Purpose and scope
Define the health workflow, intended benefit, affected people, consequence, exclusions, and measurable outcome before choosing a tool.
Apply: govern a bounded health AI use case
Connect adoption to approved data practices, safety review, workforce capability, accountability, and evidence before making a scale decision.
Build the operating case
Governance and safety
Set data access, privacy, security, clinical review, validation ownership, incident response, and escalation requirements.
Evidence for scale
Measure usefulness, safety, quality, workforce impact, equity, correction load, and unresolved risk through a bounded implementation.
Safety controls and review thresholds
- Low consequence: administrative pilots remain isolated from clinical decision-making and use approved data paths.
- Medium consequence: workflow recommendations require named clinical or operational validation and visible evidence before release.
- High consequence: patient-impacting, diagnostic, treatment, or regulated uses require independent review, explicit approval, monitoring, and an escalation route.
Practice output
Produce a Health AI Governance Integrity Packet with outcome, assumptions, evidence, validation status, ownership, risk, monitoring plan, and hold or scale recommendation.
Approved-use boundaries
Do not treat a successful demonstration as approval for production. Confirm data authorization, clinical safety, security, privacy, procurement, workforce readiness, and patient or member impact before expanding a use case.
Continue within approved practice
Return to the health organization path, see a governance brief example, or review scale controls. This site provides general practice guidance rather than medical advice.