OBJECTIVE: This study aimed to evaluate how differences in case complexity affect information-processing burden, as measured by eye-tracking, as well as the efficiency of electronic health record (EHR) use among healthcare providers in the intensive care unit. MATERIALS AND METHODS: This cross-sectional study recruited providers from 4 U.S. medical centers that use 2 prominent EHR systems (Epic and Oracle). After reporting demographic information, participants reviewed 2 complex cases and 2 standard cases in their institution's EHR system and then responded to 5 questions about each case, yielding a performance score. Information-processing burden was assessed by measuring the number of eye fixations via eye-tracking software. The efficiency of EHR use was assessed by measuring the task completion time, number of mouse clicks per minute, number of EHR screens viewed, and performance score. RESULTS: Eighty-one providers were included for analysis. Providers exhibited significantly more eye fixations (P < .001) and longer task completion times (P < .001) for complex cases than for standard cases. There were also significantly fewer mouse clicks per minute during complex cases (P < .001). Reviewing a complex case first led to significantly more eye fixations (P = .015) and longer task completion times (P < .01) than when a standard case was presented first. Case complexity did not significantly affect performance scores or the number of EHR screens viewed. DISCUSSION: Higher case complexity was shown to be associated with greater information-processing burden and less efficient EHR use. These findings have implications for enhancing the efficiency of EHR use, thereby leading to improved clinical decision-making and patient safety. Furthermore, reviewing complex cases first led to a greater information-processing burden, suggesting that providers could benefit from "warming up" with standard cases before reviewing complex cases.

OBJECTIVES: We analyzed trends in adoption of advanced patient engagement and clinical data analytics functionalities among critical access hospitals (CAHs) and non-CAHs to assess how historical gaps have changed. MATERIALS AND METHODS: We used 2014, 2018, and 2023 data from the American Hospital Association Annual Survey IT Supplement to measure differences in adoption rates (ie, the "adoption gap") of patient engagement and clinical data analytics functionalities across CAHs and non-CAHs. We measured changes over time in CAH and non-CAH adoption of 6 "core" clinical data analytics functionalities, 5 "core" patient engagement functionalities, 5 new patient engagement functionalities, and 3 bulk data export use cases. We constructed 2 composite measures for core functionalities and analyzed adoption for other functionalities individually. RESULTS: Core functionality adoption increased from 21% of CAHs in 2014 to 56% in 2023 for clinical data analytics and 18% to 49% for patient engagement. The CAH adoption gap in both domains narrowed from 2018 to 2023 (both P < .01). More than 90% of all hospitals had adopted viewing and downloading electronic data and clinical notes by 2023. The largest CAH adoption gaps in 2023 were for Fast Healthcare Interoperability Resources (FHIR) bulk export use cases (eg, analytics and reporting: 63% of CAHs, 81% of non-CAHs, P < .001). DISCUSSION: Adoption of advanced electronic health record functionalities has increased for CAHs and non-CAHs, and some adoption gaps have been closed since 2018. However, CAHs may continue to struggle with clinical data analytics and FHIR-based functionalities. CONCLUSION: Some crucial patient engagement functionalities have reached near-universal adoption; however, policymakers should consider programs to support CAHs in closing remaining adoption gaps.

OBJECTIVE: To quantify how many patient scheduled hours would result in a 40-h work week (PSH40) for ambulatory physicians and to determine how PSH40 varies by specialty and practice type. METHODS: We calculated PSH40 for 186 188 ambulatory physicians across 395 organizations from November 2021 through April 2022 stratified by specialty. RESULTS: Median PSH40 for the sample was 33.2 h (IQR: 28.7-36.5). PSH40 was lowest in infectious disease (26.2, IQR: 21.6-31.1), geriatrics (27.2, IQR: 21.5-32.0) and hematology (28.6, IQR: 23.6-32.6) and highest in plastic surgery (35.7, IQR: 32.8-37.7), pain medicine (35.8, IQR: 32.6-37.9) and sports medicine (36.0, IQR: 33.3-38.1). DISCUSSION: Health system leaders and physicians will benefit from data driven and transparent discussions about work hour expectations. The PSH40 measure can also be used to quantify the impact of variations in the clinical care environment on the in-person ambulatory patient care time available to physicians. CONCLUSIONS: PSH40 is a novel measure that can be generated from vendor-derived metrics and used by operational leaders to inform work expectations. It can also support research into the impact of changes in the care environment on physicians' workload and capacity.

