Afterhours usage of the electronic health record among medical and surgical specialists after implementation of a system wide integrated clinical information system in Alberta, Canada: A longitudinal descriptive study (Preprint)
Bibliographic record
Abstract
BACKGROUND Studies suggest the introduction of electronic health records (EHRs) has decreased efficiency of clinical practice and increased clinician workload for United States (US)-based physicians. Less is known about other healthcare settings, nor whether markers of efficiency and workload change over time. OBJECTIVE This study reports on afterhours EHR use [pajama time and time outside scheduled hours (TOSH)] among diverse specialists and tracks these parameters longitudinally. METHODS A longitudinal descriptive study of medical and surgical specialists followed from the introduction of a system-wide EHR in 2019 to 2022 at a large quaternary teaching hospital in Edmonton, Canada. Pajama Time and TOSH were extracted from the EHR on an Epic System platform, and monitored over time. Clinicians were stratified according to clinical group (medical and surgical) and workload (clinical full-time equivalent). RESULTS Seventy-one medical and surgical specialists participated in this study, spending anywhere from 24 to 40 minutes per day on pajama time and 32 to 55 minutes per day on TOSH depending on clinician grouping. Both pajama time and TOSH increased significantly over the 22- and 33-month observation periods for both variables, respectively. CONCLUSIONS Afterhours EHR use in this Canadian cohort of medical and surgical specialists is similar to what is reported in US literature, though the drivers are likely to be different. Perhaps surprisingly, these markers increased over time despite presumed improved familiarity with the EHR. The extent to which this affects clinician wellbeing and work-life integration cannot be determined from these results though there may be cause for concern.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".