Electronic Health Record Time Research Articles

Virtual Scribes and Physician Time Spent on Electronic Health Records

Time on the electronic health record (EHR) is associated with burnout among physicians. Newer virtual scribe models, which enable support from either a real-time or asynchronous scribe, have the potential to reduce the burden of the EHR and EHR-related documentation. To characterize the association of use of virtual scribes with changes in physicians' EHR time and note and order composition and to identify the physician, scribe, and scribe response factors associated with changes in EHR time upon virtual scribe use. Retrospective, pre-post quality improvement study of 144 physicians across specialties who had used a scribe for at least 3 months from January 2020 to September 2022, were affiliated with Brigham and Women's Hospital and Massachusetts General Hospital, and cared for patients in the outpatient setting. Data were analyzed from November 2022 to January 2024. Use of either a real-time or asynchronous virtual scribe. Total EHR time, time on notes, and pajama time (5:30 pm to 7:00 am on weekdays and nonscheduled weekends and holidays), all per appointment; proportion of the note written by the physician and team contribution to orders. The main study sample included 144 unique physicians who had used a virtual scribe for at least 3 months in 152 unique scribe participation episodes (134 [88.2%] had used an asynchronous scribe service). Nearly two-thirds of the physicians (91 physicians [63.2%]) were female and more than half (86 physicians [59.7%]) were in primary care specialties. Use of a virtual scribe was associated with significant decreases in total EHR time per appointment (mean [SD] of 5.6 [16.4] minutes; P < .001) in the 3 months after vs the 3 months prior to scribe use. Scribe use was also associated with significant decreases in note time per appointment and pajama time per appointment (mean [SD] of 1.3 [3.3] minutes; P < .001 and 1.1 [4.0] minutes; P = .004). In a multivariable linear regression model, the following factors were associated with significant decreases in total EHR time per appointment with a scribe use at 3 months: practicing in a medical specialty (-7.8; 95% CI, -13.4 to -2.2 minutes), greater baseline EHR time per appointment (-0.3; 95% CI, -0.4 to -0.2 minutes per additional minute of baseline EHR time), and decrease in the percentage of the note contributed by the physician (-9.1; 95% CI, -17.3 to -0.8 minutes for every percentage point decrease). In 2 academic medical centers, use of virtual scribes was associated with significant decreases in total EHR time, time spent on notes, and pajama time, all per appointment. Virtual scribes may be particularly effective among medical specialists and those physicians with greater baseline EHR time.

Explaining Variability in Electronic Health Record Effort in Primary Care Ambulatory Encounters.

Electronic health record (EHR) user interface event logs are fast providing another perspective on the value and efficiency EHR technology brings to health care. Analysis of these detailed usage data has demonstrated their potential to identify EHR and clinical process design factors related to user efficiency, satisfaction, and burnout. This study aimed to analyze the event log data across 26 different health systems to determine the variability of use of a single vendor's EHR based on four event log metrics, at the individual, practice group, and health system levels. We obtained de-identified event log data recorded from June 1, 2018, to May 31, 2019, from 26 health systems' primary care physicians. We estimated the variability in total Active EHR Time, Documentation Time, Chart Review Time, and Ordering Time across health systems, practice groups, and individual physicians. In total, 5,444 physicians (Family Medicine: 3,042 and Internal Medicine: 2,422) provided care in a total of 2,285 different practices nested in 26 health systems. Health systems explain 1.29, 3.55, 3.45, and 3.30% of the total variability in Active Time, Documentation Time, Chart Review Time, and Ordering Time, respectively. Practice-level variability was estimated to be 7.96, 13.52, 8.39, and 5.57%, respectively, and individual physicians explained the largest proportion of the variability for those same outcomes 17.09, 27.49, 17.51, and 19.75%, respectively. The most variable physician EHR usage patterns occurs at the individual physician level and decreases as you move up to the practice and health system levels. This suggests that interventions to improve individual users' EHR usage efficiency may have the most potential impact compared with those directed at health system or practice levels.

Frontiers in Operations: Does Physician’s Choice of When to Perform EHR Tasks Influence Total EHR Workload?

