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Guide

Healthcare AI governance

How to scope agents, policies, and escalation so AI stays operationally useful and controllable.

Start with operational scope, not model capability

Define which workflows an agent may touch — intake questionnaires, FAQ routing, queue signals, documentation drafts — and which it must never enter. Scope is a product and operations decision, not a prompt afterthought. Document approved channels, languages, and hours of automation before any model selection.

Policy controls belong in configuration

Governance works when rules live outside the chat transcript: knowledge boundaries, refusal conditions, review gates, and approval steps. Organisations set these during implementation so Assess-stage behaviour is predictable. Treat open-ended clinical judgment as out of scope for automation.

Escalation is the safety valve

Every governed workflow needs explicit handoff triggers — ambiguity, emotion, out-of-scope requests, identity risk, or staff override. Escalation should preserve context for a human operator without pretending the AI resolved the case. Measure handoff quality, not only automation rate.

Audit and ownership

Name who can change prompts, deploy workflow revisions, and review audit previews. Separate marketing-layer demos from production identity systems. Public product education uses illustrative audit streams; enforceable audit belongs in a future secure application plane under customer agreements.

Key takeaways

  • Scope agents by workflow and channel before choosing models.
  • Encode policies as configuration, not conversational hope.
  • Design human escalation early and test it deliberately.
  • Separate demo illustrations from production access controls.

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