Previously submitted to: JMIR AI (no longer under consideration since Apr 13, 2026)
Date Submitted: Mar 17, 2026
Open Peer Review Period: Mar 30, 2026 - May 25, 2026
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AI Governance Failures in Healthcare: A Diagnostic Framework Integrating Policy Tools and Care Delivery
ABSTRACT
Artificial intelligence is increasingly embedded in healthcare delivery, yet governance has not kept pace with its scale and influence. Algorithms now shape decisions about care allocation, treatment duration, and patient prioritization, often operating with limited transparency and oversight. High-profile failures, including the Optum risk stratification algorithm and the nH Predict coverage system, show how governance weaknesses can produce large-scale and systematically unequal outcomes. Existing approaches remain analytically incomplete, typically explaining either why governance instruments fail or where breakdowns occur within the care delivery process, but rarely both together. This gap leads to recurring failures being treated as isolated incidents rather than structural patterns. To address this limitation, this paper proposes a diagnostic framework that integrates policy tool analysis with the structure–process–outcome model. The resulting 4×3 matrix links governance characteristics to stages of care delivery, enabling more precise identification of how failures emerge and propagate. Applied to two widely documented cases, the framework reveals consistent governance deficits and offers a structured basis for anticipating risks and strengthening oversight.
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