Upadhyay, S. & Opoku-Agyeman, W. (2023). Implementatin levels of electronic health records and their influence on quality and safety. Online Journal of Nursing Informatics (OJNI), 26(3), https://www.himss.org/resources/online-journal-nursing-informatics
Improvement in quality and patient safety outcomes have been at the forefront of the United States healthcare system. Policies advocate for implementing different electronic health records (EHR) levels to support this effort. Several studies have demonstrated mixed results on the impact of EHR on quality and safety outcomes. Our study delves deeper into gaining an understanding of this influence on hospitals that have adopted various levels of EHR, especially intermediate and comprehensive. Using a longitudinal study design for general acute care hospitals within the U.S., we examine the relationship between intermediate and comprehensive EHR on quality (readmission rates for pneumonia and COPD) and safety (adverse incident rate) using a random-effects model. Our national sample consisted of 7,084 hospital-year observations from 2014-2016. Hospitals with intermediate EHR had about an 8% decrease in 30-day readmission for Pneumonia compared to the hospitals with no intermediate EHR, while comprehensive EHR experienced a decrease of about 1% in adverse incident rate compared to hospitals that did not have comprehensive EHR. Our study buttresses prior findings that simply having EHR may not have an effect on outcomes, and a more targeted, meaningful use would need to be ensured.
Patient safety and quality have been at the forefront of the United States healthcare system since the advent of two landmark reports by the Institute of Medicine, which indicated that approximately 98,000 Americans die each year due to medical errors (Altman et al., 2004). Health information technology has been found to play a role in healthcare quality and safety. Electronic Health Records (EHR) are primarily meant to make billing procedures efficient (Hersh et al., 2013). However, EHR can improve quality by making coordination efficient and potentially reducing medical errors through better documentation and communication (Abraham et al., 2019). The American Recovery and Reinvestment Act, 2009 (ARRA), has provided federal incentives for EHR adoption for enhanced patient safety (Klein & Staal, 2017).
Meaningful use criteria, which encourages hospitals to use EHRs to improve the quality and efficiency of care, has transitioned to become one of the components of the new Merit-Based Incentive Payment System program, to enhance interoperability and improve the quality of care. Interoperability in EHR systems allows healthcare systems to work together cohesively so that healthcare can be effective and better patient outcomes can be achieved (Sreenivasan & Chacko, 2021).
However, the implementation of EHR has been at different levels. Variations in functionalities across the various levels of implementation of EHR impact quality and safety measures. According to a study, only 44% of hospitals had a basic EHR system in 2010 (DesRoches et al., 2010). Implementation has increased since then, but extant literature has categorized the level of implementation into two categories: basic and comprehensive. Some hospitals have an intermediate level of EHR implementation. Our study adds to the current literature by examining varying levels of EHR implementation (using three categories: basic, intermediate, and comprehensive) as predictors of quality and safety of care.
In response to the financial incentives through CMS and the HITECH Act, the implementation of EHR has rapidly increased, although not all hospitals have all advanced functionalities. Hospitals that are safety-net, small, rural, or resource disadvantaged in specific ways may be less likely to have advanced functionalities (Adler-Milstein et al., 2014; Adler‐Milstein et al., 2015). Understanding how advanced functionalities could benefit hospitals’ quality and safety goals. The potential benefits that come from the implementation of EHR functionalities may be a necessary step toward decision-making regarding investment in this technology (Dutta & Hwang, 2020; Lin et al., 2020). Adopting EHR is not an end-all solution in itself. Once EHR has been implemented, organizations need technical and personnel support to extract, manage, and analyze the data for quality improvement purposes. Advanced patient engagement functions require dedicated staff (Adler-Milstein et al., 2014) to educate patients and respond to their needs. Therefore, this study provides valuable practical insights for managers and policymakers in decision-making about implementing advanced EHR functionalities to influence quality and safety outcomes.
Based on the tenets of the Resource Based View (RBV), resources are inherently valuable assets, capabilities, attributes, and knowledge that can generate competitive advantage and ultimately influence performance (Barney, 1991). Information technology is a valuable and essential resource for hospitals. Health Information Technology in the form of EHRs provides benefits to patient care, better-coordinated care, improved quality and safety, and improved administrative processing and billing (Jamoom et al., 2016). EHR as a capability could provide the necessary competitive advantage to hospitals (Rivard et al., 2006).
