Using electronic clinical practice audits as needs assessment to produce effective continuing medical education programming
Bibliographic record
Abstract
BACKGROUND: The traditional needs assessment used in developing continuing medical education programs typically relies on surveying physicians and tends to only capture perceived learning needs. Instead, using tools available in electronic medical record systems to perform a clinical audit on a physician's practice highlights physician-specific practice patterns. AIM: The purpose of this study was to test the feasibility of implementing an electronic clinical audit needs assessment process for family physicians in Canada. METHOD: A clinical audit of 10 preventative care interventions and 10 chronic disease interventions was performed on family physician practices in Alberta, Canada. The physicians used the results from the audit to produce personalized learning needs, which were then translated into educational programming. RESULTS: A total of 26 family practices and 4489 patient records were audited. Documented completion rates for interventions ranged from 13% for ensuring a patient's tetanus vaccine is current to 97% of pregnant patients receiving the recommended prenatal vitamins. CONCLUSIONS: Electronic medical record-based needs assessments may provide a better basis for developing continuing medical education than a more traditional survey-based needs assessment. This electronic needs assessment uses the physician's own patient outcome information to assist in determining learning objectives that reflect both perceived and unperceived needs.
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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 | 0.063 | 0.175 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".