Problem definition: Physicians spend more than five hours a day working on Electronic Health Record (EHR) systems and more than an hour doing EHR tasks after the end of the workday. Numerous studies have identified the detrimental effects of excessive EHR use and after-hours work, including physician burnout, physician attrition, and appointment delays. However, EHR time is not purely an exogenous factor because it depends on physician usage behavior that could have important operational consequences. Interestingly, prior literature has not considered this topic rigorously. In this paper, we investigate how physicians’ workflow decisions on when to perform EHR tasks affect: (1) total time on EHR and (2) time spent after work. Methodology/results: Our data comprise around 150,000 appointments from 74 physicians from a large Academic Medical Center Family Medicine unit. Our data set contains detailed, process-level time stamps of appointment progression and EHR use. We find that the effect of working on EHR systems depends on whether the work is done before or after an appointment. Pre-appointment EHR work reduces total EHR workload and after-work hours spent on EHR. Post-appointment EHR work reduces after-work hours on EHR but increases total EHR time. We find that increasing idle time between appointments can encourage both pre- and post-appointment EHR work. Managerial implications: Our results not only help us understand the timing and structure of work on secondary tasks more generally but also will help healthcare administrators create EHR workflows and appointment schedules to reduce physician burnout associated with excessive EHR use. History: This paper has been accepted in the Manufacturing &amp; Service Operations Management Frontiers in Operations Initiative. Funding: The research conducted for this paper received partial funding from the Center of Business for Health at the Kenan-Flagler Business School, University of North Carolina at Chapel Hill. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0028 .

More Tethered to the EHR: EHR Workload Trends Among Academic Primary Care Physicians, 2019-2023.

The purpose of this study is to evaluate recent trends in primary care physician (PCP) electronic health record (EHR) workload. This longitudinal study observed the EHR use of 141 academic PCPs over 4 years (May 2019 to March 2023). Ambulatory full-time equivalency (aFTE), visit volume, and panel size were evaluated. Electronic health record time and inbox message volume were measured per 8 hours of scheduled clinic appointments. From the pre-COVID-19 pandemic year (May 2019 to February 2020) to the most recent study year (April 2022 to March 2023), the average time PCPs spent in the EHR per 8 hours of scheduled clinic appointments increased (+28.4 minutes, 7.8%), as did time in orders (+23.1 minutes, 58.9%), inbox (+14.0 minutes, 24.4%), chart review (+7.2 minutes, 13.0%), notes (+2.9 minutes, 2.3%), outside scheduled hours on days with scheduled appointments (+6.4 minutes, 8.2%), and on unscheduled days (+13.6 minutes, 19.9%). Primary care physicians received more patient medical advice requests (+5.4 messages, 55.5%) and prescription messages (+2.3, 19.5%) per 8 hours of scheduled clinic appointments, but fewer patient calls (-2.8, -10.5%) and results messages (-0.3, -2.7%). While total time in the EHR continued to increase in the final study year (+7.7 minutes, 2.0%), inbox time decreased slightly from the year prior (-2.2 minutes, -3.0%). Primary care physicians' average aFTE decreased 5.2% from 0.66 to 0.63 over 4 years. Primary care physicians' time in the EHR continues to grow. While PCPs' inbox time may be stabilizing, it is still substantially higher than pre-pandemic levels. It is imperative health systems develop strategies to change the EHR workload trajectory to minimize PCPs' occupational stress and mitigate unnecessary reductions in effective physician workforce resulting from the increased EHR burden.

System-Level Factors and Time Spent on Electronic Health Records by Primary Care Physicians

Primary care physicians (PCPs) spend the most time on the electronic health record (EHR) of any specialty. Thus, it is critical to understand what factors contribute to varying levels of PCP time spent on EHRs. To characterize variation in EHR time across PCPs and primary care clinics, and to describe how specific PCP, patient panel, clinic, and team collaboration factors are associated with PCPs' time spent on EHRs. This cross-sectional study included 307 PCPs practicing across 31 primary care clinics at Massachusetts General Hospital and Brigham and Women's Hospital during 2021. Data were analyzed from October 2022 to October 2023. Total per-visit EHR time, total per-visit pajama time (ie, time spent on the EHR between 5:30 pm to 7:00 am and on weekends), and total per-visit time on the electronic inbox as measured by activity log data derived from an EHR database. The sample included 307 PCPs (183 [59.6%] female). On a per-visit basis, PCPs spent a median (IQR) of 36.2 (28.9-45.7) total minutes on the EHR, 6.2 (3.1-11.5) minutes of pajama time, and 7.8 (5.5-10.7) minutes on the electronic inbox. When comparing PCP time expenditure by clinic, median (IQR) total EHR time, median (IQR) pajama time, and median (IQR) electronic inbox time ranged from 23.5 (20.7-53.1) to 47.9 (30.6-70.7) minutes per visit, 1.7 (0.7-10.5) to 13.1 (7.7-28.2) minutes per visit, and 4.7 (4.1-5.2) to 10.8 (8.9-15.2) minutes per visit, respectively. In a multivariable model with an outcome of total per-visit EHR time per visit, an above median percentage of teamwork on orders was associated with 3.81 (95% CI, 0.49-7.13) minutes per visit fewer and having a clinic pharmacy technician was associated with 7.87 (95% CI, 2.03-13.72) minutes per visit fewer. Practicing in a community health center was associated with fewer minutes of total EHR time per visit (5.40 [95% CI, 0.06-10.74] minutes). There is substantial variation in EHR time among individual PCPs and PCPs within clinics. Organization-level factors, such as team collaboration on orders, support for medication refill functions, and practicing in a community health center, are associated with lower EHR time for PCPs. These findings highlight the importance of addressing EHR burden at a systems level.