OBJECTIVES: We analyzed the degree to which daily documentation patterns in primary care varied and whether specific patterns, consistency over time, and deviations from clinicians' usual patterns were associated with note-writing efficiency. MATERIALS AND METHODS: We used electronic health record (EHR) active use data from the Oracle Cerner Advance platform capturing hourly active documentation time for 498 physicians and advance practice clinicians (eg, nurse practitioners) for 65 152 clinic days. We used k-means clustering to identify distinct daily patterns of active documentation time and analyzed the relationship between these patterns and active documentation time per note. We determined each primary care clinician's (PCC) modal documentation pattern and analyzed how consistency and deviations were related to documentation efficiency. RESULTS: We identified 8 distinct daily documentation patterns; the 3 most common patterns accounted for 80.6% of PCC-days and differed primarily in average volume of documentation time (78.1 minutes per day; 35.4 minutes per day; 144.6 minutes per day); associations with note efficiency were mixed. PCCs with >80% of days attributable to a single pattern demonstrated significantly more efficient documentation than PCCs with lower consistency; for high-consistency PCCs, days that deviated from their usual patterns were associated with less efficient documentation. DISCUSSION: We found substantial variation in efficiency across daily documentation patterns, suggesting that PCC-level factors like EHR facility and consistency may be more important than when documentation occurs. There were substantial efficiency returns to consistency, and deviations from consistent patterns were costly. CONCLUSION: Organizational leaders aiming to reduce documentation burden should pay specific attention to the ability for PCCs to execute consistent documentation patterns day-to-day.

OBJECTIVES: Physician burnout in the US has reached crisis levels, with one source identified as extensive after-hours documentation work in the electronic health record (EHR). Evidence has illustrated that physician preferences for after-hours work vary, such that after-hours work may not be universally burdensome. Our objectives were to analyze variation in preferences for after-hours documentation and assess if preferences mediate the relationship between after-hours documentation time and burnout. MATERIALS AND METHODS: We combined EHR active use data capturing physicians' hourly documentation work with survey data capturing documentation preferences and burnout. Our sample included 318 ambulatory physicians at MedStar Health. We conducted a mediation analysis to estimate if and how preferences mediated the relationship between after-hours documentation time and burnout. Our primary outcome was physician-reported burnout. We measured preferences for after-hours documentation work via a novel survey instrument (Burden Scenarios Assessment). We measured after-hours documentation time in the EHR as the total active time respondents spent documenting between 7 pm and 3 am. RESULTS: Physician preferences varied, with completing clinical documentation after clinic hours while at home the scenario rated most burdensome (52.8% of physicians), followed by dealing with prior authorization (49.5% of physicians). In mediation analyses, preferences partially mediated the relationship between after-hours documentation time and burnout. DISCUSSION: Physician preferences regarding EHR-based work play an important role in the relationship between after-hours documentation time and burnout. CONCLUSION: Studies of EHR work and burnout should incorporate preferences, and operational leaders should assess preferences to better target interventions aimed at EHR-based contributors to burnout.

INTRODUCTION: Research on how people interact with electronic health records (EHRs) increasingly involves the analysis of metadata on EHR use. These metadata can be recorded unobtrusively and capture EHR use at a scale unattainable through direct observation or self-reports. However, there is substantial variation in how metadata on EHR use are recorded, analyzed and described, limiting understanding, replication, and synthesis across studies. RECOMMENDATIONS: In this perspective, we provide guidance to those working with EHR use metadata by describing 4 common types, how they are recorded, and how they can be aggregated into higher-level measures of EHR use. We also describe guidelines for reporting analyses of EHR use metadata-or measures of EHR use derived from them-to foster clarity, standardization, and reproducibility in this emerging and critical area of research.

OBJECTIVE: We analyze observed reductions in physician note length and documentation time, 2 contributors to electronic health record (EHR) burden and burnout. MATERIALS AND METHODS: We used EHR metadata from January to May, 2021 for 130 079 ambulatory physician Epic users. We identified cohorts of physicians who decreased note length and/or documentation time and analyzed changes in their note composition. RESULTS: 37 857 physicians decreased either note length (n = 15 647), time in notes (n = 15 417), or both (n = 6793). Note length decreases were primarily attributable to reductions in copy/paste text (average relative change of -18.9%) and templated text (-17.2%). Note time decreases were primarily attributable to reductions in manual text (-27.3%) and increases in note content from other care team members (+21.1%). DISCUSSION: Organizations must consider priorities and tradeoffs in the distinct approaches needed to address different contributors to EHR burden. CONCLUSION: Future research should explore scalable burden-reduction initiatives responsive to both note bloat and documentation time.

