---
title: "Appendix L: Clinical AI Policy Templates – The Physician AI Handbook"
source: "https://physicianaihandbook.com/appendices/policy-template.html"
author: "Bryan Tegomoh, MD, MPH"
last_updated: "2026-10-30"
doi: "10.5281/zenodo.18251404"
license: CC BY 4.0
generated-by: handbook-markdown-export
---

# Appendix L: Clinical AI Policy Templates

TL;DR

**Customizable discussion templates included:**

- AI Governance Committee charter and structure
- Clinical AI approval workflow (request → review → pilot → deploy)
- Periodic AI performance report
- Vendor evaluation checklist (see the [Clinical AI Vendor Evaluation Toolkit](../appendices/vendor-evaluation.html))
- Incident reporting and response procedures
- Staff training requirements by role
- Patient notification and consent language

**Key governance principles:** Risk-proportionate oversight, evidence matched to claims, clear accountability, change control, incident response, and monitoring tied to the intended use.

**Customize for:** Institutional structure, jurisdiction, clinical risk, applicable law, regulatory status, workflow, and patient population.

## Hospital AI Governance Policy Templates

### Executive Summary

These author-created templates set out the questions a clinical AI policy should answer. They have not been validated as a legal compliance program or copied from a verified set of institutional policies.

Legal and Operational Notice

These templates are discussion starting points only. Clinical leadership, medical staff, quality and safety, privacy, security, regulatory, legal, procurement, and patient representatives should review and adapt them to the exact system and jurisdiction. No template can promise HIPAA compliance, patient consent, opt-out rights, liability allocation, or FDA reporting obligations across every use.

------------------------------------------------------------------------

## Template 1: Clinical AI Governance Policy

### POLICY: Clinical Artificial Intelligence Governance

**Effective Date:** \[Date\]
**Policy Number:** \[XXX-XXX\]
**Approved By:** \[Chief Medical Officer\] \[Date\]

#### I. PURPOSE

To establish governance structures and processes for the safe, effective, and ethical implementation of artificial intelligence (AI) technologies in clinical care at \[Institution Name\].

#### II. SCOPE

This policy applies to all AI/ML systems used for:

- Clinical decision support
- Diagnostic assistance
- Risk prediction
- Treatment recommendations
- Clinical documentation
- Resource allocation
- Any system that directly or indirectly affects patient care

#### III. DEFINITIONS

**Artificial Intelligence (AI):** Computer systems performing tasks that typically require human intelligence.

**Clinical AI System:** Any AI/ML technology used in patient care delivery, diagnosis, treatment planning, or clinical operations.

**Algorithm Steward:** Designated clinical leader responsible for specific AI system oversight.

**Model Drift:** Degradation of AI model performance over time due to changes in data or clinical practice.

#### IV. GOVERNANCE STRUCTURE

##### A. AI Governance Committee

**Illustrative composition (adapt to local authority and risk):**

- Chief Medical Officer (Chair)
- Chief Information Officer
- Chief Medical Information Officer
- Department Chair Representatives (minimum 3)
- Nursing Leadership
- Quality & Safety Officer
- Legal Counsel
- Patient Representative
- Medical Ethics Representative
- Health Equity Officer

**Responsibilities:**

1.  Review and approve all clinical AI implementations
2.  Establish evaluation criteria for AI systems
3.  Monitor ongoing AI performance and safety
4.  Address ethical concerns and bias issues
5.  Ensure regulatory compliance
6.  Review adverse events related to AI use

**Meeting frequency:** Defined by risk, decision volume, monitoring signals, and the need for urgent review.

##### B. Algorithm Stewardship

Each approved consequential AI system should have named accountability appropriate to its use:

- **Clinical Algorithm Steward:** Senior clinician responsible for clinical oversight
- **Technical Algorithm Steward:** IT/informatics lead for technical maintenance
- **Quality Monitor:** QI professional tracking performance metrics

#### V. IMPLEMENTATION REQUIREMENTS

##### A. Pre-Implementation

Documentation to consider before clinical AI deployment, proportionate to risk:

Exact FDA or other regulatory record, pathway, and intended use where applicable

Clinical validation studies

Bias and fairness assessment

Workflow integration plan

Training materials and competency assessments

Failure mode analysis

Data privacy impact assessment

Vendor contract review (if applicable)

##### B. Pilot Requirements

The evaluation design should follow the intended use, risk, event rate, workflow, and evidence gap. A bounded pilot may be appropriate, but no fixed duration or parallel-workflow requirement fits every system. Prespecify the comparator, endpoints, stopping rules, review process, and scale decision.