EHR as a resource for competitive advantage
The resource-based view holds that a firm’s resources, like the technology used, are important determinants of its competitive advantage and performance (Alvarez et al., 2020; Barney, 1991). Resource Based View also contends that the magnitude, type, and nature of a firm’s resources and capabilities would play a role in determining performance (Yadav et al., 2017). Resources may also be heterogeneous (varying levels or quantity) across firms, according to a resource-based view (Alvarez et al., 2020). Hospitals’ varying levels of EHR implementation, with some hospitals having only basic EHR whereas others have comprehensive EHR, brings heterogeneity in information technology capabilities across hospitals. The heterogeneity of EHR functionalities depicts that hospitals in a particular group implement a different number and expanse of EHR functionalities compared to the other group. This particular heterogeneity supports a value-creating strategy for the groups with a higher number of and more advanced functionalities, thus providing them with a competitive advantage and possibly improving performance.
Uniqueness and differentiation
Federal policies have triggered the adoption of EHR, and hospitals nationwide have increasingly adopted at least basic EHR. A fundamental assertion of RBV is that if a resource possessed by a firm is also possessed by its competitors, then it would not provide a competitive advantage (Barney, 2018). Based on the aforementioned tenet, EHR adopted in its basic form would not contribute to a competitive advantage for hospitals. More advanced functionalities of EHR may need to be implemented to gain an advantage over or keep up with other hospitals. According to the healthcare strategy literature, Information Technology can contribute to changing competitive forces by lowering costs or creating differentiation (Lethbridge, 2011). Research shows that EHR systems could save up to $81 billion in healthcare costs annually (Atasoy et al., 2019; Hillestad et al., 2005). Differentiation can be enhanced by adopting the advanced EHR capabilities that are requirements for meaningful use and promise to improve quality and reduce costs, for instance, clinical decision support (CDS), health information exchanges, and computerized physician order entry (CPOE) systems.
Immobility and non-imitability
EHR as a resource may not be perfectly mobile across hospitals. Although hospitals can implement the same EHR functionalities, each may use this resource differently, which creates value that is non-imitable and non-mobile. Implementing EHR may not create value unless it is efficiently used to enhance quality performance. While some hospitals may use EHR for administrative, revenue cycle, and billing needs, others may use their system for research and quality improvement purposes (Lin et al., 2020). The resource-based view posits that firm resources may create value, and competitive advantage may be sustained if the benefits of a resource strategy cannot be copied or moved (Alvarez et al., 2020). EHR as a resource creates specific organizational abilities to deliver quality and safe patient care (Barrett, 2018; Moon et al., 2018).
According to RBV, resources and capabilities controlled by an organization underlie the performance difference across organizations (Kraaijenbrink et al., 2010; Upadhyay et al., 2020). Differences in performances across organizations vary with the level of resources and the difference in the efficiency of these resources (Kraaijenbrink et al., 2010). EHR is implemented at varying levels across organizations; thus, the different types of functionalities determine efficiency and its effect on performance. More efficient resources would allow the firm to produce better outputs (Barney, 1991). EHR with more advanced functionalities would be expected to produce better quality and safety outcomes more efficiently. The organization should have the capability (knowledge, skills, tools) to exploit the hidden potential of the resources (Barney, 1991) (see Figure 1).
Hospitals that are ‘technologically ready’ tend to use EHR more efficiently. Besides implementing advanced functionalities, hospitals should be ready to exploit higher levels of EHR by using proper knowledge, skills, and tools. Hospitals that implement and exploit higher levels of EHR are expected to produce better outcomes.
Based on the above rationale, our hypotheses are suggested below:
H1: Hospitals that have intermediate EHR have better quality and safety outcomes as compared to hospitals that do not have intermediate EHR.
H2: Hospitals that have comprehensive EHR have better quality and safety outcomes as compared to hospitals that do not have comprehensive EHR.
