Resident Duty Hours in the Outpatient Electronic Health Record Era: Inaccuracies and Implications
Bibliographic record
Abstract
BACKGROUND: The Accreditation Council for Graduate Medical Education expects resident duty hours to be monitored, yet no previous studies have examined the effect of after-hours electronic health record (EHR) use on resident hours or burnout. OBJECTIVE: We assessed internal medicine residents' perceived and actual time spent on after-hours outpatient EHR use and calculated increased duty hours if after-hours EHR use were included; we also assessed its effect on resident burnout. METHODS: We retrospectively aggregated time spent logged on to the outpatient EHR for residents in a general internal medicine clinic for 13 weeks in 2011. Residents completed a survey on EHR use, which was correlated with objectively recorded data on EHR usage. We compared actual and self-reported EHR time and identified violations that would be generated if these hours were included in reported duty hours. We also correlated resident after-hours EHR use with responses to an internally developed burnout survey. RESULTS: The 44 residents in this study overestimated time spent on the ambulatory EHR (they spent 3.03 hours/week on after-hours use compared with a recorded 1.20 hours/week). In total, 190 duty hour violations (mean duration of violation = 37 minutes) would have been generated if after-hours EHR usage were included in residents' reported duty hours. CONCLUSIONS: Resident estimates of EHR use by residents were not accurate; including after-hours EHR use would increase the number of reported duty hour violations. There was no association between after-hours EHR use and resident burnout.
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How this classification was reachedexpand
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".