Simulation-Based Therapeutic Patient Education in Diabetes: The SIM-TPE Model

Abstract

Therapeutic patient education (TPE) is a core component of diabetes care. However, many programs remain organized around topic-based workshops in which information precedes action. Although this approach conveys useful knowledge, it does not always establish whether people with diabetes can mobilize that knowledge when faced with a real, unexpected, or complex situation. To review the potential contribution of simulation to TPE in diabetes and to propose a structured methodological framework, SIM-TPE Diabetes, in which the simulated problem situation becomes the basic pedagogical unit rather than a primarily didactic workshop. A targeted narrative review was conducted using international guidance on TPE and diabetes self-management education and support (DSMES), contemporary healthcare simulation standards, and key publications on patient-directed simulation, serious games, and experiential approaches in diabetes. The synthesis was used to develop a reproducible and adaptable educational framework. This was a narrative, not systematic, review; no formal meta-analysis or risk-of-bias assessment was undertaken. SIM-TPE Diabetes comprises seven steps: situation-centered educational assessment, selection of observable competencies, prebriefing, a simulated problem situation, structured debriefing, re-simulation, and transfer to daily life. The curriculum is organized around lived situations—such as hypoglycemia, unexpected hyperglycemia, interpretation of glucose data, eating, physical activity, treatment management, intercurrent illness, and complex real-life contexts—rather than around isolated topics. The DÉCLIC debriefing structure (Describe, Explore, Clarify, Link, Identify, Commit) is proposed as a simple patient-adapted framework. Simulation may bridge the gap between knowing and acting. SIM-TPE Diabetes shifts the educational focus from information delivery toward experience, reasoning, decision-making, and transfer. The model is intentionally feasible with low-technology resources; its comparative effectiveness, acceptability, reproducibility, and optimal implementation require prospective evaluation.