Changes in Physician Electronic Health Record Use With the Expansion of Telemedicine

Understanding the drivers of electronic health record (EHR) burden, including EHR time and patient messaging, may directly inform strategies to address physician burnout. Given the COVID-19-induced expansion of telemedicine-now used for a substantial proportion of ambulatory encounters-its association with EHR burden should be evaluated. To measure the association of the telemedicine expansion with time spent working in the EHR and with patient messaging among ambulatory physicians before and after the onset of the COVID-19 pandemic. This longitudinal cohort study analyzed weekly EHR metadata of ambulatory physicians at UCSF Health, a large academic medical center. The same EHR measures were compared for 1 year before the COVID-19 pandemic (August 2018-September 2019) with the same period 1 year after its onset (August 2020-September 2021). Multivariable regression models evaluating the association between level of telemedicine use and EHR use were then assessed after the onset of the pandemic. The sample included all physician-weeks with at least 1 scheduled half-day clinic in the 11 largest ambulatory specialties at UCSF Health. Data analyses were performed from March 1, 2022, through July 1, 2023. Physicians' weekly modality mix of either entirely face-to-face visits, mixed modalities, or entirely telemedicine. The EHR time during and outside of patient scheduled hours (PSHs), time spent documenting (normalized per 8 PSHs), and electronic messages sent to and received from patients. The study sample included 1052 physicians (437 [41.5%] men and 615 [58.5%] women) during 115 weeks, which provided 35 697 physician-week observations. Comparing the period before to the period after pandemic onset showed that physician time spent working in the EHR during PSHs increased from 4.53 to 5.46 hours per 8 PSH (difference, 0.93; 95% CI, 0.87-0.98; P < 0.001); outside of PSHs, increased from 4.29 to 5.34 hours (difference, 1.04; 95% CI, 0.95-1.14; P < 0.001); and time documenting during and outside of PSHs increased from 6.35 to 8.18 hours (difference, 1.83; 95% CI, 1.72-1.94; P < 0.001). Mean weekly messages received from patients increased from 16.76 to 30.33, and messages sent to patients increased from 13.82 to 29.83. In multivariable models, weeks with a mix of face-to-face and telemedicine (β, 0.43; 95% CI, 0.31-0.55; P < .001) visits or entirely telemedicine (β, 0.91; 95% CI, 0.74-1.09; P < .001) had more EHR time during PSHs than all face-to-face weeks, with similar results for EHR time outside of PSHs. There was no association between telemedicine use and messages received from patients, whereas mixed modalities (β, -0.90; 95% CI, -1.73 to -0.08; P = .03) and all telemedicine (β, -4.06; 95% CI, -5.19 to -2.93; P < .001) were associated with fewer messages sent to patients compared with entirely face-to-face weeks. The findings of this longitudinal cohort study suggest that telemedicine is associated with greater physician time spent working in the EHR, both during and outside of scheduled hours, mostly documenting visits and not messaging patients. Health systems may need to adjust productivity expectations for physicians and develop strategies to address EHR documentation burden for physicians.

