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.     

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.     

Are Meaningful Use Stage 2 certified EHRs ready for interoperability? Findings from the SMART C-CDA Collaborative

Background and objective

Upgrades to electronic health record (EHR) systems scheduled to be introduced in the USA in 2014 will advance document interoperability between care providers. Specifically, the second stage of the federal incentive program for EHR adoption, known as Meaningful Use, requires use of the Consolidated Clinical Document Architecture (C-CDA) for document exchange. In an effort to examine and improve C-CDA based exchange, the SMART (Substitutable Medical Applications and Reusable Technology) C-CDA Collaborative brought together a group of certified EHR and other health information technology vendors.

Materials and methods

We examined the machine-readable content of collected samples for semantic correctness and consistency. This included parsing with the open-source BlueButton.js tool, testing with a validator used in EHR certification, scoring with an automated open-source tool, and manual inspection. We also conducted group and individual review sessions with participating vendors to understand their interpretation of C-CDA specifications and requirements.

Results

We contacted 107 health information technology organizations and collected 91 C-CDA sample documents from 21 distinct technologies. Manual and automated document inspection led to 615 observations of errors and data expression variation across represented technologies. Based upon our analysis and vendor discussions, we identified 11 specific areas that represent relevant barriers to the interoperability of C-CDA documents.

Conclusions

We identified errors and permissible heterogeneity in C-CDA documents that will limit semantic interoperability. Our findings also point to several practical opportunities to improve C-CDA document quality and exchange in the coming years.     

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.     

Evaluation and Certification of Computerized Provider Order Entry Systems

Computerized physician order entry (CPOE) is an application that is used to electronically write physician orders either in the hospital or in the outpatient setting. It is used in about 15% of U.S. Hospitals and a smaller percentage of ambulatory clinics. It is linked with clinical decision support, which provides much of the value of implementing it. A number of studies have assessed the impact of CPOE with respect to a variety of parameters, including costs of care, medication safety, use of guidelines or protocols, and other measures of the effectiveness or quality of care. Most of these studies have been undertaken at CPOE exemplar sites with homegrown clinical information systems. With the increasing implementation of commercial CPOE systems in various settings of care has come evidence that some implementation approaches may not achieve previously published results or may actually cause new errors or even harm. This has lead to new initiatives to evaluate CPOE systems, which have been undertaken by both vendors and other groups who evaluate vendors, focused on CPOE vendor capabilities and effective approaches to implementation that can achieve benefits seen in published studies. In addition, an electronic health record (EHR) vendor certification process is ongoing under the province of the Certification Commission for Health Information Technology (CCHIT) (which includes CPOE) that will affect the purchase and use of these applications by hospitals and clinics and their participation in public and private health insurance programs. Large employers have also joined this focus by developing flight simulation tools to evaluate the capabilities of these CPOE systems once implemented, potentially linking the results of such programs to reimbursement through pay for performance programs. The increasing role of CPOE systems in health care has invited much more scrutiny about the effectiveness of these systems in actual practice which has the potential to improve their ultimate performance.     

The association between perceived electronic health record usability and professional burnout among US nurses

ObjectivesTo measure nurse-perceived electronic health records (EHR) usability with a standardized metric of technology usability and evaluate its association with professional burnout.MethodsA cross-sectional survey of a random sample of US nurses was conducted in November 2017. EHR usability was measured with the System Usability Scale (SUS; range 0–100) and burnout with the Maslach Burnout Inventory.ResultsAmong the 86 858 nurses who were invited, 8638 (9.9%) completed the survey. The mean nurse-rated EHR SUS score was 57.6 (SD 16.3). A score of 57.6 is in the bottom 24% of scores across previous studies and categorized with a grade of “F.” On multivariable analysis adjusting for age, gender, race, ethnicity, relationship status, children, highest nursing-related degree, mean hours worked per week, years of nursing experience, advanced certification, and practice setting, nurse-rated EHR usability was associated with burnout with each 1 point more favorable SUS score and associated with a 2% lower odds of burnout (OR 0.98; 95% CI, 0.97–0.99; P < .001).ConclusionsNurses rated the usability of their current EHR in the low marginal range of acceptability using a standardized metric of technology usability. EHR usability and the odds of burnout were strongly associated with a dose-response relationship.     

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.     

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.     

