Survey evaluating physicians' experiences with an initiative aimed at improving their EHR use
Bibliographic record
Abstract
In this case report, we describe an initiative to improve physicians’ experience with Electronic Health Records (EHRs), which was one of several strategies developed within our hospital to reduce physician burnout possibly connected to the EHR. We propose a 10 member “EHR SWAT Team” with representation from clinical, educational, technical and project management staff. This team met with physicians across all hospital divisions to collect their issues with the EHR and requests for functionality changes. The team then reviewed these requests, prioritized and fixed them in a timely manner. Through in-person meetings, the EHR SWAT Team gathered 118 issues or change requests, 36.4% of which were related to education and 17% of which were quick fixes. Popular requests included improved search functionality, auto-faxing and auto-saving of notes. 46 physicians completed our short evaluation survey, where 61.3% said that it increased their proficiency in using the EHR. We highlight five important lessons learned from carrying out this initiative including the importance of engaging Information Technology (IT) leadership, physician leadership, and physicians across the hospital. Our next steps include measuring the impact of this initiative on EHR-related burnout through an organizational wide survey and objective back-end usage logs.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| 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 | 0.002 |
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".