AUTOGOVERN HEALTH · FREE PUBLIC LEARNING

Healthcare AI governance,
from policy to practice.

Learn how to review AI use in healthcare, protect health information and build a repeatable process for clinical and operational risk.

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EXPLORE THE FULL PLATFORM DESIGN

EHR Architecture & AI Governance

A dedicated, 18-section reference report covering the clinical platform, its governance model and the work needed to bring it into operation.

Free public report

01 / PLATFORM ARCHITECTURE

Design the clinical system

Clinical modules, data flows, FHIR integrations, deployment and recovery.

02 / AI & GOVERNANCE

Define the controls

AI use cases, human oversight, HIPAA, PHI/PII, accountability and monitoring.

03 / IMPLEMENTATION ROADMAP

Understand the work ahead

Current capabilities, proposed additions, delivery phases and effort estimates.

Explore all 18 sections →Explore the Healthcare AI Vision Board →Download complete report (.md) ↓

Proposed reference architecture · No account required

01 / UNDERSTAND THE SCOPE

Govern the workflow, the data and the decision.

Healthcare AI governance assigns responsibility for what an AI system is allowed to do, the evidence needed before use, and what happens when its behavior or context changes. Privacy, cybersecurity, patient safety, equity and human oversight all belong in the review.

Patient and clinical safety

Define intended use, assess errors that could cause harm, validate the relevant population and give clinicians a usable review and fallback process.

Health-data protection

Know which data enters the system, why it is used, who receives it, how it is protected and when it is deleted.

Accountability over time

Keep owners, vendor decisions, versions, evidence, monitoring coverage and incident actions connected to the approved use.

Terms to distinguish before you begin
Term	Meaning for your review
PII	Personally identifiable information. This is broader than HIPAA-protected health information; classify it in its legal and operational context.
PHI / ePHI	Protected health information / its electronic form. Assess identifiable health information and the covered-entity or business-associate relationship.
HIPAA	The Health Insurance Portability and Accountability Act. Privacy, Security and Breach Notification requirements have different scopes. “Healthcare” alone does not establish applicability.
BAA	A business associate agreement. Review the relationship and the exact service; a vendor claim or signed agreement alone does not demonstrate that controls work.

Scope references: HHS covered entities and business associates; FTC guidance for qualifying non-HIPAA health products.

This is a US-focused educational starting point. Consumer-health, substance-use-disorder, state and international requirements may add obligations. The how-tos are AutoGovern's suggested workflow, not a certification checklist or a clinical deployment approval.

02 / PUT IT INTO PRACTICE

Healthcare AI governance how-tos

Use these steps with your existing approved tools or the free templates below. Each step names a responsible team and the record it should produce.

Define and register a healthcare AI use case

Owner: Clinical or operational owner + governance lead

Start with one workflow: an ambient scribe, coding assistant, patient chatbot or predictive model. Describe the decision it influences and who could be harmed.

Write the intended use, users, care setting, affected population and uses that are out of scope. Separate administrative assistance from clinical recommendations.
Record the model/provider, version, connected tools, input data, output destination and level of autonomy. Identify who can approve a release and who can stop its use.
Assign privacy, security and clinical reviewers. Record each required decision, evidence owner and review date; name a clinical reviewer whenever care could be affected.
Check applicable legal relationships and product functions. HIPAA depends on entity and data context; FDA device status depends on the software function and intended use. Escalate uncertain scope before patient-facing deployment.

What to produce: A use-case card with named owners, intended-use limits and a review path.

Optional platform path: Healthcare workspace → New workflow → Overview

Map PHI, PII and permitted data use

Owner: Privacy officer + data owner

Follow data from collection through prompts, tools, outputs, logs, support systems and deletion. A provider API is only one part of the data flow.

Draw each source → recipient → storage path, including subprocessors and model fallback routes. List data categories rather than copying patient records into a governance worksheet.
Classify the information and document the purpose and authority for each use or disclosure. Apply minimum necessary where required; provider treatment disclosures and requests are among its exceptions.
Choose permitted recipients, role access, retention schedules and deletion evidence. Check whether consent or a specific authorization is required for the use; do not assume every HIPAA-permitted treatment use needs a new consent.
If using de-identified data, document Safe Harbor or Expert Determination. Removing names or masking regex matches alone does not establish de-identification.

