Medical education in an electronic health record-mediated world
Bibliographic record
Abstract
This paper reflects on the extent to which we are preparing learners for practice in an electronic health record (EHR)-mediated world. We are currently training the last generation to remember a world without the Internet and the first who will practice in a largely EHR-mediated practice environment. We undertook a thematic review of the literature connecting medical education with e-health using the concepts of 'electronic health record' or 'electronic medical record' as a proxy for the broader notion of e-health. Our findings are more equivocal and cautious than earlier commentators might have expected and while there are examples of good practice and successful integration, the majority of articles we reviewed raised issues and problems with the current links between EHRs and medical education. Medical professionals in particular are quite ambivalent about many of the changes brought about by EHRs, and in the absence of changes in perception and practice it is likely that the connections between medical education and e-health will continue to be problematic. We hope that this paper will lead to an improved understanding of these problems and will serve to advance the discourse on how medical education should engage with the world of e-health and the world of e-health with medical education.
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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 | 0.019 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.017 | 0.018 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".