Assessing Electronic Health Record (EHR) Use during a Major EHR Transition: An Innovative Mixed Methods Approach

BackgroundElectronic health record (EHR) transitions are inherently disruptive to healthcare workers who must rapidly learn a new EHR and adapt to altered clinical workflows. Healthcare workers’ perceptions of EHR usability and their EHR use patterns following transitions are poorly understood. The Department of Veterans Affairs (VA) is currently replacing its homegrown EHR with a commercial Cerner EHR, presenting a unique opportunity to examine EHR use trends and usability perceptions.ObjectiveTo assess EHR usability and uptake up to 1-year post-transition at the first VA EHR transition site using a novel longitudinal, mixed methods approach.DesignA concurrent mixed methods strategy using EHR use metrics and qualitative interview data.Participants141 clinicians with data from select EHR use metrics in Cerner Lights On Network®. Interviews with 25 healthcare workers in various clinical and administrative roles.ApproachWe assessed changes in total EHR time, documentation time, and order time per patient post-transition. Interview transcripts (n = 90) were coded and analyzed for content specific to EHR usability.Key ResultsTotal EHR time, documentation time, and order time all decreased precipitously within the first four months after go-live and demonstrated gradual improvements over 12 months. Interview participants expressed ongoing concerns with the EHR’s usability and functionality up to a year after go-live such as tasks taking longer than the old system and inefficiencies related to inadequate training and inherent features of the new system. These sentiments did not seem to reflect the observed improvements in EHR use metrics.ConclusionsThe integration of quantitative and qualitative data yielded a complex picture of EHR usability. Participants described persistent challenges with EHR usability 1 year after go-live contrasting with observed improvements in EHR use metrics. Combining findings across methods can provide a clearer, contextualized understanding of EHR adoption and use patterns during EHR transitions.

Electronic Health Record Usage in an Academic Orthopaedic Sports Medicine Practice

Background While the relationship between electronic health record usage and physician burnout has been investigated in other specialties, no reports exist for orthopaedic surgery. The purpose of this study was to determine the amount of time spent on the electronic health record compared to actual patient interaction in four busy orthopaedic outpatient clinics. Hypothesis We hypothesized that surgeons would spend as much time on the electronic health record as they do interacting with patients on a typical clinic day. Methods Four busy academic orthopaedic surgeons (Surgeon 1, 2, 3, and 4) at a tertiary academic institution were each followed on clinic days to determine their total time spent on Epic Hyperspace electronic health record as compared to face-to-face interactions with patients. A research assistant recorded the amount of time each surgeon spent dictating notes, writing orders, or replying to clinical inbox messages (electronic health record time) and how much time was spent on loading screens or electronic health record buffering (loading time) during which no actions could be completed. Results Surgeon 1 spent an average of 63% of their clinic days using the electronic health record. Surgeon 2 spent 50% of their time on the computer. Surgeon 3 spent 35% of his days on the computer. Surgeon 4 spent 46% of his days on the computer. Conclusion Approximately half of each surgeon’s clinic time was spent using the electronic health record. Given the known influence of electronic health record usage on orthopaedic surgeon burnout, this is an issue that needs to be addressed. The solution likely involves a multifactorial approach, including changes to electronic health records, widespread use of scribes or advanced care teams, work hour limitations, and mindfulness training for residents and faculty. Information technology quality improvements can be made to EHRs that include reducing or eliminating unnecessary information displayed on screen, providing more abundant and accessible EHR training and on-site technical support, and a more streamlined software interface that eliminates unnecessary typing and clicks to improve charting flow and efficiency. Level of Evidence Level IV Descriptive Study

The role of organizations in shaping physician use of electronic health records.

The aim of the study was to (1) characterize organizational differences in primary care physicians' electronic health record (EHR) behavior; (2) assess within-organization consistency in EHR behaviors; and (3) identify whether organizational consistency is associated with physician-level efficiency. EHR metadata capturing averaged weekly measures of EHR time and documentation composition from 75,124 US primary care physicians across 299 organizations between September 2020 and May 2021 were taken. EHR time measures include active time in orders, chart review, notes, messaging, time spent outside of scheduled hours, and total EHR time. Documentation composition measures include note length and percentage use of templated text or copy/paste. Efficiency is measured as the percent of visits with same-day note completion. All analyses are cross-sectional. Across-organization differences in EHR use and documentation composition are presented via 90th-to-10th percentile ratios of means and SDs. Multilevel modeling with post-estimation variance partitioning assesses the extent of an organizational signature-the proportion of variation in our measures attributable to organizations (versus specialty and individual behaviors). We measured organizational internal consistency for each measure via organization-level SD, which we grouped into quartiles for regression. Association between internally consistent (i.e., low SD) organizational EHR use and physician-level efficiency was assessed with multi-variable OLS models. Extraction from Epic's Signal platform used for measuring provider EHR efficiency. EHR time per visit for physicians at a 90th percentile organization is 1.94 times the average EHR time at a 10th percentile organization. There is little evidence, on average, of an organizational signature. However, physicians in organizations with high internal consistency in EHR use demonstrate increased efficiency. Physicians in organizations with the highest internal consistency (top quartile) have a 3.77 percentage point higher same-day visit closure rates compared with peers in bottom quartile organizations (95% confidence interval: 0.0142-0.0612). Results suggest unrealized opportunities for organizations and policymakers to support consistency in how physicians engage in EHR-supported work.

