AI Governance Compared with Decision Governance
Healthcare AI governance contains several necessary disciplines. They solve different problems and should not be treated as interchangeable.
Healthcare AI governance contains several necessary disciplines. They solve different problems and should not be treated as interchangeable. Decision governance connects policies, technical systems, institutional authority, actual decisions, and durable evidence at the point of consequential use.
| Approach | Primary contribution | What decision governance adds |
|---|---|---|
| Policy governance | Defines principles, responsibilities, and intended conduct. | Establishes whether a specific decision remained within authorized boundaries and records the result. |
| Model monitoring | Observes technical performance, drift, reliability, bias, latency, and security indicators. | Connects system behavior to institutional authorization, workflow context, evidence sufficiency, and accountable human authority. |
| Vendor-generated reports | Provide information about a vendor's product, controls, or service. | Preserves institution-controlled evidence of the institution's own consequential decisions and oversight. |
| Generic AI governance | Provides enterprise-wide governance concepts applicable across sectors. | Adds healthcare-specific clinical authority, patient safety, continuity, privacy, professional accountability, and liability context. |
| Framework alignment | Maps policies or capabilities to standards and governance frameworks. | Demonstrates how requirements operate in practice at the time of an actual consequential decision. |
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