Practice and market factors associated with provider volume of health information exchange

ObjectiveTo assess the practice- and market-level factors associated with the amount of provider health information exchange (HIE) use.Materials and MethodsProvider and practice-level data was drawn from the Meaningful Use Stage 2 Public Use Files from the Centers for Medicare and Medicaid Services, the Physician Compare National Downloadable File, and the Compendium of US Health Systems, among other sources. We analyzed the relationship between provider HIE use and practice and market factors using multivariable linear regression and compared primary care providers (PCPs) to non-PCPs. Provider volume of HIE use is measured as the percentage of referrals sent with electronic summaries of care (eSCR) reported by eligible providers attesting to the Meaningful Use electronic health record (EHR) incentive program in 2016.ResultsProviders used HIE in 49% of referrals; PCPs used HIE in fewer referrals (43%) than non-PCPs (57%). Provider use of products from EHR vendors was negatively related to HIE use, while use of Athenahealth and Greenway Health products were positively related to HIE use. Providers treating, on average, older patients and greater proportions of patients with diabetes used HIE for more referrals. Health system membership, market concentration, and state HIE consent policy were unrelated to provider HIE use.DiscussionHIE use during referrals is low among office-based providers with the capability for exchange, especially PCPs. Practice-level factors were more commonly associated with greater levels of HIE use than market-level factors.ConclusionThis furthers the understanding that market forces, like competition, may be related to HIE adoption decisions but are less important for use once adoption has occurred.     

Assessing hospital electronic health record vendor performance across publicly reported quality measures

ObjectiveLittle is known regarding variation among electronic health record (EHR) vendors in quality performance. This issue is compounded by selection effects in which high-quality hospitals coalesce to a subset of market leading vendors. We measured hospital performance, stratified by EHR vendor, across 4 quality metrics.Materials and MethodsWe used data on 1272 hospitals in 2018 across 4 quality measures: Leapfrog Computerized Provider Order Entry/EHR Evaluation, Centers for Medicare and Medicaid Services Hospital Compare Star Ratings, Hospital-Acquired Condition (HAC) score, and Hospital Readmission Reduction Program (HRRP) ratio. We examined score distributions and used multivariable regression to evaluate the association between vendor and score, recovering partial R² to assess the proportion of quality variation explained by vendor.ResultsWe found significant variation across and within EHR vendors. The largest vendor, vendor A, had the highest mean score on the Leapfrog Computerized Provider Order Entry/EHR Evaluation and HRRP ratio, vendor G had the highest Hospital Compare score, and vendor F had the highest HAC score. In adjusted models, no vendor was significantly associated with higher performance on more than 2 measures. EHR vendor explained between 1.2% (HAC) and 7.6 (HRRP) of the variation in quality performance.DiscussionNo EHR vendor was associated with higher quality across all measures, and the 2 largest vendors were not associated with the highest scores. Only a small fraction of quality variation was explained by EHR vendor choice.ConclusionsTop performance on quality measures can be achieved with any EHR vendor; much of quality performance is driven by the hospital and how it uses the EHR.     

The SMART Platform: early experience enabling substitutable applications for electronic health records

Objective

The Substitutable Medical Applications, Reusable Technologies (SMART) Platforms project seeks to develop a health information technology platform with substitutable applications (apps) constructed around core services. The authors believe this is a promising approach to driving down healthcare costs, supporting standards evolution, accommodating differences in care workflow, fostering competition in the market, and accelerating innovation.

Materials and methods

The Office of the National Coordinator for Health Information Technology, through the Strategic Health IT Advanced Research Projects (SHARP) Program, funds the project. The SMART team has focused on enabling the property of substitutability through an app programming interface leveraging web standards, presenting predictable data payloads, and abstracting away many details of enterprise health information technology systems. Containers—health information technology systems, such as electronic health records (EHR), personally controlled health records, and health information exchanges that use the SMART app programming interface or a portion of it—marshal data sources and present data simply, reliably, and consistently to apps.

Results

The SMART team has completed the first phase of the project (a) defining an app programming interface, (b) developing containers, and (c) producing a set of charter apps that showcase the system capabilities. A focal point of this phase was the SMART Apps Challenge, publicized by the White House, using http://www.challenge.gov website, and generating 15 app submissions with diverse functionality.

