Case study evaluates governance of AI-assisted clinical triage systems in healthcare, suggesting implications for safety and efficiency.
Healthcare systems increasingly deploy artificial intelligence tools to assist with clinical triage, diagnosis prioritization, and patient flow management. These systems operate in environments characterized by high decision pressure, limited time for review, and potentially severe consequences. This case study applies the FlowFusion institutional governance architecture to evaluate whether an AI-assisted clinical triage system remains governable, observable, reversible, and correctable under real-world healthcare conditions. The analysis examines the decision architecture, structural fragility, and governance risks associated with automated triage prioritization.
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Khaelalya (2026) studied this question.
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