Training medical students and residents in the use of electronic health records: a systematic review of the literature
Bibliographic record
Abstract
OBJECTIVE: Our objectives were to identify educational interventions designed to equip medical students or residents with knowledge or skills related to various uses of electronic health records (EHRs), summarize and synthesize the results of formal evaluations of these initiatives, and compare the aims of these initiatives with the prescribed EHR-specific competencies for undergraduate and postgraduate medical education. MATERIALS AND METHODS: We conducted a systematic review of the literature following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta Analyses) guidelines. We searched for English-language, peer-reviewed studies across 6 databases using a combination of Medical Subject Headings and keywords. We summarized the quantitative and qualitative results of included studies and rated studies according to the Best Evidence in Medical Education system. RESULTS: Our search yielded 619 citations, of which 11 studies were included. Seven studies involved medical students, 3 studies involved residents, and 1 study involved both groups. All interventions used a practical component involving entering information into a simulated or prototypical EHR. None of the interventions involved extracting, aggregating, or visualizing clinical data for panels of patients or specific populations. DISCUSSION: This review reveals few high-quality initiatives focused on training learners to engage with EHRs for both individual patient care and population health improvement. In comparing these interventions with the broad set of electronic records competencies expected of matriculating physicians, critical gaps in undergraduate and postgraduate medical education remain. CONCLUSIONS: With the increasing adoption of EHRs and rise of competency-based medical education, educators should address the gaps in the training of future physicians to better prepare them to provide high quality care for their patients and communities.
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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 | 0.020 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".