Recent policy changes have required health care delivery organizations provide patients electronic access to their clinical notes free of charge. There is concern that this could have an unintended consequence of increased electronic health record (EHR) work as clinicians may feel the need to adapt their documentation practices in light of their notes being accessible to patients, potentially exacerbating EHR-induced clinician burnout. Using a national, longitudinal data set consisting of all ambulatory care physicians and advance practice providers using an Epic Systems EHR, we used an interrupted time-series analysis to evaluate the immediate impact of the policy change on clinician note length and time spent documenting in the EHR. We found no evidence of a change in note length or time spent writing notes following the implementation of the policy, suggesting patient access to clinical notes did not increase documentation workload for clinicians.

OBJECTIVE: This study aimed to understand the association between primary care physician (PCP) proficiency with the electronic health record (EHR) system and time spent interacting with the EHR. MATERIALS AND METHODS: We examined the use of EHR proficiency tools among PCPs at one large academic health system using EHR-derived measures of clinician EHR proficiency and efficiency. Our main predictors were the use of EHR proficiency tools and our outcomes focused on 4 measures assessing time spent in the EHR: (1) total time spent interacting with the EHR, (2) time spent outside scheduled clinical hours, (3) time spent documenting, and (4) time spent on inbox management. We conducted multivariable quantile regression models with fixed effects for physician-level factors and time in order to identify factors that were independently associated with time spent in the EHR. RESULTS: Across 441 primary care physicians, we found mixed associations between certain EHR proficiency behaviors and time spent in the EHR. Across EHR activities studied, QuickActions, SmartPhrases, and documentation length were positively associated with increased time spent in the EHR. Models also showed a greater amount of help from team members in note writing was associated with less time spent in the EHR and documenting. DISCUSSION: Examining the prevalence of EHR proficiency behaviors may suggest targeted areas for initial and ongoing EHR training. Although documentation behaviors are key areas for training, team-based models for documentation and inbox management require further study. CONCLUSIONS: A nuanced association exists between physician EHR proficiency and time spent in the EHR.

OBJECTIVE: The aim of this article is to compare the aims, measures, methods, limitations, and scope of studies that employ vendor-derived and investigator-derived measures of electronic health record (EHR) use, and to assess measure consistency across studies. MATERIALS AND METHODS: We searched PubMed for articles published between July 2019 and December 2021 that employed measures of EHR use derived from EHR event logs. We coded the aims, measures, methods, limitations, and scope of each article and compared articles employing vendor-derived and investigator-derived measures. RESULTS: One hundred and two articles met inclusion criteria; 40 employed vendor-derived measures, 61 employed investigator-derived measures, and 1 employed both. Studies employing vendor-derived measures were more likely than those employing investigator-derived measures to observe EHR use only in ambulatory settings (83% vs 48%, P = .002) and only by physicians or advanced practice providers (100% vs 54% of studies, P < .001). Studies employing vendor-derived measures were also more likely to measure durations of EHR use (P < .001 for 6 different activities), but definitions of measures such as time outside scheduled hours varied widely. Eight articles reported measure validation. The reported limitations of vendor-derived measures included measure transparency and availability for certain clinical settings and roles. DISCUSSION: Vendor-derived measures are increasingly used to study EHR use, but only by certain clinical roles. Although poorly validated and variously defined, both vendor- and investigator-derived measures of EHR time are widely reported. CONCLUSION: The number of studies using event logs to observe EHR use continues to grow, but with inconsistent measure definitions and significant differences between studies that employ vendor-derived and investigator-derived measures.

OBJECTIVE: Despite 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 METHODS: We 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. RESULTS: In 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. DISCUSSION: EHR 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. CONCLUSIONS: The 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.

OBJECTIVE: To assess the practice- and market-level factors associated with the amount of provider health information exchange (HIE) use. MATERIALS AND METHODS: Provider 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. RESULTS: Providers 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. DISCUSSION: HIE 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. CONCLUSION: This 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.

Journal of the American Medical Informatics Association, doi: 10.1093/jamia/ocab024 The author name “Julia Adler-Milstein” was incorrectly given as “Julia Adler-Milstien”. This has been corrected online.

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.