Conclusion

Key strategic decisions must be made about the most effective market for further disseminating SMART: existing market-leading EHR vendors, new entrants into the EHR market, or other stakeholders such as health information exchanges.     

Factors Related to Health Information Exchange Participation and Use

This study examines factors facilitating and delaying participation and use of the Health Information Exchange (HIE) in Louisiana. Semi-structured qualitative interviews were conducted with health care representatives throughout the state. Findings suggest that Meaningful Use requirements are a critical factor influencing the decision to participate in the HIE, specifically the mandate that hospitals be able to electronically transfer summary of care documents. Creating buy-in within a few large hospital networks legitimized the HIE and hastened interest in those markets. Fees charged by electronic health record (EHR) vendors to develop HIE interfaces have been prohibitive. Funding from the federal incentive program is intended to offset the costs associated with EHR implementation and increase the likelihood that HIEs can provide value to the population; however, costs and time delays of EHR interface development may be key barriers to fully integrated HIEs. State HIEs may benefit from targeted involvement of state health care leaders who can champion the potential value of the HIE.     

Exploring the relationship between electronic health records and provider burnout: A systematic review

ObjectiveStress and burnout due to electronic health record (EHR) technology has become a focus for burnout intervention. The aim of this study is to systematically review the relationship between EHR use and provider burnout.Materials and MethodsA systematic literature search was performed on PubMed, EMBASE, PsychInfo, ACM Digital Library in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement. Inclusion criterion was original research investigating the association between EHR and provider burnout. Studies that did not measure the association objectively were excluded. Study quality was assessed using the Medical Education Research Study Quality Instrument. Qualitative synthesis was also performed.ResultsTwenty-six studies met inclusion criteria. The median sample size of providers was 810 (total 20 885; 44% male; mean age 53 [range, 34-56] years). Twenty-three (88%) studies were cross-sectional studies and 3 were single-arm cohort studies measuring pre- and postintervention burnout prevalence. Burnout was assessed objectively with various validated instruments. Insufficient time for documentation (odds ratio [OR], 1.40-5.83), high inbox or patient call message volumes (OR, 2.06-6.17), and negative perceptions of EHR by providers (OR, 2.17-2.44) were the 3 most cited EHR-related factors associated with higher rates of provider burnout that was assessed objectively.ConclusionsThe included studies were mostly observational studies; thus, we were not able to determine a causal relationship. Currently, there are few studies that objectively assessed the relationship between EHR use and provider burnout. The 3 most cited EHR factors associated with burnout were confirmed and should be the focus of efforts to improve EHR-related provider burnout.     

Measuring time clinicians spend using EHRs in the inpatient setting: a national,mixed-methods study

ObjectiveTo understand hospitals’ use of EHR audit-log-based measures to address burden associated with inpatient EHR use.Materials and MethodsUsing mixed methods, we analyzed 2018 American Hospital Association Information Technology Supplement Survey data (n = 2864 hospitals; 64% response rate) to characterize measures used and provided by EHR vendors to track clinician time spent documenting. We interviewed staff from the top 3 EHR vendors that provided these measures. Multivariable analyses identified variation in use of the measures among hospitals with these 3 vendors.Results53% of hospitals reported using EHR data to track clinician time documenting, compared to 68% of the hospitals using the EHR from the top 3 vendors. Among hospitals with EHRs from these vendors, usage was significantly lower among rural hospitals and independent hospitals (P < .05). Two of these vendors provided measures of time spent doing specific tasks while the third measured an aggregate of auditable activities. Vendors varied in the underlying data used to create measures, measure specification, and data displays.DiscussionTools to track clinicians’ documentation time are becoming more available. The measures provided differ across vendors and disparities in use exist across hospitals. Increasing the specificity of standards underlying the data would support a common set of core measures making these measures more widely available.ConclusionAlthough half of US hospitals use measures of time spent in the EHR derived from EHR generated data, work remains to make such measures and analyses more broadly available to all hospitals and to increase its utility for national burden measurement.     