What to produce: A reviewed data-flow register and a list of unresolved permissions or retention decisions.

Optional platform path: Healthcare workspace → Data flows

Review an AI vendor and its BAA

Owner: Procurement + privacy and security reviewers

Review the exact service, deployment and contract, not a vendor-wide marketing claim.

List every service that creates, receives, maintains or transmits ePHI on your behalf. Review business-associate status and relevant subcontractor arrangements; encryption without a provider-held key does not automatically remove cloud-provider obligations.
Where required, confirm an executed business associate agreement covers the service. Record an agreement reference, permitted uses, incident reporting terms and return/deletion provisions.
Ask about data retention, training on customer content, subprocessors, support access, regions, encryption, incident response and evidence of control testing. Record unresolved answers with an owner and deadline.
Approve specific endpoints and fallback services through your integration controls. Recheck material contract, model, hosting and subprocessor changes before extending the approved use.

What to produce: A service-specific vendor decision with agreement evidence, conditions and a review date.

Optional platform path: Healthcare workspace → Vendors & BAAs

Related official guidance: HHS: HIPAA and cloud computing ↗

Test clinical quality, bias and security

Owner: Clinical reviewer + evaluation and security teams

Define the acceptance criteria before looking at the results. A model that works on average may fail a particular population or workflow.

Create an approved evaluation set representative of intended use. Record provenance, permissions, model/prompt version, setting and sample limitations; use synthetic examples for early workflow tests.
Choose task-specific measures: unsupported statements and omissions for a scribe; escalation failures for a chatbot; sensitivity, specificity and calibration for a predictive model. Clinicians set acceptance limits based on the harm of errors.
Examine results across relevant populations and settings with adequate sample sizes and uncertainty. Small or missing subgroup samples are unknown coverage, not a fairness pass.
Test access boundaries, prompt injection, data leakage, incorrect tool use, clinician overrides and service outages. Record the failed cases, corrective work and retest evidence before release.

What to produce: A versioned evaluation report with metrics, denominators, limits and accountable sign-off.

Optional platform path: Store evaluations in your approved system; reference them in Controls & evidence and Risk register. AutoGovern does not perform clinical validation.

Approve a limited pilot with human oversight

Owner: Accountable clinical/operational lead + release reviewer

Make the release decision specific enough that another team can understand what was approved.

Review unresolved risks, vendor/data-use permissions, evaluation limits and the intended-use boundary. Document the rationale for proceeding, restricting or holding the pilot.
Choose a limited population, trained users, supervised actions and a review window. Explain to users what the AI can do, where it can fail and when they must escalate.
Define who reviews outputs before clinical or patient-facing use, who can override them, and how users report a problem. Record the approved model, prompt and tool versions.
Exercise the fallback and stop procedure. For care delivery, design a safe alternative workflow so stopping an AI service does not remove essential patient care.

What to produce: A dated pilot decision with restrictions, reviewer, fallback owner and reassessment triggers.

Optional platform path: Healthcare workspace → Review & export records a metadata review; it does not authorize clinical deployment.

Related official guidance: NIST: AI Risk Management Framework Playbook ↗

Monitor after release and respond to incidents

Owner: Service owner + privacy/security response team

Specify the signal, its source, coverage, threshold and response. Silence from a disconnected source is not evidence that the service is safe.

Collect approved, minimized telemetry: model changes, output quality, overrides, access anomalies, destination changes and incident reports. Record the last successful collection and the population observed.
Set review frequency and escalation thresholds with the responsible team. Track trends with denominators; distinguish alerts from confirmed incidents and verified outcomes.
On suspected harm or disclosure, follow the care-safe containment plan, preserve authorized evidence, record discovery time, and involve clinical, privacy and security owners. Keep patient details in the authorized incident system.
Have the privacy/legal team determine reporting duties. HIPAA breach analysis considers the information, recipient, whether it was acquired/viewed, and mitigation. Individual notices are required without unreasonable delay and no later than 60 days after discovery when applicable; other recipients, contracts and laws have their own rules.
Record corrective actions, verify closure and rerun the relevant evaluation before restarting or expanding the workflow.

What to produce: A monitoring plan and an incident runbook with owners, evidence references and escalation deadlines.

Optional platform path: Use Risk register, Cases and Controls & evidence for references. Live PHI/EHR monitoring and legal deadline calculations are not connected in this release.

