Cluster Analysis of Primary Care Physician Phenotypes for Electronic Health Record Use: Retrospective Cohort Study.

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.

Analysis of Electronic Health Record Use and Clinical Productivity and Their Association With Physician Turnover.

Importance: Physician 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. Results: Among 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 physician-months 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 Relevance: In 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.

Take note(s): differential EHR satisfaction with two implementations under one roof.

OBJECTIVE: The aim of this study was to rigorously evaluate perceived differences in satisfaction with an electronic health record (EHR) between residents of two medical specialties who share the same health record, practice location, administration, and information technology support. DESIGN: A cross-sectional survey was used comparing user satisfaction between pediatrics residents and internal medicine residents in an academic practice. MEASUREMENTS: The survey was designed to measure baseline demographic characteristics, attitudes toward computers, general satisfaction with an EHR, and perceived practicality of use, variation from familiar practice, organizational support, and impact on delivery of care. RESULTS: Medicine subjects were similar to pediatrics subjects in baseline demographic characteristics. Satisfaction with the EHR implementation was very high for both sets of subjects, but internal medicine residents were significantly less likely to be satisfied with the EHR implementation (relative risk [RR]=0.84, 95% confidence interval [CI]=0.73-0.98) and considerably less likely to believe that their colleagues were satisfied with it (RR=0.56, 95% CI=0.41-0.77). The only surveyed characteristic independently predicting satisfaction was medical specialty (p=0.04). Medicine subjects were less likely to believe template-based documentation improved their efficiency (RR=0.64, 95% CI=0.46-0.88). They were significantly more likely to believe the system had been designed to improve billing (RR=1.50, 95% CI=1.05-2.04) and not to improve patient care (RR=0.61, 95% CI=0.44-0.85). CONCLUSION: 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.