EHR “SWAT” teams: a physician engagement initiative to improve Electronic Health Record (EHR) experiences and mitigate possible causes of EHR-related burnout
Bibliographic record
Abstract
This case report describes an initiative implemented to improve physicians' experience with Electronic Health Records (EHRs), and is one of several strategies within our organization developed to reduce physician burnout attributed to the EHR. The EHR SWAT Team-a 10-member team-with interdisciplinary representation from clinical informatics, pharmacy informatics, health information management, clinical applications, and project management, is a direct feedback channel for all physicians to express their EHR challenges and have their requests reviewed, prioritized, and fixed in a timely manner. Through in-person divisional meetings, we gathered 118 requests, 36.4% of which were related to re-education and 17% of which were quick fixes. Popular requests included keyword search functionality, minimizing freezing, auto-faxing and auto-save. Our brief evaluation of 46 physicians demonstrated that physicians were satisfied with the initiative, with 61.3% physicians reporting that it increased their proficiency in using EHR functionalities. Lessons learned from this initiative include the importance of buy-in from Information Technology (IT) and physician leadership, extensive physician engagement, and leveraging project management techniques for coordination. Next steps include measuring the impact of this SWAT initiative on EHR-related burnout through a post-intervention 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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".