Data sources and Sample
Our study utilized data from four secondary data sources, including the American Hospital Association (AHA) Annual Survey (American Hospital Association, 2022), AHA annual Information Technology (IT) survey (American Hospital Association, 2023), the Area Health Resource File (AHRF) (HRSA Data Warehouse, 2023), and the Centers for Medicare and Medicaid Services (CMS) Hospital Compare. These datasets were linked using a hospital identification number of Healthcare Cost Report Information System (HCRIS) and Federal Information Processing Standard Codes (FIPS codes). Our national sample consisted of non-federal general acute care hospitals utilizing an unbalanced panel design from 2014 to 2016 with 7,084 hospital-year observations (or an average of 2,600 hospitals per year).
Dependent variable: Quality outcomes were measured as 30-day readmission rates for Pneumonia (PN), Chronic Obstructive Pulmonary Disease (COPD), and safety outcomes were measured by Adverse Incident Rate (AIR). Adverse incident rate (AIR) measures the rate of adverse incidents, such as an injury due to improper medical management that prolonged hospitalization or produces disability at discharge (Panagioti et al., 2019). To measure AIR, we used the Patient Safety Indicators Composite, a composite or weighted average of a subset of 'Patient Safety and Adverse Events at a hospital-level (Upadhyay et al., 2020). Higher AIR is an indication of worse patient safety outcomes.
Independent Variables: Levels of EHR Implementation. As an extension of the previous definitions by researchers (Adler‐Milstein et al., 2015), we created additional categories to fill the gap between basic and comprehensive categories. In our study, we include the following categories and their definitions: 1)
1) Less than basic EHR- A hospital is termed as having less than basic EHR if it has less than ten of the basic functionalities identified by Adler-Milstein et al. (2014) and Jha et al. (2009).
2) Basic EHR- A hospital is termed as having basic EHR if ten specific functionalities have been implemented in at least one clinical unit. This means that hospitals that have a partial implementation of those ten functionalities would qualify as having basic EHR.
3) Intermediate EHR- A hospital has intermediate EHR if more than ten functionalities are implemented in at least one clinical unit.
4) Intermediate-Basic EHR- A hospital has intermediate-basic EHR if more than ten specific functionalities are at least partially implemented.
5) Comprehensive EHR- A hospital is termed as having comprehensive EHR if all twenty-four functionalities are fully implemented across all units. All hospitals with comprehensive EHR would have the ten specific functionalities required under the basic EHR category.
Based on the above definitions, we created a set of two independent variables to examine their association with hospital-level quality and safety outcomes. The first independent variable compared hospitals with comprehensive EHR against hospitals that do not have comprehensive EHR. The second compared hospitals with intermediate EHR against hospitals that do not have intermediate EHR.
Control Variables: We included several organizational and market-level control variables that could vary over time and confound the hospital's quality and safety outcomes. These included:Hospital size (measured as the total number of beds), Payer mix (measured as the share of total inpatient discharge by payer), system membership (measured as ‘yes’ if system affiliated, or ‘no’ if not affiliated with a system), hospital ownership (measured as public, not-for-profit, and for-profit), teaching status (measured with a dummy variable; 0= not a teaching hospital; 1= teaching hospital).
Hospitals were classified as teaching hospitals if they met any of the following criteria: 1) have residency training approved by the Accreditation Council for Graduate Medical Education; 2) medical school affiliation reported to the American Medical Association, 3) member of the Council of Teaching Hospitals of the Association of American Medical Colleges (COTH), or residency approved by the American Osteopathic Association.
Additionally, we included market-level variables such as market competition (measured by the Hirschman-Herfindahl Index (HHI). The HHI represents the sum of the squared market shares in a market, with market share based on the system-level share of hospital inpatient days in a Health Service Area. Per capita income (measured as the total personal income of the residents in a given area divided by the resident population in HSA), percent of the population 65 years or older (measured as a percentage of the total resident population aged 65 years or older within a county), percent of the population without health insurance (measured as the total percentage of resident population without health insurance in a county), Medicare managed care penetration rate (measured as the ratio of Medicare Advantage plans enrollees over eligible Medicare individuals multiplied by 100), and hospital location (measured as urban, metropolitan, and rural-based on the Rural-Urban Continuum Code (RUCC) for the county where the hospital is located).
The unit of analysis was the hospital. Univariate analyses provided descriptive statistics on the variables used. Multivariable relationships between quality outcomes and hospitals with comprehensive and intermediate EHR were examined using panel regression with random effects, with hospitals without comprehensive and intermediate EHR as the reference groups, respectively. Robust standard errors were included to address the correlation of repeated observations. Confounding variables were lagged by one year, given the effects of these variables on levels of EHR Implementation. All data were analyzed in Stata 16.
