Using Real Electronic Health Records in Undergraduate Education: Roundtable Discussion
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,071 | 0,080 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,023 | 0,005 |
| Communication savante | 0,010 | 0,012 |
| Science ouverte | 0,007 | 0,029 |
| Intégrité de la recherche | 0,015 | 0,022 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,018 | 0,003 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».