03 / SEE THE REVIEW IN CONTEXT

Three healthcare AI examples

Illustrative scenarios to adapt to your use case. These are not patient records, validated clinical protocols or customer assessments.

Ambient clinical scribe

Purpose: Draft a note for a clinician to review.

Risk: Invented findings, omitted details, excessive audio retention or disclosure to an unapproved service.

Controls to consider: Clinician review before signing; approved recording/data-use process; measured omissions and unsupported statements; documented retention and vendor scope.

Evidence: Versioned sample review, reviewer corrections and a tested deletion procedure.

Patient-facing chatbot

Purpose: Answer approved administrative questions and route users to support.

Risk: Unreliable medical advice, missed escalation, prompt injection or patient-data leakage.

Controls to consider: Bounded response topics, an escalation path, adversarial tests and approved data destinations. Test that the bot hands off questions outside its scope.

Evidence: Escalation test cases, leakage tests and monitored handoff outcomes.

Predictive clinical model

Purpose: Support an explicitly defined clinical decision.

Risk: False negatives, subgroup performance gaps, drift or users relying on predictions outside the validated population.

Controls to consider: Clinician-approved thresholds, representative validation, subgroup uncertainty, version control and safe fallback. Assess device and certified-health-IT requirements where applicable.

Evidence: Local validation report, calibration and subgroup results, monitoring coverage and change-review records.

04 / TAKE A STARTING POINT

Free templates you can use today

Expand a template to see every field. Download the complete Markdown kit to edit in your own approved document system. No upload, registration or email is needed.

Healthcare AI use-case cardView template +

Workflow and intended use: [complete]
Care setting, users and affected population: [complete]
Out-of-scope uses: [complete]
Clinical/operational owner: [complete]
Privacy, security and clinical reviewers: [complete]
Model, prompt, provider and tool versions: [complete]
PHI/PII categories and approved data-flow references: [complete]
Vendor/service and BAA review reference: [complete]
Evaluation measures, acceptance limits and evidence: [complete]
Human review and escalation path: [complete]
Fallback owner and tested stop procedure: [complete]
Pilot restrictions, decision rationale and next review: [complete]

Vendor and data-flow reviewView template +

Exact vendor service / endpoint / fallback: [complete]
Source, recipient and purpose: [complete]
Data categories and permitted-use rationale: [complete]
BAA applicability and executed agreement reference: [complete]
Training, retention, support access and deletion terms: [complete]
Subprocessors and approved regions: [complete]
Security test evidence and open questions: [complete]
Incident reporting terms and responsible owner: [complete]
Approval conditions and next review date: [complete]

Monitoring and incident planView template +

Workflow and approved version: [complete]
Signal, source and collection frequency: [complete]
Coverage denominator and last successful collection: [complete]
Metric, uncertainty and escalation threshold: [complete]
Accountable reviewer and response target: [complete]
Clinical safety fallback and containment steps: [complete]
Authorized incident-system reference: [complete]
Discovery time and privacy/legal escalation: [complete]
Required notices and deadlines determined by reviewer: [complete]
Corrective action, retest evidence and restart decision: [complete]

Download all templates (.md)

Keep patient information in authorized clinical systems. Use governance metadata and evidence references in these templates.

05 / MAKE A MANAGEABLE START

Your first 30 days

A suggested planning cadence, not a regulatory deadline or a promise that a system will be ready for clinical use.

WEEK 1

Scope and ownership

Choose one workflow, identify the decision and affected population, and assign accountable reviewers.

WEEK 2

Data and vendors

Map data paths, review the exact services and agreements, and assign unresolved risks.

WEEK 3

Evidence and testing

Set acceptance criteria, review evaluation results and test oversight and fallback procedures.

WEEK 4

Review and follow-up

Record the pilot decision, restrictions, monitoring plan and next review. Hold use when evidence is insufficient.

WHEN YOU NEED A SHARED WORKSPACE

Keep your team's governance records together.

The organization workspace supports metadata records, risk treatment, control-test references and reviewed dossier exports. Sign-in is required for saved organization records. The public guide and starter kit above remain free without an account.

The current workspace does not connect to EHRs, monitor live PHI or perform clinical validation.

Open organization workspace →

06 / CHECK THE PRIMARY GUIDANCE

Official sources and further reading

Source review: September 7, 2026. Check current requirements for your role, jurisdiction and intended use before making a formal decision.