Conceptual considerations for using EHR-based activity logs to measure clinician burnout and its effects

Electronic health records (EHR) use is often considered a significant contributor to clinician burnout. Informatics researchers often measure clinical workload using EHR-derived audit logs and use it for quantifying the contribution of EHR use to clinician burnout. However, translating clinician workload measured using EHR-based audit logs into a meaningful burnout metric requires an alignment with the conceptual and theoretical principles of burnout. In this perspective, we describe a systems-oriented conceptual framework to achieve such an alignment and describe the pragmatic realization of this conceptual framework using 3 key dimensions: standardizing the measurement of EHR-based clinical work activities, implementing complementary measurements, and using appropriate instruments to assess burnout and its downstream outcomes. We discuss how careful considerations of such dimensions can help in augmenting EHR-based audit logs to measure factors that contribute to burnout and for meaningfully assessing downstream patient safety outcomes.     

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.     

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.     

e-Measures: insight into the challenges and opportunities of automating publicly reported quality measures

Using electronic health records (EHR) to automate publicly reported quality measures is receiving increasing attention and is one of the promises of EHR implementation. Kaiser Permanente has fully or partly automated six of 13 the joint commission measure sets. We describe our experience with automation and the resulting time savings: a reduction by approximately 50% of abstractor time required for one measure set alone (surgical care improvement project). However, our experience illustrates the gap between the current and desired states of automated public quality reporting, which has important implications for measure developers, accrediting entities, EHR vendors, public/private payers, and government.     

Associations of physician burnout with organizational electronic health record support and after-hours charting

In 2017, 43.9% of US physicians reported symptoms of burnout. Poor electronic health record (EHR) usability and time-consuming data entry contribute to burnout. However, less is known about how modifiable dimensions of EHR use relate to burnout and how these associations vary by medical specialty. Using the KLAS Arch Collaborative’s large-scale nationwide physician (MD/DO) data, we used ordinal logistic regression to analyze associations between self-reported burnout and after-hours charting and organizational EHR support. We examined how these relationships differ by medical specialty, adjusting for confounders. Physicians reporting ≤ 5 hours weekly of after-hours charting were twice as likely to report lower burnout scores compared to those charting ≥6 hours (aOR: 2.43, 95% CI: 2.30, 2.57). Physicians who agree that their organization has done a great job with EHR implementation, training, and support (aOR: 2.14, 95% CI: 2.01, 2.28) were also twice as likely to report lower scores on the burnout survey question compared to those who disagree. Efforts to reduce after-hours charting and improve organizational EHR support could help address physician burnout.     

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.     

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.     

Misdiagnosis: Burnout,moral injury,and implications for the electronic health record

Burnout is a long-term stress reaction marked by emotional exhaustion, depersonalization, and a lack of sense of personal accomplishment. Burnout in clinicians is receiving significant attention. Some have proposed that clinicians are experiencing symptoms of moral injury, defined as “perpetrating, failing to prevent, bearing witness to, or learning about acts that transgress deeply held moral beliefs and expectations.” Current efforts to improve the electronic health record (EHR) have focused on improving the user experience to reduce burden that has been identified as a contributing factor to provider burnout. However, if EHRs are contributing to moral injury, improvements to user experience will not eliminate the effects on providers. Current research has not evaluated the risk for moral injury resulting from the use of EHRs. This Perspective reviews the differences between burnout and moral injury, discusses the implications for clinicians using EHRs, and highlights the need for research to better define the problem.     

Feeling and thinking: can theories of human motivation explain how EHR design impacts clinician burnout?

The psychology of motivation can help us understand the impact of electronic health records (EHRs) on clinician burnout both directly and indirectly. Informatics approaches to EHR usability tend to focus on the extrinsic motivation associated with successful completion of clearly defined tasks in clinical workflows. Intrinsic motivation, which includes the need for autonomy, sense-making, creativity, connectedness, and mastery is not well supported by current designs and workflows. This piece examines existing research on the importance of 3 psychological drives in relation to healthcare technology: goal-based decision-making, sense-making, and agency/autonomy. Because these motives are ubiquitous, foundational to human functioning, automatic, and unconscious, they may be overlooked in technological interventions. The results are increased cognitive load, emotional distress, and unfulfilling workplace environments. Ultimately, we hope to stimulate new research on EHR design focused on expanding functionality to support intrinsic motivation, which, in turn, would decrease burnout and improve care.     

