ArticleDigital health
Safeguarding AI-driven digital health - An adaptation of the Swiss cheese model for safety.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
The use of artificial intelligence (AI) in digital health applications is increasing rapidly, creating new opportunities as well as safety challenges. AI-related errors can be systematic, repeating consistently across similar cases and, when deployed at scale, potentially affecting multiple patients within a short time. In this paper, we extend James Reason's classic distinction between person-based and system-based approaches to error by incorporating a third, technology-based perspective to account for failures inherent to AI-driven digital health systems and their lifecycle, including issues related to data, model design, deployment, and monitoring. Protecting patients in an increasingly AI-driven healthcare system requires not only model-centric controls, but an integrated set of overlapping technical, organizational, and regulatory safeguards designed explicitly for AI-driven digital health.To explore how such safeguards may be structured, we conducted a qualitative, exploratory analysis of 15 real-world incidents retrieved from an AI incident database. We extracted contributing factors for these incidents and conducted a thematic analysis. Aiming for conceptual alignment with Reason's Swiss cheese model for safety, the themes were grouped into higher-level system layers. As a result, we propose five protective layers to safeguard AI-driven digital health: (1) data governance and quality assurance; (2) model development, evaluation and change control; (3) sociotechnical integration and human oversight; (4) governance, regulatory and ethical compliance; and (5) post-market monitoring, incident response, and learning. We propose safeguards that were also derived from the incident analysis to operationalize the layers, but they are not intended to represent an exhaustive or consensus-based set of recommendations, but rather an exploratory, incident-informed framework to support analysis and system design. Finally, we argue for a "just culture" in AI-driven digital health, where researchers, healthcare professionals, and patients are encouraged to report critical incidents and errors, as this is essential for learning and system improvement.
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