a multi-faceted approach that includes technology, such as EHR, as well as system-wide nursing supports are needed to improve patient care outcomes in acute care hospitals. In summary, these findings add to a growing body of knowledge in nursing research that identifies modifiable technologic and nursing-focused system factors that are critical to improving patient care and outcomes.
Agency for Healthcare Research and Quality (AHRQ) (2004). AHRQ Quality Indicators – Guide to Patient Safety Indicators. Rockville, MD: Agency for Healthcare Research and Quality, 2003. Version 2.1, Revision 2, AHRQ Pub.03-R203
Agency for Healthcare Research and Quality (AHRQ), (2010). AHRQ quality indicators: Composite measures user guide for the patient safety indicators (PSI). Version 4.2 (September, 2010). Department of Health and Human Services Agency for Healthcare Research and Quality. Retrieved from http://www.qualityindicators.ahrq.gov
Aiken, L. H., Cimiotti, J. P., Sloane D. M., Flynn, L. & Neff, D. F. (2011). Effects of nurse staffing and nurse education on patient deaths in hospitals with different nurse work environments. Medical Care, 49 (12): 1047-53.
Aiken, L.H., Clarke, S.P., & Sloane, D.M. (2002). Hospital staffing, organizational, and quality of care: Cross-national findings. Nursing Outlook, 50, 187-194. doi: 10.1067/mno.2002.126696.
Aiken, L. H., Clarke, S. P., Sloane, D. M., Sochalski, J., & Silber, J. H. (2002). Hospital nurse staffing and patient mortality, nurse burnout and job dissatisfaction. Journal of the American Medical Association, 288(16), 1987-1993.
Appari, A., Johnson, E. & Anthony, D.L. (2013). Meaningful use of electronic health record systems and process quality of care: Evidence from a panel analysis of U.S. acute-care hospitals. Health Services Research, 48(2), 354-375.
Blavin, F., Ramos, C., Shah, A., & Devers, K. (2013). Final report: Lessons from the literature on electronic health record implementation. Retrieved from https://www.healthit.gov/sites/default/files/hit_lessons_learned_lit_review_final_08-01-2013.pdf
Centers for Medicare & Medicaid Services (CMS) (2012a). EHR Incentive program:
Active Registrations. Retrieved from https://www.cms.gov/Regulations-and-Guidance/Legislation/EHRIncentivePrograms/Downloads/Oct_IncentiveProgramPayment_Registration_SummaryReport.pdf
Centers for Medicare & Medicaid Services (CMS) (2012b). Hospital care quality information from the consumer perspective. Retrieved from http://www.hcahpsonline.org/home.aspx
Centers for Medicare & Medicaid Services (CMS) (2013). Hospital value-based purchasing program. Retrieved from http://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Hospital-Value-Based-Purchasing/
DesRoches, C.M., Miralles, P., Buerhaus, P., Hess, R., & Donelan, K. (2011). Health information technology in the workplace. The Journal of Nursing Administration, 41(9), 357-364.
Elixhauser, A., Steiner, C., Harris, D. R., & Coffey, R. M. (1998). Comorbidity measures for use with administrative data. Medical Care, 36, (1), 8-27.
Elnahal, S.M., Joynt, K.E., Bristol, S.J., & Jha, A.K., (2011). Electronic health record functions differ between the best and worst hospitals. American Journal of Managed Care, 17(4), e121-e147.
Furukawa, M.F., Raghu, T.S. & Shao, B.M. (2011). Electronic medical records, nurse staffing, and nurse-sensitive patient outcomes: evidence from the national database of Nursing Quality Indicators. Medical Care Research and Review, 68(3), 311-331. doi: 10.1177/1077558710384877
Gajewski B J, Boyle D K, Miller P A, et al. (2010). A multilevel confirmatory factor analysis of the practice environment scale. Nursing Research,59(2), 147-153.
Garets, D., & Davis, M. (2006). Electronic medical records vs. electronic health records: Yes, there is a difference. Chicago, IL. HIMSS Analytics. Retrieved from https://app.himssanalytics.org/docs/WP_EMR_EHR.pdf
Harrell F.E. (2001). Regression modeling strategies: With applications to linear models, logistic regression, and survival analysis. New York, N.Y.: Springer.
