Electronic Health Record (EHR) Training in Undergraduate Medical Education: Theory-Informed Development of a Longitudinal Curriculum for Empowering Patient- and Relationship-Centered Care in the Computerized Setting
Bibliographic record
Abstract
Objectives: While electronic health record (EHR) use is becoming state-of-the-art, formal teaching of Health Care Information Technology (HCIT) competencies is not keeping pace with burgeoning use. Medical students require training to become skilled users of HCIT but formal pedagogy is sparse. Fundamental challenges include preserving and fostering effective health care provider-patient communication skills in the computerized setting to preserve patient and relationship-centered care and facilitate reciprocity within whole person care. Thus, curriculum innovation with overarching goal of empowering undergraduate medical students’ patient- and relationship-centered interviewing skills, information mastery, electronic documentation skills, and HCIT-supported patient education is needed.Methods: The authors describe innovative, systematic curriculum development for EHR training within a series of clinical skills courses at their institution, informed by Kern et al.’s framework, narrative medicine, and reflective practice. Initially, a didactic and an observed standardized patient encounter were piloted in Year 3. Subsequent surveys of participating faculty both validated the session’s educational value and identified the need for additional practice opportunities.Results: In addition to the existing presentation and individualized practice, second iteration revisions include reflective readings and exercises, relevant “introductory” skills presented in grid format, and opportunities for direct observation of and by mentor physicians in clinical settings. The behavior grid was then expanded to include “advanced” Year 4 skills, i.e. patient participation for chart building, patient education/information sharing, shared decision-making, and sending information to the interprofessional health care team.Conclusions: Effective triangulation of physician-patient-computer may be optimized with medical education curriculum developing competencies of effective EHR use preserving patient-and relationship-centered care, reflection, and narrative medicine. Systematic, longitudinal monitoring of learners' skill development by faculty, standardized patient, self-assessment, and reflective writing will inform our innovative multi-faceted, longitudinal, transferable curriculum presented herein. Further research is needed on formal pedagogy for EHR use by learners.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".