Online virtual cases to teach resource stewardship
Bibliographic record
Abstract
BACKGROUND: As health care costs rise, medical education must focus on high-value clinical decision making. To teach and assess efficient resource use in rheumatology, online virtual interactive cases (VICs) were developed to simulate real patient encounters to increase price transparency and reinforce cost consciousness. To teach and assess efficient resource use in rheumatology, online virtual interactive cases (VICs) were developed METHODS: The VIC modules were distributed to a sample of medical students and internal medicine residents, who were required to assess patients, order appropriate investigations, develop differential diagnoses and formulate management plans. Each action was associated with a time and price, with the totals compared against ideals. Trainees were evaluated not only on their diagnosis and patient management, but also on the total time, cost and value of their selected workup. Trainee responses were tracked anonymously, with opportunity to provide feedback at the end of each case. RESULTS: Seventeen medical trainees completed a total of 48 VIC modules. On average, trainees spent CAN $227.52 and 68 virtual minutes on each case, which was lower than expected. This may have been the result of a low management score of 52.4%, although on average 92.0% of participants in each case achieved the correct diagnosis. In addition, 85.7% felt more comfortable working up similar cases, and 57.1% believed that the modules increased their ability to appropriately order cost-conscious rheumatology investigations. DISCUSSION: Our initial assessment of the VIC rheumatology modules was positive, supporting their role as an effective tool in teaching an approach to rheumatology patients, with an emphasis on resource stewardship. Future directions include the expansion of cases, based on feedback, wider dissemination and an evaluation of learning retention.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.029 | 0.006 |
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".