416 On-Call Junior Neurosurgery Residents Spend 9 Hours of Their On-Call Shift Actively Using the Electronic Health Record

INTRODUCTION: The Electronic Health Record (EHR) is central to clinical workflow, yet few studies to date have explored EHR usage patterns among neurosurgery trainees. METHODS: We utilized the EHR audit logs, time-stamped records of user activities, to review EHR usage of PGY-2 and 3 neurosurgery residents scheduled for 1 or more on-call days across 2 calendar years at the University of California San Francisco. We focused on the PGY-2 and 3 years, which in our training program represent the primary participants in the in-house on-call pool. RESULTS: Over 723 call days, 12 different residents took at least one on-call shift. The median (IQR) number of minutes that residents spent per on-call shift actively using the EHR was 536.8 (203.5), while interacting with an average (SD) of 68.1 (14.7) patient charts. There was no significant difference between active EHR time between residents as PGY-2 and PGY-3 on paired t-tests. Residents spent the most time on the following EHR activities: patient reports, notes, order management, patient list, and chart review. CONCLUSIONS: Residents spent, on average, 9 hours of their on-call shift actively using the EHR, and there was no improved efficiency as residents gained experience. We noted several areas of administrative EHR burden which could be reduced.

Electronic Health Record Burden Among Gastroenterology Providers Associated With Subspecialty and Training.

Use of the electronic health record (EHR) has become increasingly widespread. Higher EHR burden is associated with burnout, but this has not been specifically investigated among gastroenterology (GI) providers. We retrospectively collected measures of EHR use for outpatient GI providers during a 6-month period. We compared metrics across provider sex, subspecialty, and training (physicians vs nonphysician providers [NPPs]). Data collected represented more than 16,000 appointments from 41 providers across the Division of Gastroenterology and Hepatology. Inflammatory bowel disease (IBD) and hepatology specialists spent more time per appointment in the EHR, clinical review, and outside regular hours compared with other subspecialists. NPPs spent more EHR time than physicians. IBD and hepatology specialists and NPPs may have disproportionally high EHR burden. More work is needed to understand differences in provider workload to combat burnout.

Trends in Electronic Health Record Capabilities for Tracking Documentation Time

To understand hospitals' access to and use of data from electronic health record (EHR) developers that quantify the amount of time clinicians spend documenting clinical care in EHRs. Descriptive analysis of 4 waves of a nationally representative survey of US nonfederal acute care hospitals from 2017 to 2019 and 2021 (N = 10,662 across years). We identified the share of hospitals that had access to EHR documentation time measures between 2017 and 2021 and how access varied by hospital and EHR characteristics. We then described how EHR data were used among hospitals with access and whether use varied by developer. The share of hospitals with access to EHR documentation time measures increased significantly each year between 2017 and 2021, when more than two-thirds of hospitals reported having access to these measures. Despite hospitals' increased access to measures that track EHR time, lower-resourced hospitals, nonteaching hospitals, and hospitals with non-market-leading EHR developers were less likely to report having access than their counterparts. In 2021, the 2 most common uses of EHR data were "identifying providers in need of training and support" and "identifying areas to improve clinical workflow." The share of hospitals indicating use of EHR data increased between 2019 and 2021 for all studied uses. A higher proportion of hospitals with access to EHR documentation time measures used them for more purposes over time, suggesting their increased value. Although hospitals' access to and use of EHR documentation measures increased significantly in the last 5 years, future research efforts should investigate whether the use of these measures translates into reduced burden for providers.

Health record hiccups-5,526 real-world time series with change points labelled by crowdsourced visual inspection.