HCUP State Inpatient Databases (SID), (2012a). Healthcare Cost and Utilization Project (HCUP). New Jersey, 2006- 2007. Agency for Healthcare Research and Quality, Rockville, MD. Retrieved from https://www.hcup-us.ahrq.gov/sidoverview.jsp
HCUP (2012b). SID Description of Data Elements All States, Healthcare Cost and Utilization Project (HCUP). Agency for Healthcare Research and Quality, Rockville, MD. Retrieved from https://www.hcup-us.ahrq.gov/db/state/siddbdocumentation.jsp.
Healthcare Information and Management Systems Society (HIMSS). (2015). EMR Adoption Model. Retrieved from http://www.himssanalytics.org/provider-solutions#block-himss-general-himss-prov-sol-emram
Himmelstein D U, Wright A, Woolhandler, S. (2010). Hospital computing and the costs and quality of care: A national study. American Journal of Medicine, 123, 40-46.doi: 10.1016/j.amjmed.2009.09.004
Huber, G. P. (1990). A theory of the effects of advanced information technologies on organizational design, intelligence, and decision making. Academy of Management Review, 15(1), 47-71.
Institute of Medicine (IOM) (2001). To Err is Human: Building a Safer Health System. Washington, DC: National Academies Press.
Institute of Medicine (IOM) (2009). Comparative Effectiveness Research. Washington, DC: National Academies Press.
Jha, A. K., DesRoches, C. M., Campbell, E. G., Donelan, K., Rao, S. R., Ferris, T. G.,…Blumenthal, D. (2009). Use of electronic health records in U.S. hospitals. New England Journal of Medicine, 350(16), 1628-1638.
Jha, A.K., Orav, J., Zheng, J. & Epstein, A.M. (2008). Patient’s perceptions of hospital care in the United States. New England Journal of Medicine, 359(18), 1921-1931.
Kazley, A.S., Diana, M.L., Ford, E.W., & Menachemi, N. (2012). Is electronic health record use associated with patient satisfaction in hospitals? Health Care Management Review, 37(1), 23-30. doi: 10.1097/HMR.0b0113e3182307bd3
Kazley, A. S. & Ozcan, Y. A. (2008). Does hospital electronic medical record use increase health care quality? An examination of three clinical conditions. Medical Care Research Review, 65(4), 496-513.
Kazley, A. S. & Ozcan, Y. A. (2007). Organizational and environmental determinants of hospital EMR adoption: A National Study. Journal of Medical Systems, 31, 375-384. doi: 10.1007/s10916-007-9079-7
Kutney-Lee, A. & Kelly, D. (2011). The effect of hospital electronic health record adoption on nurse-assessed quality of care and patient safety. Journal of Nursing Administration, 41(11), 466-472.
Kutney-Lee, A., McHugh, M.D., Sloane, D.M., Cimiotti, J.P., Flynn, L., Neff, D.F. & Aiken, A.H. (2009). Nursing: A key to patient satisfaction. Health Affairs, 28(4), w669-w677. doi:10.1377/hltaff.28.4w669
Lake, E.T. (2002). Development of the Practice Environment Scale of the Nursing Work
Index. Research in Nursing & Health, 25(3), 176-186.
Lake, E.T. (2007). The nursing practice environment: Measurement and evidence. Medical Care Research and Review, 64(2), 104S-122S. doi: 10.1171/107755870799253
Lake E T, Friese C R. (2006). Variations in nursing practice environments. Nursing Research, 55(1), 1-9.
Landrigan, C.P., Parry, G.J., Bones, C.B., Hackbarth, A.D., Goldmann, D.A., & Sharek, P.J. (2010). Temporal trends in rates of patient harm resulting from medical care. New England Journal of Medicine, 363(26), 2124-2134.
Leape, L., Berwick, D., Clancy, C., Conway, J., Gluc, J., Guest, J., Lawrence, D., Morath, J., O’Leary, D. O’Neil, P., Pinakiewicz, D. & Isaac, T. (2009). Transforming health care: a safety imperative. Quality and Safety in Health Care, 18(6), 424-428.
