Governance of AI-Generated Explanations of Test Results Sent to Patients

Lifecycle of an explanation and its control points

Between result release and patient delivery, an explanation passes through five control points. Each one is a place where governance is applied to a single explanation.

What the governance framework must cover

The framework needs to enforce a defined set of controls at each stage between result release and portal delivery. These are:

• Eligibility rules that determine whether a result may receive an AI explanation.
• Least-privilege data and tool access for each request.
• Output validation against source data and approved language.
• Clinician review thresholds that route defined cases to a clinician before release.
• A log record for every explanation.

Policy statements versus runtime-enforceable controls

A policy statement describes what should happen. A runtime-enforceable control is one the system checks in code while it operates. Controls that exist only in policy documents do not constrain the system.

Implementation decisions and tradeoffs

The questions below frame the main implementation decisions: whether validation blocks release and fails closed when it errors, how narrowly data and tool access are scoped for each request, and how clinician reviews and overrides are recorded.

Evaluate your current approach

• Can you show, for any released explanation, which eligibility rule allowed it?
• Can you show the exact source data and the model and prompt versions used to produce it?
• Is validation a blocking step, and does the system fail closed when it errors?
• Can you demonstrate that data and tool access were limited for that specific request?
• Are clinician reviews and overrides recorded with the identity, time, and edited text?
• Can the audit record be retrieved and reviewed during an incident without reconstructing it manually?

Where a runtime governance layer fits

A runtime governance layer is where access scoping, policy checks, and audit logging are enforced for patient-facing AI workflows. It applies the controls described above to each explanation as it moves from result release to portal delivery.