We sought to identify barriers to hospital reporting of electronic surveillance data to local, state, and federal public health agencies and the impact on areas projected to be overwhelmed by the COVID-19 pandemic. Using 2018 American Hospital Association data, we identified barriers to surveillance data reporting and combined this with data on the projected impact of the COVID-19 pandemic on hospital capacity at the hospital referral region level. Our results find the most common barrier was public health agencies lacked the capacity to electronically receive data, with 41.2% of all hospitals reporting it. We also identified 31 hospital referral regions in the top quartile of projected bed capacity needed for COVID-19 patients in which over half of hospitals in the area reported that the relevant public health agency was unable to receive electronic data. Public health agencies' inability to receive electronic data is the most prominent hospital-reported barrier to effective syndromic surveillance. This reflects the policy commitment of investing in information technology for hospitals without a concomitant investment in IT infrastructure for state and local public health agencies.

We appreciate the productive discussion about our article, Barriers to Hospital Electronic Public Health Reporting and Implications for the COVID-19 Pandemic, which serves to advance efforts to strengthen information sharing between hospitals and public health agencies. The letter by Staes et al raises important considerations and contributes to a useful dialogue regarding the current state and barriers to hospital–public health agency electronic data sharing as well as opportunities to increase knowledge of extant public health capabilities and foster more comprehensive utilization of those capabilities. We agree that the current level of interoperability between hospitals and public health agencies is not at an ideal level, even under normal circumstances, and critical infrastructure gaps have been laid bare as a result of the COVID-19 pandemic.1 Our findings should not be interpreted as evidence of fault or sole responsibility on either the hospital or public health side for barriers that impede effective information sharing. Indeed, we are careful to focus our discussion on the likely roots of current challenges, which we trace to federal incentive programs that have focused almost entirely on health care delivery organizations, rather than the public health agencies and other community partners that play a critical role in emergency preparedness, disease monitoring, and efforts to improve population health. Instead, we believe the value in our findings comes in prompting better coordination between public health and clinical partners to address current shortcomings and ensure that robust electronic data exchange is meeting the needs for both clinical and public health organizations. The first step towards this coordination is better understanding of how the other side views the issues. We suspect that, when citing barriers to public health receipt of data, hospitals are not referring specifically to the pure technical capability (which the letter indicates exists at a broad level). As with any interoperability effort, functional interoperability requires the technological capability to send and receive data alongside the nontechnical factors such as data governance, incentives to share electronically, a clear onboarding and testing process, and more. Surveys like the AHA IT Supplement shed some light on where the sticky points may lie but isn’t able to home in on and separate one from another. Given these constraints, the key insight from our study is that 1 side of the exchange—namely, the Chief Information Officers or Chief Medical Information Officers who typically respond to the AHA IT Supplement—perceive some aspect of public health agency ability to receive data as a barrier to effective electronic exchange. Regardless of whether that barrier is technical in nature or related to a socio-technical process such as data governance, public health agencies should be aware that nearly 40% of potential exchange partner hospitals view their ability to receive data electronically as a barrier to effective exchange. Awareness of this perceived barrier—especially if it is inconsistent with barriers perceived by public health agencies—is a critical first step towards resolving outstanding issues and clarifying any misunderstandings. We suggest that 1 possible strategy going forward is for public health agencies and hospitals to publicly list their electronic exchange partners, similar to how health information exchange organizations publicly list participants.2 This may help both clinical and public health organizations better understand who is successfully sharing data, enable both parties to engage in peer learning and best practice dissemination, serve as an accountability mechanism for all parties, and allow researchers to differentiate between stated ability to send and receive data electronically and actual connectivity in practice. Secondarily, national surveys of public health agency informatics infrastructure and capabilities should seek to capture more detailed data than they have historically, which has thus far prevented insight into such basic questions as regional variation in capabilities, much less the geographic or proportional scope of connectivity for a given public health agency.3,4 We applaud public health agencies’ hard work on building electronic case reporting capabilities through platforms such as AIMS. However, it’s important to note that the AIMS service is primarily facilitating data exchange between public health laboratories and public health agencies, not from hospitals or other clinical exchange partners. This underscores the complex nature of interoperability for public health surveillance, which frequently involves local and state agencies establishing and maintaining bidirectional interoperability with multiple exchange partners of many types. Most importantly, we wholeheartedly agree with Staes et al that increasing support for public health agencies to build a more robust informatics infrastructure is a critical policy goal to ensure accurate, reliable data exchange.