Physician stress and burnout: the impact of health information technology
Bibliographic record
Abstract
Objective: To quantify how stress related to use of health information technology (HIT) predicts burnout among physicians. Methods: All 4197 practicing physicians in Rhode Island were surveyed in 2017 on their HIT use. Our main outcome was self-reported burnout. The presence of HIT-related stress was defined by report of at least 1 of the following: poor/marginal time for documentation, moderately high/excessive time spent on the electronic health record (EHR) at home, and agreement that using an EHR adds to daily frustration. We used logistic regression to assess the association between each HIT-related stress measure and burnout, adjusting for respondent demographics, practice characteristics, and the other stress measures. Results: Of the 1792 physician respondents (43% response rate), 26% reported burnout. Among EHR users (91%), 70% reported HIT-related stress, with the highest prevalence in primary care-oriented specialties. After adjustment, physicians reporting poor/marginal time for documentation had 2.8 times the odds of burnout (95% CI: 2.0-4.1; P < .0001), compared to those reporting sufficient time. Physicians reporting moderately high/excessive time on EHRs at home had 1.9 times the odds of burnout (95% CI: 1.4-2.8; P < .0001), compared to those with minimal/no EHR use at home. Those who agreed that EHRs add to their daily frustration had 2.4 times the odds of burnout (95% CI: 1.6-3.7; P < .0001), compared to those who disagreed. Conclusion: HIT-related stress is measurable, common (about 70% among respondents), specialty-related, and independently predictive of burnout symptoms. Identifying HIT-specific factors associated with burnout may guide healthcare organizations seeking to measure and remediate burnout among their physicians and staff.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".