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 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 | 0.028 | 0.149 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".