Using Real Electronic Health Records in Undergraduate Education: Roundtable Discussion
Bibliographic record
Abstract
Background: Simulated electronic health records (EHRs) are used in structured teaching for health care students. This partly addresses inconsistent student exposure to EHRs while on clinical placements. However, simulated records are poor replacements for the complexity of data encountered in real EHRs. While routinely collected health care data are often used for research, secondary use does not include education. We are exploring the perceptions, governance, and ethics required to support the use of real patient records within teaching. Objective: The aim of the study is to explore the perspectives of health care professionals regarding the use of real patient records to deliver interprofessional EHR education to undergraduate health care students. Methods: We held 90-minute group discussions with 10 health care professionals from nursing, pharmacy, medicine, and allied health disciplines. We used the GRIPP2 (Guidance for Reporting Involvement of Patients and the Public 2) checklist for reporting Patient and Public Involvement and Engagement to present our reflections. Results: There was consensus on the need to upskill health care students in the use of EHRs. Participants emphasized teaching general EHR competencies and transferable skills to overcome the diversity in EHR systems. They highlighted limitations in current teaching due to accessibility issues, disparities within clinical teaching, and curricular gaps on important topics such as clinical documentation and coding. Highlighted benefits of using real EHRs in teaching included learning from the complexities and inaccuracies of real patient data, grasping real-world time frames, and better appreciation of multidisciplinary interactions. Concerns included exposing individual clinicians to unfounded scrutiny and the potential consequences of incidental findings within EHRs. The ethical implications of overlooking perceived errors within EHRs versus the impracticality of acting on them were discussed. To mitigate concerns, it was suggested that data donors would provide informed consent ensuring they understand that they will not be recontacted should any such errors be found. Conclusions: Innovative solutions are needed to realign health care education with clinical practice in rapidly evolving digital environments. Real patient records are optimal for teaching students to handle complex and abundant real-world data. Data within EHRs represent a wealth of clinical knowledge encompassing professional and personal experiences spanning the lifetimes of patients and their caregivers. Drawing experiences and events from real EHRs will prepare health care students to anticipate, confront, and manage real patients in a variety of real-life scenarios. Our reflections highlight the processes and safeguards to consider when using real patient records to deliver EHR education to health care students. These detailed reflections from discussions with health care professionals provide the grounds for a robust framework, with appropriate governance and consent in place to use real health data in training to support preparation for clinical practice.
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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 | 0.071 | 0.080 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.023 | 0.005 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.007 | 0.029 |
| Research integrity | 0.015 | 0.022 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".