Epic overhaul at a Canadian hospital: Pre-Post evaluation insights from physicians and medical residents
Bibliographic record
Abstract
The implementation of Electronic Medical Records (EMRs) in hospitals offers potential benefits but often disrupts clinician workflows, affecting care delivery and outcomes. This study evaluates physicians' and medical residents’ perspectives on the impacts of introducing a new Epic system at a Canadian academic hospital. A pre-post evaluation design was conducted using physician and resident surveys before (T0) and 4- and 9-months post-implementation (T1, T2) that assessed technology use, satisfaction with training and system use, and EMR's perceived impact on care delivery, work practices and quality. Satisfaction with training and system use declined for both groups in the first four months (more sharply for residents) but several measures improved at T2 as users readjusted to the system. There was a significant increase in physicians’ daily computer use (4 h at T0 to 6 h at T1; P < . 001 ). Limited early benefits of the Epic system were observed and a decline in perceived improvement in clinical documentation (P = . 006 and .0012) , order entry (P = . 018 and .002) and patient safety (P = . 044 and .024) were reported at T1 for physicians and residents, respectively. Although some medical practice/work indicators improved by 9 months for physicians, the changes were not statistically significant; these benefits were not observed for residents at T2. Medical training was not significantly affected by the new Epic system either immediately or later post implementation. At T1, 83% of physicians reported that the new system sometimes or often improved the quality of care, as opposed to only 33% of residents; no significant improvements were noted at 9 months post implementation by both groups. Physicians and residents adapt differently to Epic and full system assimilation does not happen in one year. Early perceptions of Epic do not reflect its long-term potential, and meaningful benefits require prolonged stabilization periods for user satisfaction and efficiency gains. We caution hospital leaders not to rely heavily on a vendor-driven implementation, and recommend tailored training, rapid-cycle improvements, transparent communication, and monitoring of agreed-upon performance indicators to strengthen clinician engagement and support long-term success. • Epic implementation improved aspects of clinician workflows, but no quality-of-care impacts were reported up to 9 months. • Training satisfaction and system usability declined early with partial recovery by 9 months, underscoring adaptation challenges until stabilization. • Physicians reported the new system supported their work procedures as early as 4 months post-Epic implementation. • Epic mainly affected clinical documentation, orders entry, patient safety and flow. • Clinicians' adaptation varied, stressing the need for tailored training, rapid-cylce adjustments, and clear performance metrics.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".