Information blocking remains prevalent at the start of 21st Century Cures Act: results from a survey of health information exchange organizations

ObjectiveRecent policy making aims to prevent health systems, lectronic health record (EHR) vendors, and others from blocking the electronic sharing of patient data necessary for clinical care. We sought to assess the prevalence of information blocking prior to enforcement of these rules.Materials and MethodsWe conducted a national survey of health information exchange organizations (HIEs) to measure the prevalence of information blocking behaviors observed by these third-party entities. Eighty-nine of 106 HIEs (84%) meeting the inclusion criteria responded.ResultsThe majority (55%) of HIEs reported that EHR vendors at least sometimes engage in information blocking, while 30% of HIEs reported the same for health systems. The most common type of information blocking behavior EHR vendors engaged in was setting unreasonably high prices, which 42% of HIEs reported routinely observing. The most common type of information blocking behavior health systems engaged in was refusing to share information, which 14% of HIEs reported routinely observing. Reported levels of vendor information blocking was correlated with regional competition among vendors and information blocking was concentrated in some geographic regions.DiscussionOur findings are consistent with early reports, revealing persistently high levels of information blocking and important variation by actor, type of behavior, and geography. These trends reflect the observations and experiences of HIEs and their potential biases. Nevertheless, these data serve as a baseline against which to measure the impact of new regulations and to inform policy makers about the most common types of information blocking behaviors.ConclusionEnforcement aimed at reducing information blocking should consider variation in prevalence and how to most effectively target efforts.     

Informatics and operations—let's get integrated

The widespread adoption of commercial electronic health records (EHRs) presents a significant challenge to the field of informatics. In their current form, EHRs function as a walled garden and prevent the integration of outside tools and services. This impedes the widespread adoption and diffusion of research interventions into the clinic. In most institutions, EHRs are supported by clinical operations staff who are largely separate from their informatics counterparts. This relationship needs to change. Research informatics and clinical operations need to work more closely on the implementation and configuration of EHRs to ensure that they are used to collect high-quality data for research and improvement at the point of care. At the same time, the informatics community needs to lobby commercial EHR vendors to open their systems and design new architectures that allow for the integration of external applications and services.     

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.     

Comparison of Methodologies for Calculating Quality Measures Based on Administrative Data versus Clinical Data from an Electronic Health Record System: Implications for Performance Measures

New reimbursement policies and pay-for-performance programs to reward providers for producing better outcomes are proliferating. Although electronic health record (EHR) systems could provide essential clinical data upon which to base quality measures, most metrics in use were derived from administrative claims data. We compared commonly used quality measures calculated from administrative data to those derived from clinical data in an EHR based on a random sample of 125 charts of Medicare patients with diabetes. Using standard definitions based on administrative data (which require two visits with an encounter diagnosis of diabetes during the measurement period), only 75% of diabetics determined by manually reviewing the EHR (the gold standard) were identified. In contrast, 97% of diabetics were identified using coded information in the EHR.The discrepancies in identified patients resulted in statistically significant differences in the quality measures for frequency of HbA1c testing, control of blood pressure, frequency of testing for urine protein, and frequency of eye exams for diabetic patients. New development of standardized quality measures should shift from claims-based measures to clinically based measures that can be derived from coded information in an EHR. Using data from EHRs will also leverage their clinical content without adding burden to the care process.     