Electronic health records and clinician burnout: A story of three eras

ObjectiveThe study sought to provide physicians, informaticians, and institutional policymakers with an introductory tutorial about the history of medical documentation, sources of clinician burnout, and opportunities to improve electronic health records (EHRs). We now have unprecedented opportunities in health care, with the promise of new cures, improved equity, greater sensitivity to social and behavioral determinants of health, and data-driven precision medicine all on the horizon. EHRs have succeeded in making many aspects of care safer and more reliable. Unfortunately, current limitations in EHR usability and problems with clinician burnout distract from these successes. A complex interplay of technology, policy, and healthcare delivery has contributed to our current frustrations with EHRs. Fortunately, there are opportunities to improve the EHR and health system. A stronger emphasis on improving the clinician’s experience through close collaboration by informaticians, clinicians, and vendors can combine with specific policy changes to address the causes of burnout.Target audienceThis tutorial is intended for clinicians, informaticians, policymakers, and regulators, who are essential participants in discussions focused on improving clinician burnout. Learners in biomedicine, regardless of clinical discipline, also may benefit from this primer and review.ScopeWe include (1) an overview of medical documentation from a historical perspective; (2) a summary of the forces converging over the past 20 years to develop and disseminate the modern EHR; and (3) future opportunities to improve EHR structure, function, user base, and time required to collect and extract information.     

Clinical data sharing improves quality measurement and patient safety

ObjectiveAccurate and robust quality measurement is critical to the future of value-based care. Having incomplete information when calculating quality measures can cause inaccuracies in reported patient outcomes. This research examines how quality calculations vary when using data from an individual electronic health record (EHR) and longitudinal data from a health information exchange (HIE) operating as a multisource registry for quality measurement. Materials and MethodsData were sampled from 53 healthcare organizations in 2018. Organizations represented both ambulatory care practices and health systems participating in the state of Kansas HIE. Fourteen ambulatory quality measures for 5300 patients were calculated using the data from an individual EHR source and contrasted to calculations when HIE data were added to locally recorded data.ResultsA total of 79% of patients received care at more than 1 facility during the 2018 calendar year. A total of 12 994 applicable quality measure calculations were compared using data from the originating organization vs longitudinal data from the HIE. A total of 15% of all quality measure calculations changed (P < .001) when including HIE data sources, affecting 19% of patients. Changes in quality measure calculations were observed across measures and organizations.DiscussionThese results demonstrate that quality measures calculated using single-site EHR data may be limited by incomplete information. Effective data sharing significantly changes quality calculations, which affect healthcare payments, patient safety, and care quality.ConclusionsFederal, state, and commercial programs that use quality measurement as part of reimbursement could promote more accurate and representative quality measurement through methods that increase clinical data sharing.     

Human-centered development of an electronic health record-embedded,interactive information visualization in the emergency department using fast healthcare interoperability resources

ObjectiveDevelop and evaluate an interactive information visualization embedded within the electronic health record (EHR) by following human-centered design (HCD) processes and leveraging modern health information exchange standards.Materials and MethodsWe applied an HCD process to develop a Fast Healthcare Interoperability Resources (FHIR) application that displays a patient’s asthma history to clinicians in a pediatric emergency department. We performed a preimplementation comparative system evaluation to measure time on task, number of screens, information retrieval accuracy, cognitive load, user satisfaction, and perceived utility and usefulness. Application usage and system functionality were assessed using application logs and a postimplementation survey of end users.ResultsUsability testing of the Asthma Timeline Application demonstrated a statistically significant reduction in time on task (P < .001), number of screens (P < .001), and cognitive load (P < .001) for clinicians when compared to base EHR functionality. Postimplementation evaluation demonstrated reliable functionality and high user satisfaction.DiscussionFollowing HCD processes to develop an application in the context of clinical operations/quality improvement is feasible. Our work also highlights the potential benefits and challenges associated with using internationally recognized data exchange standards as currently implemented.ConclusionCompared to standard EHR functionality, our visualization increased clinician efficiency when reviewing the charts of pediatric asthma patients. Application development efforts in an operational context should leverage existing health information exchange standards, such as FHIR, and evidence-based mixed methods approaches.     

