A workshop to improve workflow efficiency in emergency medicine.
Bibliographic record
Abstract
OBJECTIVE: The emergency department (ED) environment requires physicians to focus on workflow efficiency (WFE) and manage ED throughput. We sought to determine whether an interactive workshop could be designed and favourably perceived by emergency physicians and residents as a means to improve their self-assessed WFE skills. METHODS: The authors designed a 4-station workshop to simulate key components of ED throughput. These included resource management in 1) acute care, 2) minor care, 3) charting and 4) communication skills and patient sign-overs. Anonymous surveys were completed after each workshop using 5-point Likert scales and qualitative responses. Qualitative data encompassed participants' past WFE training experiences and perspectives on the current workshop. Data were analyzed using descriptive statistics. The workshops were administered on 2 separate occasions to different groups of physicians. The first occasion was primarily for residents and the second session was only for practising physicians. RESULTS: A total of 22 residents and 24 practising physicians participated. Evaluations were completed by 45 of 46 participants. Ratings of "definitely helpful" or "helpful" as noted for each station were received by 37 of 44 respondents for the sign-over and communication station, by 37 of 44 for the minor care station, by 41 of 44 for the acute care station and by 33 of 43 for the effective charting station. Among all participants, 42 of 45 reported that they felt the overall workshop experience was "helpful" or "definitely helpful." CONCLUSION: ED management "flow skills" are valued yet undertaught. A flow workshop designed to improve self-perceived WFE skills yields positive evaluations. Teaching this competency in a workshop setting is both feasible and appreciated by participants. Similar efforts should be considered for inclusion in both graduate and continuing medical education curricula.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".