Overall, the sample size available for analysis in this study was 7,084 hospital-year observations from 2014 to 2016. From baseline to endline, changes in the proportion of each category of EHR implementation are presented in Table 1. The proportions of hospitals with comprehensive EHR increased significantly from 811 (35.6%) to 1,319 (53.4%), and intermediate decreased from 1,328 (62%) to 1,073 (44.9%) from 2014 to 2016. The mean baseline adverse incident rate was 0.86 (SD=0.20), which increased slightly to 0.89 (SD=0.18) in 2016. On the contrary, 30-day readmission for COPD and Pneumonia decreased from 2014 to 2016. Specifically, 30-day readmission for COPD decreased from 20.35 (SD=2.14) in 2014 to 19.54 (SD=2.08) in 2016. Similarly, 30-day readmission for Pneumonia decreased from 17.21(SD=2.11) in 2014 to 16.93(SD=2.25) in 2016. These decreases were not statistically significant.
On average, hospitals had a bed size of about 189 in 2014 and 186 in 2016. Medicare and Medicaid payer mix stayed fairly the same. Specifically, Medicare payer mix slightly increased from an average of 52.06 (SD=22.23) in 2014 to an average of 52.74 (SD=18.71) in 2016. On the contrary, Medicaid payer mix, on average, slightly decreased from 19.50 (SD=15.12) in 2014 to 19.37(SD=14.44) in 2016. About 63% of hospitals belonged to a hospital system in 2014 and 65% in 2016. The proportion of investor-owned for-profit hospitals decreased from approximately 22% in 2014 to 21% in 2016. However, the percentage of not-for-profit hospitals increased from 64% in 2014 to 66% in 2016. The proportions of hospitals with teaching status stayed fairly the same at 41% in both 2014 and 2016, indicating less than 50% of the hospitals as teaching in our sample. Among market-level characteristics in which hospitals operate, market competition (HHI) remained unchanged, 0.78 (SD=0.31) in 2014 and 2016, respectively. The average per capita income increased from approximately $43,000 (SD=11,753.94) in 2014 to $44,000 (SD=12,757.05) in 2016. The population of people older than 65 years for hospitals in these counties saw, on average, a decrease in population from approximately 60,135.66(SD=110,176.4) in 2014 to 58,782.38(SD=108,508.6) in 2016. The average number of people without health insurance declined from 13.29(SD=5.22) in 2014 to 11.02(SD=5.02) in 2016. Medicare managed care penetration saw a slight increase from 26.21(SD=14.64) in 2014 to 27.52(14.95) in 2016. The majority of the hospitals in our sample, both in 2014 and 2016, were in metropolitan areas (59% and 68%, respectively), followed by urban areas (35%).
Table 2 presents the findings from the regression analyses examining the relationship between hospital-level quality and safety outcomes and intermediate EHR implementation (model 1) and the relationship between hospital-level quality outcomes and comprehensive EHR implementation (model 2). Results indicate significant associations between intermediate EHR implementation and quality and safety outcomes measured by 30-day readmission for COPD, 30-day readmission for Pneumonia, and adverse incident rate. Specifically, compared with hospitals without intermediate EHR implemented, hospitals with intermediate EHR, on average, experience an increase of about 8% (p=0.05) in 30-day readmission for COPD and 1% (p=0.02) in adverse incident rate. However, hospitals with intermediate EHR had, on average, about an 8% (p=0.05) decrease in 30-day readmission for Pneumonia compared to the hospitals with no intermediate EHR. Hospitals with comprehensive EHR, on average, experience a decrease of about 1% (p=0.01) in adverse incident rate compared to hospitals that did not have comprehensive EHR.
Some organizational and market factors were also significantly associated with quality outcomes in both models. In model 1, hospital size indicated a significant association with outcomes at the hospital level. Specifically, on average, as hospital size increases, there is a corresponding increase of about 0.2% in 30-day readmission for COPD and Pneumonia and adverse incident rate (p<0.01). Government nonfederal hospitals experienced, on average, an increase of about 43% in 30-day readmission for Pneumonia compared with not-for-profit hospitals. However, the significant association between investor-owned for-profit hospitals with quality outcome were mixed. Specifically, for-profit hospitals experienced a decrease of about 25% in 30-day readmission for COPD but an increase of approximately 3% in adverse incident rates (p=0.04 and p<0.01, respectively). Teaching hospitals experienced an increase of about 27% and 24% in 30-day readmission for COPD and Pneumonia, respectively, and 2% in adverse incident rate (p<0.01).