A decade post-HITECH: Critical access hospitals have electronic health records but struggle to keep up with other advanced functions

ObjectiveDespite broad electronic health record (EHR) adoption in U.S. hospitals, there is concern that an “advanced use” digital divide exists between critical access hospitals (CAHs) and non-CAHs. We measured EHR adoption and advanced use over time to analyzed changes in the divide.Materials and MethodsWe used 2008 to 2018 American Hospital Association Information Technology survey data to update national EHR adoption statistics. We stratified EHR adoption by CAH status and measured advanced use for both patient engagement (PE) and clinical data analytics (CDA) domains. We used a linear probability regression for each domain with year-CAH interactions to measure temporal changes in the relationship between CAH status and advanced use.ResultsIn 2018, 98.3% of hospitals had adopted EHRs; there were no differences by CAH status. A total of 58.7% and 55.6% of hospitals adopted advanced PE and CDA functions, respectively. In both domains, CAHs were less likely to be advanced users: 46.6% demonstrated advanced use for PE and 32.0% for CDA. Since 2015, the advanced use divide has persisted for PE and widened for CDA.DiscussionEHR adoption among hospitals is essentially ubiquitous; however, CAHs still lag behind in advanced use functions critical to improving care quality. This may be rooted in different advanced use needs among CAH patients and lack of access to technical expertise.ConclusionsThe advanced use divide prevents CAH patients from benefitting from a fully digitized healthcare system. To close the widening gap in CDA, policymakers should consider partnering with vendors to develop implementation guides and standards for functions like dashboards and high-risk patient identification algorithms to better support CAH adoption.     

Characterizing physician EHR use with vendor derived data: a feasibility study and cross-sectional analysis

ObjectiveTo derive 7 proposed core electronic health record (EHR) use metrics across 2 healthcare systems with different EHR vendor product installations and examine factors associated with EHR time.Materials and MethodsA cross-sectional analysis of ambulatory physicians EHR use across the Yale-New Haven and MedStar Health systems was performed for August 2019 using 7 proposed core EHR use metrics normalized to 8 hours of patient scheduled time.ResultsFive out of 7 proposed metrics could be measured in a population of nonteaching, exclusively ambulatory physicians. Among 573 physicians (Yale-New Haven N = 290, MedStar N = 283) in the analysis, median EHR-Time₈ was 5.23 hours. Gender, additional clinical hours scheduled, and certain medical specialties were associated with EHR-Time₈ after adjusting for age and health system on multivariable analysis. For every 8 hours of scheduled patient time, the model predicted these differences in EHR time (P < .001, unless otherwise indicated): female physicians +0.58 hours; each additional clinical hour scheduled per month −0.01 hours; practicing cardiology −1.30 hours; medical subspecialties −0.89 hours (except gastroenterology, P = .002); neurology/psychiatry −2.60 hours; obstetrics/gynecology −1.88 hours; pediatrics −1.05 hours (P = .001); sports/physical medicine and rehabilitation −3.25 hours; and surgical specialties −3.65 hours.ConclusionsFor every 8 hours of scheduled patient time, ambulatory physicians spend more than 5 hours on the EHR. Physician gender, specialty, and number of clinical hours practicing are associated with differences in EHR time. While audit logs remain a powerful tool for understanding physician EHR use, additional transparency, granularity, and standardization of vendor-derived EHR use data definitions are still necessary to standardize EHR use measurement.     

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.     

Validity of electronic health record-derived quality measurement for performance monitoring

Background

Since 2007, New York City''s primary care information project has assisted over 3000 providers to adopt and use a prevention-oriented electronic health record (EHR). Participating practices were taught to re-adjust their workflows to use the EHR built-in population health monitoring tools, including automated quality measures, patient registries and a clinical decision support system. Practices received a comprehensive suite of technical assistance, which included quality improvement, EHR customization and configuration, privacy and security training, and revenue cycle optimization. These services were aimed at helping providers understand how to use their EHR to track and improve the quality of care delivered to patients.

Materials and Methods

Retrospective electronic chart reviews of 4081 patient records across 57 practices were analyzed to determine the validity of EHR-derived quality measures and documented preventive services.

Results

Results from this study show that workflow and documentation habits have a profound impact on EHR-derived quality measures. Compared with the manual review of electronic charts, EHR-derived measures can undercount practice performance, with a disproportionately negative impact on the number of patients captured as receiving a clinical preventive service or meeting a recommended treatment goal.

Conclusion

This study provides a cautionary note in using EHR-derived measurement for public reporting of provider performance or use for payment.     

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.     

The marginal value of pre-visit paper reminders when added to a multifaceted electronic health record based quality improvement system

Objective

We have reported that implementation of an electronic health record (EHR) based quality improvement system that included point-of-care electronic reminders accelerated improvement in performance for multiple measures of chronic disease care and preventive care during a 1-year period. This study examined whether providing pre-visit paper quality reminders could further improve performance, especially for physicians whose performance had not improved much during the first year.

Design

Time-series analysis at a large internal medicine practice using a commercial EHR. All patients eligible for each measure were included (range approximately 100–7500).

Measurements

The proportion of eligible patients in the practice who satisfied each of 15 quality measures after removing those with exceptions from the denominator. To analyze changes in performance for individual physicians, two composite measures were used: prescribing seven essential medications and completion of five preventive services.

Results

During the year after implementing pre-encounter reminders, performance continued to improve for eight measures, remained stable for four, and declined for three. Physicians with the worst performance at the start of the pre-encounter reminders showed little absolute improvement over the next year, and most remained below the median performance for physicians in the practice.