Building the evidence-base to reduce electronic health record–related clinician burden

Clinicians face competing pressures of being clinically productive while using imperfect electronic health record (EHR) systems and maximizing face-to-face time with patients. EHR use is increasingly associated with clinician burnout and underscores the need for interventions to improve clinicians’ experiences. With an aim of addressing this need, we share evidence-based informatics approaches, pragmatic next steps, and future research directions to improve 3 of the highest contributors to EHR burden: (1) documentation, (2) chart review, and (3) inbox tasks. These approaches leverage speech recognition technologies, natural language processing, artificial intelligence, and redesign of EHR workflow and user interfaces. We also offer a perspective on how EHR vendors, healthcare system leaders, and policymakers all play an integral role while sharing responsibility in helping make evidence-based sociotechnical solutions available and easy to use.     

Openness of patients' reporting with use of electronic records: psychiatric clinicians' views

Objectives

Improvements in electronic health record (EHR) system development will require an understanding of psychiatric clinicians'' views on EHR system acceptability, including effects on psychotherapy communications, data-recording behaviors, data accessibility versus security and privacy, data quality and clarity, communications with medical colleagues, and stigma.

Design

Multidisciplinary development of a survey instrument targeting psychiatric clinicians who recently switched to EHR system use, focus group testing, data analysis, and data reliability testing.

Measurements

Survey of 120 university-based, outpatient mental health clinicians, with 56 (47%) responding, conducted 18 months after transition from a paper to an EHR system.

Results

Factor analysis gave nine item groupings that overlapped strongly with five a priori domains. Respondents both praised and criticized the EHR system. A strong majority (81%) felt that open therapeutic communications were preserved. Regarding data quality, content, and privacy, clinicians (63%) were less willing to record highly confidential information and disagreed (83%) with including their own psychiatric records among routinely accessed EHR systems.

Limitations

single time point; single academic medical center clinic setting; modest sample size; lack of prior instrument validation; survey conducted in 2005.

Conclusions

In an academic medical center clinic, the presence of electronic records was not seen as a dramatic impediment to therapeutic communications. Concerns regarding privacy and data security were significant, and may contribute to reluctances to adopt electronic records in other settings. Further study of clinicians'' views and use patterns may be helpful in guiding development and deployment of electronic records systems.     

Enhancing the use of EHR systems for pragmatic embedded research: lessons from the NIH Health Care Systems Research Collaboratory

ObjectiveWe identified challenges and solutions to using electronic health record (EHR) systems for the design and conduct of pragmatic research.Materials and MethodsSince 2012, the Health Care Systems Research Collaboratory has served as the resource coordinating center for 21 pragmatic clinical trial demonstration projects. The EHR Core working group invited these demonstration projects to complete a written semistructured survey and used an inductive approach to review responses and identify EHR-related challenges and suggested EHR enhancements.ResultsWe received survey responses from 20 projects and identified 21 challenges that fell into 6 broad themes: (1) inadequate collection of patient-reported outcome data, (2) lack of structured data collection, (3) data standardization, (4) resources to support customization of EHRs, (5) difficulties aggregating data across sites, and (6) accessing EHR data.DiscussionBased on these findings, we formulated 6 prerequisites for PCTs that would enable the conduct of pragmatic research: (1) integrate the collection of patient-centered data into EHR systems, (2) facilitate structured research data collection by leveraging standard EHR functions, usable interfaces, and standard workflows, (3) support the creation of high-quality research data by using standards, (4) ensure adequate IT staff to support embedded research, (5) create aggregate, multidata type resources for multisite trials, and (6) create re-usable and automated queries.ConclusionWe are hopeful our collection of specific EHR challenges and research needs will drive health system leaders, policymakers, and EHR designers to support these suggestions to improve our national capacity for generating real-world evidence.     

