Healthcare AI evaluation often focuses on model outputs, including discrimination, calibration and predictive performance. These measures are important, but they do not fully explain whether a system improves clinical practice.
Between an AI recommendation and a patient outcome lie several distinct stages: clinical review, decision-making, intervention availability, execution and follow-up.
A recommendation may be accepted, modified, rejected or never reviewed. Each possibility has a different interpretation.
For example, a clinician may override an AI recommendation because additional evidence changes the decision. Alternatively, a useful recommendation may not be acted upon because the alert arrives too late or the required resource is unavailable.
These situations should not be grouped into a single category of recommendation failure.
A robust evaluation framework should capture recommendation status, decision rationale where appropriate, action completion, operational barriers and relevant outcomes. It should also account for confounding factors before attributing outcomes to the AI system.
Agentic systems need an additional layer of execution verification. The system should distinguish between an intended action, an attempted action and confirmed completion. Failed actions, exceptions and human interventions should be visible to the appropriate monitoring and audit processes.
Feedback should not automatically become a training signal. Clinical outcomes are influenced by many factors, and naive optimization can reinforce harmful behavior or reward the wrong objective.
The goal is a governed feedback loop that helps teams understand real-world impact while preserving clinical judgment.
A healthcare AI system should not stop at generating an output. It should help establish what happened next.
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