Background: Clinical practice guidelines are not uniformly successful in influencing clinicians' behaviour toward best practices. Implementability refers to a set of characteristics that predict ease of (and obstacles to) guideline implementation. Our objective is to develop and validate a tool for appraisal of implementability of clinical guidelines.

were grouped into primary care, medical, and surgical specialties (eTable 2 in the Supplement).Variables were compared before (March 2018 to February 2020) and during (March 2020 to June 2021) the COVID-19 pandemic. To assess whether the onset of the pandemic was an inflection point in PMARs, message volume per physician per day was modeled by piecewise linear regression using a spline for month with a single knot at March 2020 and Huber-White SEs. Three months of inbox message data were missing (3 of 40 months [7.5%]) and excluded from the analysis. To test for significance (P < .05), we used a 2-sided Wald test for equivalence of the coefficients. We used Stata statistical software version 16 (StataCorp) for data analyses. ResultsForty months of inbox messages were analyzed, including 10 850 401 messages to 419 unique physicians from 38 specialties across 141 practice sites (Figure). Overall, primary care, medical, and surgical physicians received 49.3, 33.4, and 20.7 messages per day, respectively. Between March 2020 and June 2021, mean (SD) total messages per day increased from 45.0 (27.4) to 46.0 (27.4) messages per day for primary care physicians, from 29.3 (20.7) to 32.0 (20.8) messages per day for medical physicians, and from 16.6 (11.9) to 23.3 (17.9) messages per day for surgical physicians.Patient-originated messages also increased, including PMARs (from a mean [SD] of 1.8 [1.8] to 3.9 [3.2] messages per day for primary care physicians; from 1.0 [1.7] to 2.2 [2.9] messages per day for medical physicians; and from 0.4 [0.5] to 1.1 [1.3] messages per day for surgical physicians), patient calls, and time in the inbox (from 21.7 [12.7] to 25.1 [13.7] minutes per day for primary care physicians;

The authors found a difference in satisfaction between internal medicine and pediatrics users of an EHR. Although many potential factors that influence satisfaction were similar between subjects in the two specialties, differences in previous experience may have influenced the results. Medicine residents had more previous experience with a different EHR implementation, which they may have perceived as superior to the one involved in this study. Pediatric residents had more previous experience with structured data entry prior to EHR implementation and more preventive care patient encounters for which structured data entry may be well suited. Since successful implementations generally require satisfied users, understanding what factors affect satisfaction can improve chances of a system's success.

IMPORTANCEPhysician turnover takes a heavy toll on patients, physicians, and health care organizations. Survey research has established associations of electronic health record (EHR) use with professional burnout and reduction in professional effort, but these findings are subject to response fatigue and bias. OBJECTIVE To evaluate the association of physician productivity and EHR use patterns, as determined by vendor-derived EHR use data platforms, with physician turnover. DESIGN, SETTING, AND PARTICIPANTS This retrospective cohort study was conducted among nonteaching ambulatory physicians at a large ambulatory practice network based in New England. Data were collected from March 2018 to February 2020. MAIN OUTCOMES AND MEASURES Physician departure from the practice network; 4 time-based core measures of EHR use, normalized to 8 hours of scheduled clinical time; teamwork, percentage of a physician's orders that are placed by other members of the care team; and productivity measures of patient volume, intensity, and demand. RESULTSAmong 335 physicians assessed for eligibility, 314 unique physicians (89.2%) were included in the analysis (123 [39%] women; 100 [32%] aged 45-54 years), with 5663 physicianmonths of data. The turnover rate was 5.1%/year (32 of 314 physicians). Physicians completed a mean 2.6 appointments/hour (95% CI, 2.5-2.6 appointments/hour) and 206 appointments/month (95% CI, 197-215 appointments/month) with 5.5 hours (95% CI, 5.3-5.8 hours) of EHR time for every 8 hours of scheduled patient time. After controlling for gender, medical specialty, and time, the following variables were associated with turnover: inbox time (odds ratio [OR], 0.70; 95% CI, 0.61-0.82; P < .001), teamwork (OR, 0.68; 95% CI, 0.52-0.87; P = .003), demand (ie, proportion of available appointments filled: OR, 0.49; 95% CI, 0.35-0.70; P < .001), and age 45 to 54 years vs 25 to 34 years (OR, 0.19; 95% CI, 0.04-0.93; P = .04). CONCLUSIONS AND RELEVANCEIn this study, physician productivity and EHR use metrics were associated with physician departure. Prospectively tracking these metrics could identify physicians at high risk of departure who would benefit from early, team-based, targeted interventions. The counterintuitive finding that less time spent on the EHR (in particular inbox management) was associated with physician departure warrants further investigation.

Background Increasing numbers of transgender and nonbinary (TNB) people seek careers in medicine, but little is known about their experiences and the effect of their gender identity during residency application. Objective This project sought to evaluate the experiences and needs of TNB individuals during the residency application and Match process in order to inform the practice of residency programs. Methods An online survey was distributed in 2019 via social media, professional groups, and snowball sampling to TNB persons, who were current residents or recent graduates (within the past 3 years) of a US residency program. Results Twenty-six eligible respondents from 10 medical specialties completed the survey. Eighteen (69.2%) respondents felt unsafe disclosing their gender identity or discussing it during interviews some or all of the time due to fear of discrimination and how it might affect their match; 26.9% (7 of 26) felt they were ranked lower than their qualifications due to their gender identity. Eleven (42.3%) were misnamed or misgendered some or all of the time during interviews through use of incorrect name and pronouns. Respondents' recommendations for programs included: (1) adopt gender affirming practices; (2) offer gender affirming health benefits; (3) advertise nondiscrimination policies; (4) understand experiences of discrimination during medical training; and (5) value resident gender diversity. Conclusions TNB residents and recent graduates perceived gender identity discrimination during residency application, including feeling unsafe to disclose their gender identity and being misnamed or misgendered. Suggestions for programs to improve the experience of TNB applicants are included.

Background Electronic health records (EHRs) have become ubiquitous in US office-based physician practices. However, the different ways in which users engage with EHRs remain poorly characterized. Objective The aim of this study is to explore EHR use phenotypes among ambulatory care physicians. Methods In this retrospective cohort analysis, we applied affinity propagation, an unsupervised clustering machine learning technique, to identify EHR user types among primary care physicians. Results We identified 4 distinct phenotype clusters generalized across internal medicine, family medicine, and pediatrics specialties. Total EHR use varied for physicians in 2 clusters with above-average ratios of work outside of scheduled hours. This finding suggested that one cluster of physicians may have worked outside of scheduled hours out of necessity, whereas the other preferred ad hoc work hours. The two remaining clusters represented physicians with below-average EHR time and physicians who spend the largest proportion of their EHR time on documentation. Conclusions These findings demonstrate the utility of cluster analysis for exploring EHR use phenotypes and may offer opportunities for interventions to improve interface design to better support users’ needs.

Forms have traditionally been employed to record and organize data. Here we show how principles of usability engineering can be used to create a form to meet the needs of users and even encourage evidence-based practice.

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