Additionally, as the number of patients without health insurance increases in a county, hospitals serving in these counties experienced a decrease of about 3% in 30-day readmission for COPD and 0.2% in adverse incident rate (p<0.01). Similarly, as market competition increases, hospitals' adverse incident rates decrease by approximately 6%. Hypothesis 1 was partially satisfied. For H1, we found that hospitals with intermediate EHR were related to lower readmission rates for pneumonia than those without intermediate EHR. For H2, we found that hospitals with comprehensive EHR were related to lower adverse incident rates than hospitals without comprehensive EHR.
In model 2, as the Medicare payer mix increases, the adverse incident rate decreases by 0.1% (p<0.01). For-profit, on average, has a 3% higher adverse incident rate (p<0.01) than not-for-profit hospitals. Teaching hospitals have approximately 3% higher adverse incident rates (p<0.01) than hospitals with a non-teaching status. As market competition increased, the adverse incident rate decreased by approximately 6% (p<0.01). As the number of patients without health insurance increases in a county, the adverse incident rate decreases by 0.2% (p<0.01).
Our objective was to examine if various levels of EHR implementation impact the quality and safety of care. Furthermore, if they do, what are those effects? Based on the tenets of RBV, EHR adopted only in a basic form would not be competitively advantageous for a hospital because the hospital’s competitors also possess this resource. Our study suggests that the effects of EHR implementation levels on quality and safety are mixed. Hospitals that were intermediate and had more EHR functionalities that were more widely implemented than hospitals without intermediate EHR showed positive effects on reducing readmission rates for Pneumonia. A plausible reason could be that a higher mortality rate could be accounting for a decreased readmission rate. Hospitals with fully implemented comprehensive EHR with all EHR functionalities showed some effects on reducing potentially preventable adverse events, even if those effects were less than 5%. Our study corroborated findings from a prior study showing that comprehensive EHR led to lower readmissions and reduced mortality (Lin et al., 2020).
EHR functionalities can potentially improve the quality and safety of care because EHR improves the accessibility of patients’ health information, timely documentation, better discharge planning for patients, tracking patients’ information over time, and providing decision support mechanisms to reduce medical errors. Despite the potential advantages, prior studies have found mixed findings of EHR’s effect on the quality of care. Our study’s findings are consistent with those of prior studies that have found that simply having EHR may not affect the quality of care (Holroyd-Leduc et al., 2011). Even if hospitals acquire higher than basic functionalities, how those functionalities are used is more important than just implementing them. The usage of EHR by providers for decision support and communication is critical to improving the quality of care. Besides usage, studies have found that the duration of use of EHR may affect outcomes with long-term users having mature EHRs and better-designed clinical decision support processes. They also might be more willing to use more advanced functionalities. Users with shorter and limited experience may only have subpar EHR functionalities and may be less willing to use them to influence outcomes (Enaizan et al., 2020).
Per RBV, performance differences across organizations vary based on the level of resources and efficiency of those resources possessed by the organization. Intermediate and comprehensive EHRs are expected to have advanced functionalities with more sophisticated technology capabilities to identify patients at risk for readmissions. Prior studies have shown that high levels of electronic documentation, an aspect of EHR use for intermediate hospitals, were associated with modest reductions in readmission for pneumonia (Rudin et al., 2014). Consistent with the prior studies, even in our study, identifying patients at high risk for readmissions through EHR use perhaps led to a modest decrease in Pneumonia readmission rate in the case of hospitals with intermediate EHR.
The advanced functionalities provided by intermediate and comprehensive EHR, such as computerized provider order entry and decision support, are expected to provide numerous benefits, including decreased adverse incident rates and improved patient safety outcomes. We found mixed evidence of this. While comprehensive EHR hospitals showed a reduction in adverse incident rates by 1%, intermediate EHR hospitals did not. From 2014 to 2016, the implementation of comprehensive EHR increased by approximately 18%, and there was also a decline in the mean adverse incident rate by 0.03. This points towards more targeted uses of EHR by hospitals with comprehensive EHR. Hospitals with computerized decision support may identify near misses and potential adverse incidents more accurately with drug interaction alerts, drug allergy alerts, and clinical reminders. Provider entry of lab tests, radiology tests, and nursing orders enables smooth handoffs and transitions of patients. The above features would also assist with a greater frequency of error reporting by staff.
