Large Language Model–Based Patient Simulation to Foster Communication Skills in Health Care Professionals: User-Centered Development and Usability Study
Notice bibliographique
Résumé
BACKGROUND: Case-based learning using standardized patients is a key method for teaching communication skills in medicine. Besides logistical and financial hurdles, standardized patients portrayed by actors cannot cover the complete diversity of sociodemographic factors of patients. Large language models (LLMs) show promise for creating scalable patient simulations and could probably cover a broader diversity of factors. They could also be integrated into the continuous training of future health care professionals' communication and interaction skills. OBJECTIVE: This study aimed to introduce the system architecture of a digital tool that leverages LLMs to simulate patient conversations for medical education, focusing specifically on medical history taking. Through an explorative analysis, we aimed to assess the tool's usability and examine differences between LLMs in simulating patient encounters. METHODS: We followed a user-centered design process, gathering initial requirements from 2 medical students. We then developed a fully functional web prototype using a Python Flask backend and a PostgreSQL database, integrating 5 LLMs from OpenAI, Anthropic, and xAI. The system includes an artificial intelligence-assisted case vignette generator and a dynamic patient simulator. For the explorative analysis of the prototype, we conducted a task-based usability test with 5 medical students, measuring their experience using the System Usability Scale (SUS) questionnaire and qualitative questions. We then conducted an explorative analysis in which 4 practicing physicians evaluated the simulation quality of 3 models (Grok 3, GPT-4, and Claude 3 Opus) across 7 criteria on a 5-point Likert scale. RESULTS: Usability testing yielded a mean SUS score of 91.5 (SD 8.40), indicating high perceived usability in a small formative sample. Students praised the system's simplicity and intuitive design but noted the absence of a formal conclusion and performance feedback, expressing a desire for a "didactic loop" to maximize learning. The models showed limitations in simulating uncertainties and memory lapses, responding to follow-up questions, and producing natural conversational flow. They perform well in simulating a coherent symptom profile, in using patient-like language, and in describing a realistic timeline and symptom progression. The differences among the models were not statistically significant. Ratings showed limited discriminative reliability (Kendall W=0-0.19, ie, very low) and a ceiling effect, with most scores clustered at 4-5, constraining interpretation; all group differences should therefore be viewed as exploratory. CONCLUSIONS: We successfully developed a highly usable patient simulation tool that serves as a foundation for further development. Our results show that while the tool could be effective for communication training, its full potential will only be realized by integrating an automated feedback mechanism to create a complete didactic loop, as requested by the test users. Future work should assess in more depth the differences among the models in simulating psychosocial patient characteristics.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».