Mining tasks and task characteristics from electronic health record audit logs with unsupervised machine learning

ObjectiveThe characteristics of clinician activities while interacting with electronic health record (EHR) systems can influence the time spent in EHRs and workload. This study aims to characterize EHR activities as tasks and define novel, data-driven metrics.Materials and MethodsWe leveraged unsupervised learning approaches to learn tasks from sequences of events in EHR audit logs. We developed metrics characterizing the prevalence of unique events and event repetition and applied them to categorize tasks into 4 complexity profiles. Between these profiles, Mann-Whitney U tests were applied to measure the differences in performance time, event type, and clinician prevalence, or the number of unique clinicians who were observed performing these tasks. In addition, we apply process mining frameworks paired with clinical annotations to support the validity of a sample of our identified tasks. We apply our approaches to learn tasks performed by nurses in the Vanderbilt University Medical Center neonatal intensive care unit.ResultsWe examined EHR audit logs generated by 33 neonatal intensive care unit nurses resulting in 57 234 sessions and 81 tasks. Our results indicated significant differences in performance time for each observed task complexity profile. There were no significant differences in clinician prevalence or in the frequency of viewing and modifying event types between tasks of different complexities. We presented a sample of expert-reviewed, annotated task workflows supporting the interpretation of their clinical meaningfulness.ConclusionsThe use of the audit log provides an opportunity to assist hospitals in further investigating clinician activities to optimize EHR workflows.     

Research and applications: Primary care practitioners’ views on test result management in EHR-enabled health systems: a national survey

Context

Failure to notify patients of test results is common even when electronic health records (EHRs) are used to report results to practitioners. We sought to understand the broad range of social and technical factors that affect test result management in an integrated EHR-based health system.

Methods

Between June and November 2010, we conducted a cross-sectional, web-based survey of all primary care practitioners (PCPs) within the Department of Veterans Affairs nationwide. Survey development was guided by a socio-technical model describing multiple inter-related dimensions of EHR use.

Findings

Of 5001 PCPs invited, 2590 (51.8%) responded. 55.5% believed that the EHRs did not have convenient features for notifying patients of test results. Over a third (37.9%) reported having staff support needed for notifying patients of test results. Many relied on the patient''s next visit to notify them for normal (46.1%) and abnormal results (20.1%). Only 45.7% reported receiving adequate training on using the EHR notification system and 35.1% reported having an assigned contact for technical assistance with the EHR; most received help from colleagues (60.4%). A majority (85.6%) stayed after hours or came in on weekends to address notifications; less than a third reported receiving protected time (30.1%). PCPs strongly endorsed several new features to improve test result management, including better tracking and visualization of result notifications.

Conclusions

Despite an advanced EHR, both social and technical challenges exist in ensuring notification of test results to practitioners and patients. Current EHR technology requires significant improvement in order to avoid similar challenges elsewhere.     

Measures of electronic health record use in outpatient settings across vendors

Electronic health record (EHR) log data capture clinical workflows and are a rich source of information to understand variation in practice patterns. Variation in how EHRs are used to document and support care delivery is associated with clinical and operational outcomes, including measures of provider well-being and burnout. Standardized measures that describe EHR use would facilitate generalizability and cross-institution, cross-vendor research. Here, we describe the current state of outpatient EHR use measures offered by various EHR vendors, guided by our prior conceptual work that proposed seven core measures to describe EHR use. We evaluate these measures and other reporting options provided by vendors for maturity and similarity to previously proposed standardized measures. Working toward improved standardization of EHR use measures can enable and accelerate high-impact research on physician burnout and job satisfaction as well as organizational efficiency and patient health.     

Supporting health insurance expansion: do electronic health records have valid insurance verification and enrollment data?

Objective To validate electronic health record (EHR) insurance information for low-income pediatric patients at Oregon community health centers (CHCs), compared to reimbursement data and Medicaid coverage data.Materials and Methods Subjects Children visiting any of 96 CHCs (N = 69 189) from 2011 to 2012. Analysis The authors measured correspondence (whether or not the visit was covered by Medicaid) between EHR coverage data and (i) reimbursement data and (ii) coverage data from Medicaid.Results Compared to reimbursement data and Medicaid coverage data, EHR coverage data had high agreement (87% and 95%, respectively), sensitivity (0.97 and 0.96), positive predictive value (0.88 and 0.98), but lower kappa statistics (0.32 and 0.49), specificity (0.27 and 0.60), and negative predictive value (0.66 and 0.45). These varied among clinics.Discussion/Conclusions EHR coverage data for children had a high overall correspondence with Medicaid data and reimbursement data, suggesting that in some systems EHR data could be utilized to promote insurance stability in their patients. Future work should attempt to replicate these analyses in other settings.