Large routinely collected data such as electronic health records (EHRs) are increasingly used in research, but the statistical methods and processes used to check such data for temporal data quality issues have not moved beyond manual, ad hoc production and visual inspection of graphs. With the prospect of EHR data being used for disease surveillance via automated pipelines and public-facing dashboards, automation of data quality checks will become increasingly valuable. We generated 5,526 time series from 8 different EHR datasets and engaged >2,000 citizen-science volunteers to label the locations of all suspicious-looking change points in the resulting graphs. Consensus labels were produced using density-based clustering with noise, with validation conducted using 956 images containing labels produced by an experienced data scientist. Parameter tuning was done against 670 images and performance calculated against 286 images, resulting in a final sensitivity of 80.4% (95% CI, 77.1%-83.3%), specificity of 99.8% (99.7%-99.8%), positive predictive value of 84.5% (81.4%-87.2%), and negative predictive value of 99.7% (99.6%-99.7%). In total, 12,745 change points were found within 3,687 of the time series. This large collection of labelled EHR time series can be used to validate automated methods for change point detection in real-world settings, encouraging the development of methods that can successfully be applied in practice. It is particularly valuable since change point detection methods are typically validated using synthetic data, so their performance in real-world settings cannot be assumed to be comparable. While the dataset focusses on EHRs and data quality, it should also be applicable in other fields.

On-Call Junior Neurosurgery Residents Spend 9 hours of Their On-Call Shift Actively Using the Electronic Health Record.

The electronic health record (EHR) is central to clinical workflow, yet few studies to date have explored EHR usage patterns among neurosurgery trainees. To describe the amount of EHR time spent by postgraduate year (PGY)-2 and PGY-3 neurosurgery residents during on-call days and the distribution of EHR activities in which they engage. This cohort study used the EHR audit logs, time-stamped records of user activities, to review EHR usage of PGY-2 and PGY-3 neurosurgery residents scheduled for 1 or more on-call days across 2 calendar years at the University of California San Francisco. We focused on the PGY-2 and PGY-3, which, in our training program, represent the primary participants in the in-house on-call pool. Over 723 call days, 12 different residents took at least one on-call shift. The median (IQR) number of minutes that residents spent per on-call shift actively using the EHR was 536.8 (203.5), while interacting with an average (SD) of 68.1 (14.7) patient charts. There was no significant difference between Active EHR Time between residents as PGY-2 and PGY-3 on paired t -tests. Residents spent the most time on the following EHR activities: patient reports, notes, order management, patient list, and chart review. Residents spent, on average, 9 hours of their on-call shift actively using the EHR, and there was no improved efficiency as residents gained experience. We noted several areas of administrative EHR burden, which could be reduced.

Physician Note Composition Patterns and Time on the EHR Across Specialty Types: a National, Cross-sectional Study.

The burden of clinical documentation in electronic health records (EHRs) has been associated with physician burnout. Numerous tools (e.g., note templates and dictation services) exist to ease documentation burden, but little evidence exists regarding how physicians use these tools in combination and the degree to which these strategies correlate with reduced time spent on documentation. To characterize EHR note composition strategies, how these strategies differ in time spent on notes and the EHR, and their distribution across specialty types. Secondary analysis of physician-level measures of note composition and EHR use derived from Epic Systems' Signal data warehouse. We used k-means clustering to identify documentation strategies, and ordinary least squares regression to analyze the relationship between documentation strategies and physician time spent in the EHR, on notes, and outside scheduled hours. A total of 215,207 US-based ambulatory physicians using the Epic EHR between September 2020 and May 2021. Percent of note text derived from each of five documentation tools: SmartTools, copy/paste, manual text, NoteWriter, and voice recognition and transcription; average total and after-hours EHR time per visit; average time on notes per visit. Six distinct note composition strategies emerged in cluster analyses. The most common strategy was predominant SmartTools use (n=89,718). In adjusted analyses, physicians using primarily transcription and dictation (n=15,928) spent less time on notes than physicians with predominant Smart Tool use. (b=-1.30, 95% CI=-1.62, -0.99, p<0.001; average 4.8 min per visit), while those using mostly copy/paste (n=23,426) spent more time on notes (b=2.38, 95% CI=1.92, 2.84, p<0.001; average 13.1 min per visit). Physicians' note composition strategies have implications for both time in notes and after-hours EHR use, suggesting that how physicians use EHR-based documentation tools can be a key lever for institutions investing in EHR tools and training to reduce documentation time and alleviate EHR-associated burden.

Documentation dynamics: Note composition, burden, and physician efficiency.