Liou S. & Cheng, C. (2009). Using the Practice Environment Scale of the Nursing Work Index on Asian nurses. Nursing Research, 58(3), 218-225
McCullough, J. S., Casey, M., Moscovice, I., & Prasad, S. (2010). The effect of health information technology on quality in U.S. hospitals. Health Affairs, 29(4), 647-654.
Mitchell, P. H. & Shortell, S. M. (1997) Adverse outcomes and variations in organization and delivery of care. Medical Care, 35(11, Suppl.), NS19-N32.
Office of the National Coordinator for Health Information Technology (ONC), (2010). Federal Health Information Technology Strategic Plan 2011-2015 . Retrieved from http://www.healthit.gov/policy-researchers-implementers/health-it-strategic-planning
Poissant, L., Pereira, J., Tamblyn, R., & Kawasumi, Y. (2005). The impact of electronic health records on time efficiency of physicians and nurses: A systematic review. Journal of the American Medical Informatics Association, 12(5), 505-516. doi: 10.1197/jamia.M1700
Powell-Cope, Nelson, A.L., & Patterson, E.S. (2008). Patient care technology and safety.
In Patient Safety and Quality: An Evidence-Based Handbook for Nurses (AHRQ Publication No. 08-0043), (Chapter 50), Agency for Healthcare Research and Quality: Rockville, MD. Retrieved from http://www.ahrq.gov/qual/nurseshdbk/
Schubert, M., Clarke, S.P., Glass, T.R., Schaffert-Witvliet, B., S & DeGeest, S. (2009). Identifying thresholds for relationships between impacts of rationing of nursing care and nurse- and patient-reported outcomes in Swiss hospitals: A correlational study. International Journal for Nursing Studies, 46, 884-893. doi: 10.1016/j.ijnurstu.2008.10.008
Schubert, M., Glass, T.R., Clarke, S.P., Aiken, L.H., Schaffert-Witvliet, B., Sloane, D.M. & DeGeest, S. (2008). Rationing of nursing care and its relationship to patient outcomes: the Swiss extension of the International Hospital Outcomes Study. International Journal for Quality in Health Care, 20(4), 227-237.
Schoen, C., How, S.K.H., Weinbaum, I., Craig, J.E. & Davis, K. (2006). Public views on shaping the future of the U.S. health system. The Commonwealth Fund, NY, NY. Retrieved from http://www.commonwealthfund.org/
Schoen, C., Osborn, R., Squires, D., Doty, M., Pierson, R. & Applebaum, S. (2011). New 2011 survey of patients with complex care needs in eleven countries finds that care is often poorly coordinated. Health Affairs, 30(12), 2437-2448. doi: 10.1377/hltaff.2011.0923
Shekelle PG, Morton SC, & Keeler EB. (2006). Costs and Benefits of Health Information Technology. Evidence Report/Technology Assessment No. 132. (Prepared by the Southern California Evidence-based Practice Center under Contract No. 290-02-0003.) AHRQ Publication No.06-E006. Rockville, MD: Agency for Healthcare Research and Quality. April 2006.
Silber, J.H., Rosenbaum, P.R., Rosen, A.K., Romano, P.S., Itani, K.M.F., Cen, L., Mi, L., Halenar, M.J., Even-Shoshan, O., & Volpp, K.G. (2009). Prolonged hospital stay and the resident duty hour rules of 2003. Medical Care, 47(12), 1191-1200. doi:10.1097/MLR.0b013e3181adcbff.
Silber, J.H., Rosenbaum, P.R., Koziol, L.F., Sutaria, N., Marsh, R.R. & Even-Shoshan, O. (1999). Conditional length of stay. Health services Research, 34(1), 349-363.
Sochalski, J. (2001). Quality of care, nurse staffing, and patient outcomes. Policy, Politics, & Nursing Practice, 2(1), 9-18.
Sochalski, J. (2004). Is more better? The relationship between nurse staffing and the quality of nursing care in hospitals. Medical Care, 42(Suppl. 2), 67-73.