Conclusions

Paper pre-encounter reminders did not appear to improve performance beyond electronic point-of-care reminders in the EHR alone. Lagging performance is likely not due to providers'' EHR workflow alone, and trying to step backwards and use paper reminders in addition to point-of-care reminders in the EHR may not be an effective strategy for engaging slow adopters.     

High-throughput phenotyping with temporal sequences

ObjectiveHigh-throughput electronic phenotyping algorithms can accelerate translational research using data from electronic health record (EHR) systems. The temporal information buried in EHRs is often underutilized in developing computational phenotypic definitions. This study aims to develop a high-throughput phenotyping method, leveraging temporal sequential patterns from EHRs.Materials and MethodsWe develop a representation mining algorithm to extract 5 classes of representations from EHR diagnosis and medication records: the aggregated vector of the records (aggregated vector representation), the standard sequential patterns (sequential pattern mining), the transitive sequential patterns (transitive sequential pattern mining), and 2 hybrid classes. Using EHR data on 10 phenotypes from the Mass General Brigham Biobank, we train and validate phenotyping algorithms.ResultsPhenotyping with temporal sequences resulted in a superior classification performance across all 10 phenotypes compared with the standard representations in electronic phenotyping. The high-throughput algorithm’s classification performance was superior or similar to the performance of previously published electronic phenotyping algorithms. We characterize and evaluate the top transitive sequences of diagnosis records paired with the records of risk factors, symptoms, complications, medications, or vaccinations.DiscussionThe proposed high-throughput phenotyping approach enables seamless discovery of sequential record combinations that may be difficult to assume from raw EHR data. Transitive sequences offer more accurate characterization of the phenotype, compared with its individual components, and reflect the actual lived experiences of the patients with that particular disease.ConclusionSequential data representations provide a precise mechanism for incorporating raw EHR records into downstream machine learning. Our approach starts with user interpretability and works backward to the technology.     

Electronic Health Records in Specialty Care: A Time-Motion Study

BackgroundElectronic health records (EHRs) have great potential to improve safety, quality, and efficiency in medicine. However, adoption has been slow, and a key concern has been that clinicians will require more time to complete their work using EHRs. Most previous studies addressing this issue have been done in primary care.ObjectiveTo assess the impact of using an EHR on specialists’ time.DesignProspective, before-after trial of the impact of an EHR on attending physician time in four specialty clinics at an integrated delivery system: cardiology, dermatology, endocrine, and pain.MeasurementsWe used a time-motion method to measure physician time spent in one of 85 designated activities.ResultsAttending physicians were monitored before and after the switch from paper records to a web-based ambulatory EHR. Across all specialties, 15 physicians were observed treating 157 patients while still using paper-based records, and 15 physicians were observed treating 146 patients after adoption. Following EHR implementation, the average adjusted total time spent per patient across all specialties increased slightly but not significantly (Δ = 0.94 min., p = 0.83) from 28.8 (SE = 3.6) to 29.8 (SE = 3.6) min.ConclusionThese data suggest that implementation of an EHR had little effect on overall visit time in specialty clinics.     

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.     

Democratizing EHR analyses with FIDDLE: a flexible data-driven preprocessing pipeline for structured clinical data

ObjectiveIn applying machine learning (ML) to electronic health record (EHR) data, many decisions must be made before any ML is applied; such preprocessing requires substantial effort and can be labor-intensive. As the role of ML in health care grows, there is an increasing need for systematic and reproducible preprocessing techniques for EHR data. Thus, we developed FIDDLE (Flexible Data-Driven Pipeline), an open-source framework that streamlines the preprocessing of data extracted from the EHR.Materials and MethodsLargely data-driven, FIDDLE systematically transforms structured EHR data into feature vectors, limiting the number of decisions a user must make while incorporating good practices from the literature. To demonstrate its utility and flexibility, we conducted a proof-of-concept experiment in which we applied FIDDLE to 2 publicly available EHR data sets collected from intensive care units: MIMIC-III and the eICU Collaborative Research Database. We trained different ML models to predict 3 clinically important outcomes: in-hospital mortality, acute respiratory failure, and shock. We evaluated models using the area under the receiver operating characteristics curve (AUROC), and compared it to several baselines.ResultsAcross tasks, FIDDLE extracted 2,528 to 7,403 features from MIMIC-III and eICU, respectively. On all tasks, FIDDLE-based models achieved good discriminative performance, with AUROCs of 0.757–0.886, comparable to the performance of MIMIC-Extract, a preprocessing pipeline designed specifically for MIMIC-III. Furthermore, our results showed that FIDDLE is generalizable across different prediction times, ML algorithms, and data sets, while being relatively robust to different settings of user-defined arguments.ConclusionsFIDDLE, an open-source preprocessing pipeline, facilitates applying ML to structured EHR data. By accelerating and standardizing labor-intensive preprocessing, FIDDLE can help stimulate progress in building clinically useful ML tools for EHR data.     

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.