To analyze how physician clinical note length and composition relate to electronic health record (EHR)-based measures of burden and efficiency that have been tied to burnout. Secondary EHR use metadata capturing physician-level measures from 203,728 US-based ambulatory physicians using the Epic Systems EHR between September 2020 and May 2021. In this cross-sectional study, we analyzed physician clinical note length and note composition (e.g., content from manual or templated text). Our primary outcomes were three time-based measures of EHR burden (time writing EHR notes, time in the EHR after-hours, and EHR time on unscheduled days), and one measure of efficiency (percent of visits closed in the same day). We used multivariate regression to estimate the relationship between our outcomes and note length and composition. Physician-week measures of EHR usage were extracted from Epic's Signal platform used for measuring provider EHR efficiency. We calculated physician-level averages for our measures of interest and assigned physicians to overall note length deciles and note composition deciles from six sources, including templated text, manual text, and copy/paste text. Physicians in the top decile of note length demonstrated greater burden and lower efficiency than the median physician, spending 39% more time in the EHR after hours (p< 0.001) and closing 5.6 percentage points fewer visits on the same day (p< 0.001). Copy/paste demonstrated a similar dose/response relationship, with top-decile copy/paste users closing 6.8 percentage points fewer visits on the same day (p< 0.001) and spending more time in the EHR after hours and on days off (both p< 0.001). Templated text (e.g., Epic's SmartTools) demonstrated a non-linear relationship with burden and efficiency, with very low and very high levels of use associated with increased EHR burden and decreased efficiency. "Efficiency tools" like copy/paste and templated text meant to reduce documentation burden and increase provider efficiency may have limited efficacy.

The effect of remote scribes on primary care physicians’ wellness, EHR satisfaction, and EHR use

BackgroundPhysician burnout is a major problem in the United States. Small studies suggest scribes can improve clinician satisfaction, but scribe programs have not been evaluated using separate control groups or structured measures of electronic health record (EHR) use. MethodsWe conducted a pre-post, non-randomized controlled evaluation of a remote scribe pilot program introduced in September 2019 in an academic primary care practice. Scribes were paired with physicians via an audio-only cellphone connection to hear and document in real-time. Physician wellness was measured with the 10-item Mini-Z and 16-item Professional Fulfillment Index. EHR use was measured using vendor-derived platforms that provide routine EHR-related data. Results37 of 38 scribe users (97.4%) and 68 of 160 potential control physicians (42.5%) completed both pre and post intervention questionnaires. Compared with controls, scribe users had improvements in Mini-Z wellness metrics including Joyful Workplace (mean improvement 2.83, 95%CI 0.60, 5.06) and a single-item dichotomized burnout measure (OR 0.15, 95%CI 0.03, 0.71). There were significant reductions among scribe users compared to controls in total EHR time per 8 scheduled hours (−1.14 h, 95%CI -1.55, −0.72), and an increase in the percentage of orders with team contribution (10.4%, 95%CI 5.2, 15.6). These findings remained significant in adjusted analyses. Conclusions/implicationsA remote scribe program was associated with improvements in physician wellness and reduced EHR use. Healthcare organizations can consider scribe programs to help improve wellness among their physician workforce.

Association Between Electronic Health Record Time and Quality of Care Metrics in Primary Care

Physicians across the US spend substantial time working in the electronic health record (EHR), with primary care physicians (PCPs) spending the most time. The association between EHR time and ambulatory care quality outcomes is unclear. To characterize measures of EHR use and ambulatory care quality performance among PCPs. A cross-sectional study of PCPs with longitudinal patient panels using a single EHR vendor was conducted at Brigham and Women's Hospital and Massachusetts General Hospital during calendar year 2021. Independent variables included PCPs demographic and practice characteristics and EHR time measures (PCP-level mean of daily total EHR time, after-hours time, time from 5:30 pm to 7:00 am and time on weekends, and daily EHR time on notes, sending and receiving patient, staff, results, prescription, or system messages [in-basket], and clinical review). Outcome variables were ambulatory quality measures (year-end, PCP panel-level achievement of targets for hemoglobin A1c level control, lipid management, hypertension control, diabetes screening, and breast cancer screening). The sample included 291 physicians (174 [59.8%] women). Median panel size was 829 (IQR, 476-1157) patients and mean (SD) clinical full-time equivalent was 0.54 (0.27). The PCPs spent a mean (SD) of 145.9 (64.6) daily minutes on the EHR. There were significant associations between EHR time and panel-level achievement of hemoglobin A1c control, hypertension control, and breast cancer screening targets. In adjusted analyses, each additional 15 minutes of total daily EHR time was associated with 0.58 (95% CI, 0.32-0.84) percentage point greater panel-level hemoglobin A1c control, 0.52 (95% CI, 0.33-0.71) percentage point greater hypertension control, and 0.28 (95% CI, 0.05-0.52) higher breast cancer screening rates. Each daily additional 15 minutes of in-basket time was associated with 2.26 (95% CI, 1.05-3.48) greater panel-wide hemoglobin A1c control, 1.65 (95% CI, 0.83-2.47) percentage point greater hypertension control, and 1.26 (95% CI, 0.51-2.02) percentage point higher breast cancer screening rates. Associations were largely concentrated among PCPs with 0.5 clinical full-time equivalent or less. There were no associations between EHR use metrics and diabetes screening or lipid management in patients with cardiovascular disease. This cross-sectional study found an association between EHR time and some measures of ambulatory care quality. Although increased EHR time is associated with burnout, it may represent a level of thoroughness or communication that enhances certain outcomes. It may be useful for future studies to characterize payment models, workflows, and technologies that enable high-quality ambulatory care delivery while minimizing EHR burden.