Staggers N., Weir C., & Phansalkar S. (2008). Patient safety and health information
technology: Role of the electronic health record. In R. G. Hughes (Ed.), Patient safety and quality: an evidence-based handbook for nurses (p. 91-133). Rockville, MD: Agency for Healthcare Research and Quality.
U.S. Department of Health and Human Services (DHHS), (2010a). Office of Inspector General.Adverse events in hospitals: National incidence among Medicare beneficiaries (OEI Publication No, 06-09-00090). Retrieved from http://oig.hhs.gov/oei/reports/oei-06-09-00090.pdf
U.S. Department of Health and Human Services (DHHS), (2010b). Department of Health and Human Services Agency- wide plan . Retrieved from http://www.hhs.gov/sites/default/files/secretary/about/priorities/strategicplan2010-2015.pdf
Wachter, R. M. (2010a). Patient safety at ten: Unmistakable progress, troubling gaps. Health Affairs, 29 (1), 1-9.
Wachter, R. M. (2010b). Why diagnostic errors don’t get any respect – and what can be done about them. Health Affairs, 29(9), 1605-1610.
Walker, J.M., Carayon, P., Leveson, N., Paulus, R.A., Tooker, J., Chin, H., Boothe, A., & Stewart, W.F. (2008). EHR safety: The way forward to safe and effective systems. Journal of the Medical Informatics Association, 15, 272-277. doi: 10.1197/jamaia.M2618
Waneka, R. & Spetz, J. (2010). Hospital information technology systems’ impact on nurses and nursing care. Journal of Nursing Administration, 40(12), 509-514.
Wears, R.L. (2002). Advanced Statistics: Statistical methods for analyzing cluster and cluster-randomized data. Academic Emergency Medicine, 9, 330-341.
Zhan, C., & Miller, M. R. (2003). Administrative data based patient safety research: a critical review. Quality and Safety in Health Care, 12 [Supplement 2], ii58-63.
Amanda Hessels, PhD, MPH, RN, CIC, CPHQ is a Postdoctoral Research Fellow at the Center for Interdisciplinary Research to Prevent Infections (CIRI), Columbia University, School of Nursing and Nurse Scientist at Meridian Health in New Jersey. Her program of funded research examines the relationships among organizational factors, processes of nursing care and adverse patient outcomes in acute care settings. She has used both primary survey methodology and existing large-scale datasets to advance knowledge of these relationships.
Linda Flynn, PhD, RN, FAAN is a Professor and the Associate Dean of Academic Programs at the University of Colorado College Of Nursing. Her program of funded research, using large-scale survey methodology, focuses on the impact of system factors on nurse, patient, and faculty outcomes across a variety of settings. She is the author of multiple peer-reviewed publications and her research has influenced policy decisions at the state and national levels.
Jeannie P. Cimiotti, PhD, RN, FAAN is Associate Professor and the Dorothy M. Smith Endowed Chair at the University of Florida College Of Nursing. She is internationally known for the development and implementation of health care surveys and managing large health care datasets. Dr. Cimiotti’s research examines the organizational features of hospitals that lead to poor patient care outcomes.
Suzanne Bakken, RN, PhD, FAAN, FACMI is the Alumni Professor of Nursing and Professor of Biomedical Informatics at Columbia University. She directs the Center for Evidence-based Practice in the Underserved and the Reducing Health Disparities Through Informatics pre- and post-doctoral training program. She has been conducting federally-funded informatics research for more than 25 years. Dr. Bakken currently serves as the President of the American College of Medical Informatics
Robyn Gershon, MT (ASCP), MHS, DrPH is a Professor of Epidemiology and Biostatistics and Core Faculty in the Philip R. Lee Institute for Health Policy Studies in the School of Medicine at University of California, San Francisco. She is also an Adjunct Professor at the School of Nursing, UCSF, and Adjunct Professor at University of California, Berkeley. She is a nationally recognized researcher in the area of occupational and environmental health, specializing in public health disaster preparedness and response and in the health care workplace and workforce.