Explainable
The people relying on a deployment can inspect the basis for its current authorization: intended use, evidence, assumptions, known limits, and accountable owner.
Operating model
A deployment should remain authorized only while the facts supporting reliance remain true.
Healthcare AI governance is not a one-time review. It is an operating system for deciding when a deployment may influence care, what conditions limit that permission, how people can challenge it, and what happens when evidence or performance changes.
01 · Governing premise
Three properties of a governable deployment
Challengeable
A clinician, operator, patient advocate, or reviewer can contest a result or deployment condition through a defined route that does not depend on informal access or personal discretion.
Correctable
When evidence, policy, performance, or operating conditions change, the institution can reconsider the deployment, issue a disposition, and propagate the correction to affected workflows.
02 · Authorization states
Authority increases only as evidence and correction capacity increase
These states are a reusable pattern, not a universal regulatory classification. Each institution should set evidence requirements proportionate to the decision, population, exposure, and reversibility of the deployment.
The proposed use cannot yet influence care or workflow because the institution has not bounded the decision, named the accountable owners, or defined the evidence needed for reliance.
- Defined clinical or operational decision
- Named deployment and escalation owners
- Specified affected population and exclusions
Transition condition: A bounded use case, accountable owner, and evaluation plan are approved.
The system operates without influencing care or workflow. Outputs are captured to test relevance, failure modes, and whether the proposed controls fit real operating conditions.
- Silent or retrospective evaluation
- Error taxonomy and exception review
- Baseline comparison against current practice
Transition condition: Observed performance and failure modes justify a limited prospective deployment.
The system may inform a narrow workflow under explicit human review, limited population scope, active exception monitoring, and predefined stop conditions.
- Prospective workflow validation
- Documented override and escalation paths
- Monitored safety, equity, and operational indicators
Transition condition: The deployment performs acceptably inside its stated scope and the institution can pause, correct, or roll it back.
The institution permits routine reliance within a defined scope while preserving independent review, challenge rights, change detection, and periodic reconsideration.
- Stable prospective performance
- Independent evaluation appropriate to the use
- Operational readiness for correction and rollback
Transition condition: Authorization remains conditional: material change reopens review rather than silently inheriting prior approval.
03 · Authorization record
The decision artifact that should persist after approval
Approval should produce a versioned reliance record, not merely a meeting outcome or compliance ticket. The record makes the deployment inspectable and creates the object that can later be reconsidered.
- The decision or workflow being supported
- Authorized users, population, setting, and exclusions
- Current model, prompt, policy, data, and integration versions
- Evidence and assumptions supporting reliance
- Known limitations and required human checks
- Named deployment, clinical, and escalation owners
- Effective date, review date, and superseded authorization
- Stop conditions and rollback route
04 · Reconsideration loop
What happens when the operating facts change
Detect
Identify a potentially material change in evidence, model behavior, policy, data, workflow, population, or observed outcomes.
Output · Change signalConnect
Map the change to the specific authorization assumptions, populations, workflows, and downstream decisions that may be affected.
Output · Affected reliance mapTriage
Assess materiality, urgency, reversibility, exposure, and whether continued operation is acceptable while review proceeds.
Output · Review priority and interim controlsReview
Present the source-traced change, prior rationale, observed performance, dissent, and unresolved uncertainty to the authorized reviewers.
Output · Reconsideration caseDecide
Issue an explicit disposition: preserve, caveat, narrow, expand, monitor, escalate, suspend, or retire.
Output · Authorized dispositionPropagate
Update the authorization record and each connected workflow, instruction, interface, monitoring rule, and affected stakeholder.
Output · Corrected operating state05 · Escalation matrix
Default responses should exist before the edge case
| Trigger | Default action | Decision owner | Required record |
|---|---|---|---|
| Immediate patient-safety signal or prohibited use | Suspend affected use; preserve records; initiate urgent review | Clinical safety owner | Incident, scope, interim control, and restart authority |
| Material performance degradation or distribution shift | Constrain scope or return to observed mode | Deployment owner with clinical sign-off | Metric change, affected population, and evaluation plan |
| New evidence changes benefit, risk, or required qualification | Open a reconsideration case; assess continued reliance | Evidence or policy owner | Source change, affected scope, rationale, and disposition |
| Repeated overrides, complaints, or workflow workarounds | Investigate usability, fit, incentives, and hidden failure modes | Product and operations owner | Pattern analysis, user accounts, and corrective action |
| Model, prompt, data, vendor, or integration change | Reassess inherited authorization before release | Change approver | Version change, test evidence, and authorization linkage |
06 · Independent validation
Reliance should not eliminate the institution’s ability to detect that it is wrong
A sample of cases receives independent review rather than automatically accepting the system-supported path.
Reviewers compare outcomes, overrides, delays, and subgroup effects against current practice where the deployment permits a valid comparison.
Independent review continues after initial approval so gradual drift and workflow adaptation can remain visible.
Material discrepancies trigger reconsideration; they are not absorbed as routine monitoring noise.
07 · Implementation boundary
A control layer, not a replacement authority
- This framework does not decide clinical truth or replace authorized clinical, legal, safety, or compliance judgment.
- It does not prescribe one universal score or promotion threshold. Evidence standards should match the decision, population, exposure, and reversibility of the use.
- It does not treat initial approval as permanent. Authorization is versioned, scoped, and conditional on the facts that supported it.
- It does not require replacing the existing technology stack. The governance record can connect model, EHR, quality, policy, and incident systems.
The practical objective is bounded institutional reliance: the system may move work faster inside an authorized scope, while human authority, challenge rights, and correction obligations remain explicit.
08 · Related work
From principles to operating practice
The framework defines the tests. The case studies show related patterns in production systems. This page describes the control structure that connects them.