Building a Medical Education Outcomes Center: Development Study
Bibliographic record
Abstract
BACKGROUND: Medical education outcomes and clinical data exist in multiple unconnected databases, resulting in 3 problems: (1) it is difficult to connect learner outcomes with patient outcomes, (2) learners cannot be easily tracked over time through the education-training-practice continuum, and (3) no standard methodology ensures quality and privacy of the data. OBJECTIVE: The purpose of this study was to develop a Medical Education Outcomes Center (MEOC) to integrate education data and to build a framework to standardize the intake and processing of requests for using these data. METHODS: An inventory of over 100 data sources owned or utilized by the medical school was conducted, and nearly 2 dozen of these data sources have been vetted and integrated into the MEOC. In addition, the American Medical Association (AMA) Physician Masterfile data of the University of Minnesota Medical School (UMMS) graduates were linked to the data from the National Provider Identifier (NPI) registry to develop a mechanism to connect alumni practice data to education data. RESULTS: Over 160 data requests have been fulfilled, culminating in a range of outcomes analyses, including support of accreditation efforts. The MEOC received data on 13,092 UMMS graduates in the AMA Physician Masterfile and could link 10,443 with NPI numbers and began to explore their practice demographics. The technical and operational work to expand the MEOC continues. Next steps are to link the educational data to the clinical practice data through NPI numbers to assess the effectiveness of our medical education programs by the clinical outcomes of our graduates. CONCLUSIONS: The MEOC provides a replicable framework to allow other schools to more effectively operate their programs and drive innovation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.054 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".