Responsible AI governance begins before a model is purchased or deployed. Healthcare organizations need a shared process that connects clinical safety, information security, privacy, data quality, legal obligations and operational value.
Define decision rights
Assign accountable executive ownership and a multidisciplinary review group. Clarify who can approve a pilot, who can approve production use and who can pause a system when risk changes.
Classify use cases
Not every use case carries the same consequence. Administrative automation, patient communication, clinical decision support and autonomous actions should receive different levels of evidence, review and monitoring.
Document the lifecycle
Maintain a register of models, vendors, intended users, data sources, performance limitations, human oversight and change history. Governance should continue after deployment through incident reporting, drift monitoring and periodic re-approval.
Measure value and harm
Track operational outcomes alongside safety, fairness, privacy and user experience. A system that appears accurate but creates workflow burden or unequal access may not deliver acceptable value.
