Training medical students and residents in the use of electronic health records: a systematic review of the literature
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it