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arXiv · 2605.04085

Evaluating Patient Safety Risks in Generative AI: Development and Validation of a FMECA Framework for Generated Clinical Content

Lydie Bednarczyk·Jamil Zaghir·Julien Ehrsam·Maria Tcherepanova·Christian Skalafouris·Karim Gariani·Catherine Geslin·Claire-Bénédicte Rivara·Pascal Bonnabry·Laetitia Gosetto·Richard Dubos·Mina Bjelogrlic·Christophe Gaudet-Blavignac·Christian Lovis
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Abstract

Objectives: Large language models (LLMs) are increasingly used for clinical text summarization, yet structured methods to assess associated patient safety risks remain limited. Failure Mode, Effects, and Criticality Analysis (FMECA) provides a proactive framework for systematic risk identification but has not been adapted to LLM-generated clinical content. This study aimed to develop and validate a novel FMECA framework for the prospective assessment of patient safety risks in LLM-generated clinical summaries. Materials and Methods: An interdisciplinary expert panel (n = 8) developed a taxonomy of failure modes through literature review and brainstorming. Standard FMECA dimensions (occurrence, severity, detectability) were adapted into 5-point ordinal scales. The framework was applied to 36 discharge summaries from four patients, generated by an open LLM (GPT-OSS 120B) using real-world clinical data from the Geneva University Hospitals. Reviewers independently annotated the summaries across two rounds. Inter-rater reliability was assessed at failure mode, severity and detectability score levels. Usability and content validity were evaluated using an adapted System Usability Scale and structured feedback. Results: The final framework comprised 14 failure modes organized into categories. Inter-rater agreement improved between rounds, reaching moderate-to-substantial agreement for failure mode identification and good agreement for severity and detectability scoring. Usability was rated as good (mean SUS: 79.2/100), with high evaluator confidence. Discussion and Conclusion: This study presents the first FMECA-based framework for systematic patient safety risk assessment of LLM-generated clinical summaries. The framework provides a structured and reproducible method for identifying clinically relevant risks caused by these summaries.

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Lydie Bednarczyk, Jamil Zaghir, Julien Ehrsam, Maria Tcherepanova, Christian Skalafouris, Karim Gariani, Catherine Geslin, Claire-Bénédicte Rivara, Pascal Bonnabry, Laetitia Gosetto, Richard Dubos, Mina Bjelogrlic, Christophe Gaudet-Blavignac, Christian Lovis. 2026-04-23. Evaluating Patient Safety Risks in Generative AI: Development and Validation of a FMECA Framework for Generated Clinical Content. https://arxiv.org/abs/2605.04085

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