Bringing Electronic Patient Records into Health Professional Education: Towards an Integrative Framework
Bibliographic record
Abstract
In this paper we discuss our approach for integrating electronic patient records into health professional education. Electronic patient record (EPR) use is increasing globally. The EPR is considered the cornerstone of the modernization and streamlining of healthcare worldwide. However, despite the importance of the EPR, health professional education in much of the world provides health professional students (who will become the practicing health professionals of the future) with limited access or knowledge about the EPR. New ways of exposing students to EPRs will be needed in order to ensure that health professionals will adopt and use this complex technology wisely and effect the positive benefits EPRs are expected to bring to healthcare globally. In this paper we describe: (a) a framework we have developed for integrating EPRs into health professional education and (b) an innovative Web portal, known as the University of Victoria Electronic Health Record (EHR) Educational Portal (which houses a number of EPRs) that can be used to explore the integration of EPRs in health professional education. It is hoped that adoption and use of EPRs will ultimately be improved through the use of the portal to allow students virtual and ubiquitous access to example EPRs, coupled with principled educational approaches for integrating EPR technology into health professional curricula.
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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 | 0.053 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.008 | 0.032 |
| Scholarly communication | 0.027 | 0.031 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".