A systematic review of contributing factors of and solutions to electronic health record–related impacts on physician well-being

ObjectivePhysicians often describe the electronic health record (EHR) as a cumbersome impediment to meaningful work, which has important implications for physician well-being. This systematic review (1) assesses organizational, physician, and information technology factors associated with EHR-related impacts on physician well-being; and (2) highlights potential improvements to EHR form and function, as recommended by frontline physicians.Materials and methodsThe MEDLINE, Embase, CINAHL, PsycINFO, ProQuest, and Web of Science databases were searched for literature describing EHR use by physicians and markers of well-being.ResultsAfter reviewing 7388 article, 35 ultimately met the inclusion criteria. Multiple factors across all levels were associated with EHR-related well-being among physicians. Notable predictors amenable to interventions include (1) total EHR time, (2) after-hours EHR time, (3) on-site EHR support, (4) perceived EHR usability, (5) in-basket burden, and (6) documentation burden. Physician recommendations also echoed these themes.ConclusionsThere are multiple complex factors involved in EHR-related well-being among physicians. Our review shows physicians have recommendations that span from federal regulations to organizational policies to EHR modifications. Future research should assess multipronged interventions that address these factors. As primary stakeholders, physicians should be included in the planning and implementation of such modifications to ensure compatibility with physician needs and clinical workflows.     

The Relationship between Electronic Health Record Use and Quality of Care over Time

Objective

Electronic health records (EHRs) have the potential to advance the quality of care, but studies have shown mixed results. The authors sought to examine the extent of EHR usage and how the quality of care delivered in ambulatory care practices varied according to duration of EHR availability.

Methods

The study linked two data sources: a statewide survey of physicians' adoption and use of EHR and claims data reflecting quality of care as indicated by physicians' performance on widely used quality measures. Using four years of measurement, we combined 18 quality measures into 6 clinical condition categories. While the survey of physicians was cross-sectional, respondents indicated the year in which they adopted EHR. In an analysis accounting for duration of EHR use, we examined the relationship between EHR adoption and quality of care.

Results

The percent of physicians reporting adoption of EHR and availability of EHR core functions more than doubled between 2000 and 2005. Among EHR users in 2005, the average duration of EHR use was 4.8 years. For all 6 clinical conditions, there was no difference in performance between EHR users and non-users. In addition, for these 6 clinical conditions, there was no consistent pattern between length of time using an EHR and physicians performance on quality measures in both bivariate and multivariate analyses.

Conclusions

In this cross-sectional study, we found no association between duration of using an EHR and performance with respect to quality of care, although power was limited. Intensifying the use of key EHR features, such as clinical decision support, may be needed to realize quality improvement from EHRs. Future studies should examine the relationship between the extent to which physicians use key EHR functions and their performance on quality measures over time.     

Sequencing of EHR adoption among US hospitals and the impact of meaningful use

Objective

To examine whether there is a common sequence of adoption of electronic health record (EHR) functions among US hospitals, identify differences by hospital type, and assess the impact of meaningful use.

Materials and methods

Using 2008 American Hospital Association (AHA) Information Technology (IT) Supplement data, we calculate adoption rates of individual EHR functions, along with Loevinger homogeneity (H) coefficients, to assess the sequence of EHR adoption across hospitals. We compare adoption rates and Loevinger H coefficients for hospitals of different types to assess variation in sequencing. We qualitatively assess whether stage 1 meaningful use functions are those adopted early in the sequence.

Results

There is a common sequence of EHR adoption across hospitals, with moderate-to-strong homogeneity. Patient demographic and ancillary results functions are consistently adopted first, while physician notes, clinical reminders, and guidelines are adopted last. Small hospitals exhibited greater homogeneity than larger hospitals. Rural hospitals and non-teaching hospitals exhibited greater homogeneity than urban and teaching hospitals. EHR functions emphasized in stage 1 meaningful use are spread throughout the scale.

Discussion

Stronger homogeneity among small, rural, and non-teaching hospitals may be driven by greater reliance on vendors and less variation in the types of care they deliver. Stage 1 meaningful use is likely changing how hospitals sequence EHR adoption—in particular, by moving clinical guidelines and medication computerized provider order entry ahead in sequence.

Conclusions

While there is a common sequence underlying adoption of EHR functions, the degree of adherence to the sequence varies by key hospital characteristics. Stage 1 meaningful use likely alters the sequence.