Responsible AI in Healthcare Operations: A Governance Checklist

AI adoption in healthcare rarely fails on model accuracy. It fails on governance: unclear ownership, undocumented training data, and no plan for what happens when performance drifts.

First, define the decision the model informs and who remains accountable for it. A model that flags rising risk is a prioritization tool; a model that determines eligibility is a policy instrument. The oversight required differs sharply.

Second, document the training population and evaluate performance across subgroups. A model validated on a commercially insured population will behave differently in a Medicaid population, and that difference belongs in writing before deployment.

Third, establish monitoring. Data pipelines change, coding practices shift, and populations move. Without scheduled revalidation, silent degradation is the default outcome.

Fourth, plan the human workflow. Alerts that arrive without capacity to act on them train staff to ignore them.

Fifth, write down the retirement criteria. Knowing in advance what performance threshold would take a model out of production is the clearest sign a governance process is real.