The Influence of Electronic Health Record Use on Physician Burnout: Cross-Sectional Survey
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Physician burnout has a direct impact on the delivery of high-quality health care, with health information technology tools such as electronic health records (EHRs) adding to the burden of practice inefficiencies. OBJECTIVE: The aim of this study was to determine the extent of burnout among physicians and learners (residents and fellows); identify significant EHR-related contributors of physician burnout; and explore the differences between physicians and learners with regard to EHR-related factors such as time spent in EHR, documentation styles, proficiency, training, and perceived usefulness. In addition, the study aimed to address gaps in the EHR-related burnout research methodologies by determining physicians' patterns of EHR use through usage logs. METHODS: This study used a cross-sectional survey methodology and a review of administrative data for back-end log measures of survey respondents' EHR use, which was conducted at a large Canadian academic mental health hospital. Chi-square and Fisher exact tests were used to examine the association of EHR-related factors with general physician burnout. The survey was sent out to 474 individuals between May and June 2019, including physicians (n=407), residents (n=53), and fellows (n=14), and we measured physician burnout and perceptions of EHR stressors (along with demographic and practice characteristics). RESULTS: Our survey included 208 respondents, including physicians (n=176) and learners (n=32). The response rate was 43.2% for physicians (full-time: 156/208, 75.0%; part-time: 20/199, 10.1%), and 48% (32/67) for learners. A total of 25.6% (45/176) of practicing physicians and 19% (6/32) of learners reported having one or more symptoms of burnout, and 74.5% (155/208) of all respondents who reported burnout symptoms identified the EHR as a contributor. Lower satisfaction and higher frustration with the EHRs were significantly associated with perceptions of EHR contributing toward burnout. Physicians' and learners' experiences with the EHR, gathered through open-ended survey responses, identified challenges around the intuitiveness and usability of the technology as well as workflow issues. Metrics gathered from back-end usage logs demonstrated a 13.6-min overestimation in time spent on EHRs per patient and a 5.63-hour overestimation of after-hours EHR time, when compared with self-reported survey data. CONCLUSIONS: This study suggests that the use of EHRs is a perceived contributor to physician burnout. There should be a focus on combating physician burnout by reducing the unnecessary administrative burdens of EHRs through efficient implementation of systems and effective postimplementation strategies.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.047 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.010 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it