Clinical AI without documented oversight isn’t a governance gap. It’s a liability.

Health systems, payers, and life sciences companies are deploying AI in clinical decision support, utilization management, and patient-facing tools faster than the governance behind it — and the FDA, ONC, CMS, and plaintiffs’ attorneys are paying attention. Trustible gives healthcare teams the structured intake, risk documentation, and audit-ready oversight that clinical AI governance requires.

92%

of health system leaders report using AI in clinical or operational contexts — but fewer than 1 in 4 have a formal governance program with documented risk assessments. That gap is the one regulators, accreditation bodies, and plaintiffs’ attorneys step into.

The challenge

Clinical AI carries patient-safety and liability risk

Clinical AI bias is a patient-safety problem

Systems trained on historical clinical data can systematically underserve certain patient populations — a pattern that doesn’t surface until an ONC review, a CMS audit, or a health-equity incident makes it visible.

FDA SaMD oversight is maturing

The line between exempt clinical decision support and software that triggers FDA regulation is narrow and shifting. Misclassifying a tool creates exposure your legal team won’t enjoy discovering after deployment.

CMS is scrutinizing AI in utilization management

Prior-authorization decisions made or influenced by AI are under examination. You need documented evidence that AI-assisted decisions are clinically valid, fair, and subject to human oversight.

Explainability is now required

The ONC HTI-1 rule on algorithmic transparency requires clinical decision support to be transparent about its basis. “The AI recommended this” is no longer sufficient.

Vendor AI in care delivery is governed by you

When a third-party tool is embedded in your EHR, scheduling, or care-management platform, your organization is the deployer and carries the liability. Your vendor’s governance program is not yours.

How Trustible helps

Governance that holds up to clinical scrutiny

Screen clinical AI before it reaches a patient

Structured intake captures the clinical context governance teams need: whether a system is Software as a Medical Device, what patient populations are affected, how clinical decisions interact with AI outputs, and what human oversight is in place.

Use case — A clinical AI governance committee screens every tool for FDA SaMD implications before deployment, replacing an informal review that produced inconsistent documentation across departments.

Score risk in healthcare-specific terms

Risk is scored across Performance, Data Privacy, Cybersecurity, Ethical, and Legal categories — with attributes for patient-safety impact, health-equity exposure, HIPAA considerations for training data, and clinical validation evidence.

Use case — A health system documents inherent and residual risk for every active clinical tool, with mitigation tracking and evidence that satisfies The Joint Commission standards on clinical decision support.

Prove oversight continued after go-live

Periodic review and attestation workflows create evidence that clinical tools in active use are being overseen — satisfying CMS expectations for ongoing oversight in utilization management and FDA post-market expectations for SaMD.

Use case — A payer documents quarterly clinical oversight of its AI-assisted prior-authorization system, producing an audit-ready history for program-integrity reviews.

Map once across health AI frameworks

Governance activity maps simultaneously to FDA AI/ML SaMD guidance, ONC HTI-1, CMS utilization-management guidance, and the EU AI Act’s high-risk classification for health AI — document once, produce evidence for each audience.

Use case — A digital health company maintains simultaneous FDA, EU AI Act, and NIST AI RMF mapping as it prepares for EU market entry, generating each framework’s technical documentation from one set of records.

Regulatory & framework coverage

Frameworks that govern healthcare AI

FDA AI/ML SaMD guidance

Assess whether clinical tools constitute Software as a Medical Device, document classification rationale, and maintain the oversight evidence FDA’s AI/ML action plan expects of developers and deployers.

ONC HTI-1

Capture the clinical decision support characteristics, data sources, and governance documentation the algorithmic-transparency rule requires to be available to clinicians and patients.

CMS AI utilization management guidance

Produce documented evidence of clinical validity, human oversight, and ongoing monitoring for AI-assisted prior authorization and utilization-management decisions.

EU AI Act

Map clinical use cases to high-risk health classifications, generate Annex IV technical documentation, and maintain the post-market monitoring evidence Article 72 requires.

NIST AI RMF

Operationalize GOVERN, MAP, MEASURE, and MANAGE across clinical AI governance workflows, generating the documentation health-system programs increasingly reference.

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Where healthcare teams go next

AI Risk & Impact Management

Clinical risk scoring and impact assessment with documented mitigations.

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AI Compliance & Audit

Map FDA, ONC, CMS, and EU AI Act evidence from one set of records.

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Continuous AI Monitoring

Prove ongoing clinical oversight with scheduled reviews and attestations.

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Clinical AI needs governance before something goes wrong.

Trustible gives healthcare governance teams the structured documentation, risk assessment, and oversight evidence that clinical AI requires.