Variation in Support for Documentation Among Primary Care Physicians by Gender.

There are known gender differences in both time spent on the electronic health record (EHR) and burnout. Previous studies have described potential benefits of staff support for documentation for physician experience and EHR time. It is not known, however, to what extent availability of staff support for documentation differs by gender in the context of primary care. METHODS: This was a cross-sectional study of primary care physicians (PCPs) using data from the 2018 and 2019 National Electronic Health Records Survey administrations. After descriptively analyzing the prevalence of staff support for documentation, we used multivariable logistic regression to identify the adjusted relationship of staff support for documentation with gender. RESULTS: Among the 813 physicians who endorsed having an EHR (92.5% of sample, representing 296,854 physicians), female PCPs were significantly less likely than male PCPs (25.1% vs 37.3%; P = .04) to report having staff support for documentation. This difference was most pronounced in practices with a single physician and practices with more than 50% of patients insured by Medicaid. Gender differences persisted in analyses adjusted for practice ownership and percent of patients insured by Medicaid. CONCLUSIONS: Given positive effects of documentation support and known gender differences in burnout and EHR use times, the differences identified have important implications for the physician workforce. Future research should focus on identifying underlying reasons and potential solutions for the gender differences described.

Temporal Associations Between EHR-Derived Workload, Burnout, and Errors: a Prospective Cohort Study.

The temporal progression and workload-related causal contributors to physician burnout are not well-understood. To characterize burnout's time course and evaluate the effect of time-varying workload on burnout and medical errors. Six-month longitudinal cohort study with measurements of burnout, workload, and wrong-patient orders every 4 weeks. Seventy-five intern physicians in internal medicine, pediatrics, and anesthesiology at a large academic medical center. Burnout was measured using the Professional Fulfillment Index survey. Workload was collected from electronic health record (EHR) audit logs and summarized as follows: total time spent on the EHR, after-hours EHR time, patient load, inbox time, chart review time, note-writing time, and number of orders. Wrong-patient orders were assessed using retract-and-reorder events. Seventy-five of 104 interns enrolled (72.1%) in the study. A total of 337 surveys and 8,863,318 EHR-based actions were analyzed. Median burnout score across the cohort across all time points was 1.2 (IQR 0.7-1.7). Individual-level burnout was variable (median monthly change 0.3, IQR 0.1-0.6). In multivariable analysis, increased total EHR time (β=0.121 for an increase from 54.5 h per month (25th percentile) to 123.0 h per month (75th percentile), 95%CI=0.016-0.226), increased patient load (β=0.130 for an increase from 4.9 (25th percentile) to 7.1 (75th percentile) patients per day, 95%CI=0.053-0.207), and increased chart review time (β=0.096 for an increase from 0.39 (25th percentile) to 0.59 (75th percentile) hours per patient per day, 95%CI=0.015-0.177) were associated with an increased burnout score. After adjusting for the total number of ordering sessions, burnout was not statistically associated with an increased rate of wrong-patient orders (rate ratio=1.20, 95%CI=0.76-1.89). Burnout and recovery were associated with recent clinical workload for a cohort of physician trainees, highlighting the elastic nature of burnout. Wellness interventions should focus on strategies to mitigate sustained elevations of work responsibilities.