Some limitations of this study are worth noting. Due to many hospital year observations, with hospitals having disparate attributes, there was a wide variation in their characteristics. Also, secondary datasets that are self-reported by hospitals were used. The above two factors limit our findings' generalizability because secondary datasets' validity and reliability may need to be documented. Studies in the future should use qualitative techniques or a mixed-method approach to assess how the various levels of EHR implementation may impact quality and safety outcomes. More years in the study, a variety of quality indicators, and the inclusion of mortality rates as control variables will help improve future studies. Mixed findings between EHR levels and quality and safety indicators show that having more than basic EHR might be an advantage for hospitals. However, gains from having comprehensive EHR capabilities need to be explored further.
Our study provides valuable practical insights for healthcare practitioners and policymakers. Our study is another one in the current line of research that has found that EHR implementation or use was not associated with improving condition-specific readmission rates (Yuan et al., 2019). More targeted use of EHR may be necessary to bring about substantial change (DesRoches et al., 2010). Hospital administrators must aim at healthcare professionals to ensure that EHR is used meaningfully. While hospitals may take the responsibility of implementing EHR with the best functionalities in many units, physicians and nurses need to step up to use EHR in a way that affects outcomes. Testing EHR in real-time in the working environment and proper training from the vendors is essential to get the best out of EHR. Hospitals can be selective in the implementation of specific comprehensive capabilities of EHR. Implementing the functionalities that seem to fit and are worthwhile may be a wise investment of time and effort. For policymakers, it might be valuable to revisit the meaningful use criteria so that hospitals can perform efficiently regarding improving quality and safety while following this regulation.
In conclusion, EHR at its intermediate and comprehensive levels encompasses information regarding patient care, provides decision support, and improves accessibility and timely documentation. In the years to come, the benefits of installing advanced functionalities would go beyond economics to include tools for enhanced quality, patient safety, and greater clinical efficiencies.
Powered by the HIMSS Foundation and the HIMSS Nursing Informatics Community, the Online Journal of Nursing Informatics is a free, international, peer reviewed publication that is published three times a year and supports all functional areas of nursing informatics.
Abraham, J., Jaros, J., Ihianle, I., Kochendorfer, K., & Kannampallil, T. (2019). Impact of EHR-based rounding tools on interactive communication: A prospective observational study. International Journal of Medical Informatics, 129, 423–429. https://doi.org/10.1016/j.ijmedinf.2019.07.012
Adler-Milstein, J., DesRoches, C. M., Furukawa, M. F., Worzala, C., Charles, D., Kralovec, P., Stalley, S., & Jha, A. K. (2014). More than half of US hospitals have at least a Basic EHR, but Stage 2 criteria remain challenging for most. Health Affairs, 33(9), 1664–1671. https://doi.org/10.1377/hlthaff.2014.0453
Adler-Milstein, J., Everson, J., & Lee, S.-Y. D. (2015). EHR adoption and hospital performance: Time-related effects. Health Services Research, 50(6), 1751–1771. Portico. https://doi.org/10.1111/1475-6773.12406
Altman, D. E., Clancy, C., & Blendon, R. J. (2004). Improving patient safety — Five years after the IOM Report. New England Journal of Medicine, 351(20), 2041–2043. https://doi.org/10.1056/nejmp048243
Alvarez, S. A., Zander, U., Barney, J. B., & Afuah, A. (2020). Developing a theory of the firm for the 21st Century. Academy of Management Review, 45(4), 711–716. https://doi.org/10.5465/amr.2020.0372
American Hospital Association (2023). AHA Annual Survey Information Technology Database. https://www.ahadata.com/aha-healthcare-it-database
American Hospital Association (2022). American Hospital Association (AHA) Annual Survey Database. https://www.ahadata.com/aha-annual-survey-database
Atasoy, H., Greenwood, B. N., & McCullough, J. S. (2019). The digitization of patient care: A review of the effects of Electronic Health Records on health care quality and utilization. Annual Review of Public Health, 40(1), 487–500. https://doi.org/10.1146/annurev-publhealth-040218-044206
Barney, J. B. (2018). Why resource‐based theory’s model of profit appropriation must incorporate a stakeholder perspective. Strategic Management Journal, 39(13), 3305–3325.
Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99–120. https://doi.org/10.1177/014920639101700108
Barrett, A. K. (2017). Electronic Health Record (EHR) organizational change: Explaining resistance through profession, organizational experience, and EHR communication quality. Health Communication, 33(4), 496–506. https://doi.org/10.1080/10410236.2016.1278506
Centers for Medicare and Medicaid Services (CMS). (2023). Hospital Compare. https://hospitalcompare.io/
DesRoches, C. M., Campbell, E. G., Vogeli, C., Zheng, J., Rao, S. R., Shields, A. E., Donelan, K., Rosenbaum, S., Bristol, S. J., & Jha, A. K. (2010). Electronic Health Records’ limited successes suggest more targeted uses. Health Affairs, 29(4), 639–646. https://doi.org/10.1377/hlthaff.2009.1086
Dutta, B., & Hwang, H.-G. (2020). The adoption of electronic medical record by physicians. Medicine, 99(8), e19290. https://doi.org/10.1097/md.0000000000019290
Enaizan, O., Eneizan, B., Almaaitah, M., Al-Radaideh, A. T., & Saleh, A. M. (2020). Effects of privacy and security on the acceptance and usage of EMR: The mediating role of trust on the basis of multiple perspectives. Informatics in Medicine Unlocked, 21, 100450. https://doi.org/10.1016/j.imu.2020.100450
Hersh, W. R., Weiner, M. G., Embi, P. J., Logan, J. R., Payne, P. R. O., Bernstam, E. V., Lehmann, H. P., Hripcsak, G., Hartzog, T. H., Cimino, J. J., & Saltz, J. H. (2013). Caveats for the use of operational Electronic Health Record data in comparative effectiveness research. Medical Care, 51(Supplement 8Suppl 3), S30–S37. https://doi.org/10.1097/mlr.0b013e31829b1dbd
Hillestad, R., Bigelow, J., Bower, A., Girosi, F., Meili, R., Scoville, R., & Taylor, R. (2005). Can Electronic Medical Record systems transform health care? Potential health benefits, savings, and costs. Health Affairs, 24(5), 1103–1117. https://doi.org/10.1377/hlthaff.24.5.1103
Holroyd-Leduc, J. M., Lorenzetti, D., Straus, S. E., Sykes, L., & Quan, H. (2011). The impact of the electronic medical record on structure, process, and outcomes within primary care: a systematic review of the evidence: Figure 1. Journal of the American Medical Informatics Association, 18(6), 732–737. https://doi.org/10.1136/amiajnl-2010-000019
HRSA Data Warehouse. (2023). Area Health Resources Files. https://data.hrsa.gov/topics/health-workforce/ahrf
Jamoom, E., Heisey-Grove, D., Yang, N., & Scanlon, P. (2016). Physician opinions about EHR use by EHR experience and by whether the practice had optimized its EHR Use. Journal of Health & Medical Informatics, 7(4). https://doi.org/10.4172/2157-7420.1000240
Jha, A. K., Orav, E. J., & Epstein, A. M. (2009). Public reporting of discharge planning and rates of readmissions. New England Journal of Medicine, 361(27), 2637–2645. https://doi.org/10.1056/nejmsa0904859
Klein, B., & Staal, K. (2017). Was the American recovery and reinvestment act an economic stimulus? International Advances in Economic Research, 23(4), 395–404.
Kraaijenbrink, J., Spender, J.-C., & Groen, A. J. (2009). The Resource-Based View: A review and assessment of its critiques. Journal of Management, 36(1), 349–372. https://doi.org/10.1177/0149206309350775
Lethbridge, J. (2011). Understanding multinational companies in public health systems, using a competitive advantage framework. Globalization and Health, 7(1), 19. https://doi.org/10.1186/1744-8603-7-19
Lin, H.L., Wu, D.C., Cheng, S.M., Chen, C.J., Wang, M.C., & Cheng, C.A. (2020). Association between Electronic Medical Records and healthcare quality. Medicine, 99(31), e21182. https://doi.org/10.1097/md.0000000000021182
Moon, M. C., Hills, R., & Demiris, G. (2018). Understanding optimisation processes of electronic health records (EHRs) in select leading hospitals: a qualitative study. BMJ Health Care Informatics, 25(2), 109–125. https://doi.org/10.14236/jhi.v25i2.1011
Panagioti, M., Khan, K., Keers, R. N., Abuzour, A., Phipps, D., Kontopantelis, E., Bower, P., Campbell, S., Haneef, R., Avery, A. J., & Ashcroft, D. M. (2019). Prevalence, severity, and nature of preventable patient harm across medical care settings: systematic review and meta-analysis. BMJ, 366, l4185
Rivard, S., Raymond, L., & Verreault, D. (2006). Resource-based view and competitive strategy: An integrated model of the contribution of information technology to firm performance. The Journal of Strategic Information Systems, 15(1), 29–50. https://doi.org/10.1016/j.jsis.2005.06.003
Rudin, R. S., Motala, A., Goldzweig, C. L., & Shekelle, P. G. (2014). Usage and Effect of Health Information Exchange. Annals of Internal Medicine, 161(11), 803. https://doi.org/10.7326/m14-0877
Sreenivasan M. & Chacko, A. (2021). Interoperability issues in EHR systems: Research directions. In K. Chang Lee, S. Roy, P. Samui, V. Kumar, (Eds). Data Analytics in Biomedical Engineering and Healthcare, Academic Press, pp. 13-28. https://doi.org/10.1016/B978-0-12-819314-3.00002-1.
Upadhyay, S., Weech-Maldonado, R., Lemak, C. H., Stephenson, A., Mehta, T., & Smith, D. G. (2018). Resource-based view on safety culture’s influence on hospital performance: The moderating role of electronic health record implementation. Health Care Management Review, 45(3), 207–216. https://doi.org/10.1097/hmr.0000000000000217
Yadav, P. L., Han, S. H., & Kim, H. (2017). Sustaining Competitive Advantage Through Corporate Environmental Performance. Business Strategy and the Environment, 26(3), 345–357. Portico. https://doi.org/10.1002/bse.1921
Yuan, N., Dudley, R. A., Boscardin, W. J., & Lin, G. A. (2019). Electronic health records systems and hospital clinical performance: a study of nationwide hospital data. Journal of the American Medical Informatics Association, 26(10), 999–1009. https://doi.org/10.1093/jamia/ocz092
Dr. Soumya Upadhyay is an Associate Professor at the Department of Healthcare Administration and Policy, School of Public Health at the University of Nevada Las Vegas. Dr. Upadhyay’s research examines (1) how patient safety and quality of healthcare can be improved using health IT, and (2) how do patient safety culture and quality outcomes impact hospital performance. Dr. Upadhyay’s papers have been featured in top healthcare management/administration journals such as the Healthcare Management Review, Journal of Patient Safety, Journal of Healthcare Management, Journal of Health Administration and Education, and INQUIRY. In 2018, she received the Faculty Opportunity Award from UNLV’s Vice President of Research, and in 2019 her research was recognized by the UNLV School of Public Health. In 2021, she received a grant award from the Troesh Center for Entrepreneurship and Innovation at UNLV. In 2022, she received Runners Up Award for Best ‘Theory to Practice’ paper at the Academy of Management’s Healthcare Management division. Prior to joining UNLV, Dr. Upadhyay managed data analysis at Sutter Health, California. Her roles as a strategic management consultant at Kaiser Permanente and as a performance improvement specialist at MD Anderson Cancer Center gave her new insights into the broad scope of the healthcare management field.
William Opoku-Agyeman, Ph.D., is an Assistant Professor of Healthcare Management at the University of North Carolina at Wilmington. Dr. Agyeman is a health services researcher whose research interest focuses on healthcare organizations’ strategic population health management (health promotion activities and community need assessment), specifically FQHCs and Hospitals. He utilizes a wide range of statistical methodologies with large national databases. He holds a PhD. in Health Services Administration from the University of Alabama at Birmingham, a Master of Science Degree in Geographic Information Science from the University of Akron, Ohio, and a master’s in public health from Kent State University in Ohio.