##### C. Training Requirements

Before using a consequential clinical AI system, users should, as appropriate to their role:

1.  Complete system-specific training
2.  Demonstrate competency
3.  Understand limitations and failure modes
4.  Know escalation procedures

#### VI. ONGOING MONITORING

##### A. Performance Monitoring

**Required Metrics:**

- Clinical accuracy/agreement rates
- Alert fatigue measures
- System uptime and response times
- User adoption and satisfaction
- Patient safety events
- Disparate impact analysis

**Review frequency:** Set from clinical risk, event rate, expected change, volume, regulatory obligations, and performance signals. Fixed weekly, monthly, quarterly, or annual schedules should not replace risk-based triggers.

##### B. Model Performance Surveillance

- Monitoring appropriate to the model, data, workflow, and failure consequences
- Performance and allocation audits at justified intervals
- Bias and fairness assessment linked to clinically meaningful groups and decisions
- Immediate investigation of unexpected outcomes

#### VII. CLINICAL DECISION RIGHTS

1.  Decision rights should match the authorized intended use and institutional policy.
2.  Decision-support outputs do not replace professional accountability for the clinical decision.
3.  Override, deferral, escalation, and documentation expectations should be explicit.
4.  Autonomous functionality requires product-specific regulatory, medical-staff, safety, legal, and workflow review.
5.  Patient communication should follow applicable law, ethics, risk, institutional policy, and practical feasibility.

Requests to evaluate autonomous clinical AI require separate medical staff, legal, and regulatory review beyond standard decision-support governance. See [Clinical AI Policy and Governance](../future/policy.html#sec-autonomous-ai-licensure).

#### VIII. DATA GOVERNANCE

- Determine HIPAA and other privacy roles for the exact data flow.
- Establish required agreements, permitted uses, safeguards, retention, deletion, and secondary-use limits.
- De-identification is one possible control, not a universal requirement for every external use.
- Assess whether notice, consent, or opt-out is legally required, ethically appropriate, operationally feasible, or limited by embedded systems.

#### IX. QUALITY ASSURANCE

##### A. Adverse Event Reporting

AI-related incidents should follow severity-based institutional response:

1.  Address immediate clinical risk and preserve the relevant record, version, input, output, and workflow context.
2.  Notify the accountable clinical, safety, technical, privacy, or security owners according to severity.
3.  Investigate the complete sociotechnical pathway and determine corrective action.
4.  Assess external reporting obligations for the exact product and event. Device association alone does not make FDA reporting automatic.

##### B. Regular Audits

- Clinical outcome audits when outcome benefit is claimed or a safety signal warrants review
- Versioned performance reviews at risk-based intervals and after material change
- Periodic system evaluation covering workflow, equity, privacy, security, and continuity

#### X. VENDOR MANAGEMENT

For vendor-supplied AI systems, consider:

- Service levels appropriate to the clinical dependency
- Regular performance reviews
- Liability and indemnification clauses
- Data ownership clearly defined
- Exit strategy documented

#### XI. COMPLIANCE

Before adoption, map the policy against the current requirements actually applicable to the institution and product. Possible sources include FDA device requirements, HIPAA, state law, accreditation standards, CMS participation requirements, medical-staff bylaws, contracts, and professional duties. Listing an authority here does not establish compliance.

#### XII. POLICY VIOLATIONS

Responses should follow severity, intent, patient impact, applicable policy, and due process. Options may include:

- Immediate system suspension
- Remedial training requirements
- Disciplinary action per medical staff bylaws
- Reporting to appropriate regulatory bodies

------------------------------------------------------------------------

## Template 2: Clinical AI Implementation Checklist

### PRE-IMPLEMENTATION CHECKLIST

**System Name:** \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_
**Vendor/Developer:** \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_
**Implementation Date:** \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_

#### Clinical Validation

Peer-reviewed publication demonstrating efficacy

Exact regulatory record, pathway, and intended-use match where applicable: \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_

Local validation completed on our patient population

Failure modes documented and understood

Clinical champion identified: \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_

#### Technical Requirements

Integration with EMR/PACS/LIS confirmed

Cybersecurity assessment completed

Disaster recovery plan documented

System redundancy in place

Performance benchmarks established

#### Workflow Integration

Current state workflow mapped

Future state workflow designed

Impact on clinical efficiency assessed

Change management plan developed

Training materials created

#### Ethical & Legal

Bias assessment completed

Disparate impact analysis performed

Patient consent process defined

Liability coverage confirmed

Data use agreements signed

#### Quality Metrics

Baseline metrics collected

Performance indicators defined

Monitoring plan established

Reporting dashboard created

Escalation procedures documented

#### Training & Competency

Training curriculum developed

Competency assessment created

Super-users identified

Support resources available

Ongoing education plan defined

**Approval Signatures:**

CMO: \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_ Date: \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_
CIO: \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_ Date: \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_
Department Chair: \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_ Date: \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_

------------------------------------------------------------------------

## Template 3: Patient Communication Template

### PATIENT INFORMATION: AI-Assisted Care

Dear Patient,

\[Hospital Name\] uses \[name and describe the specific system\] for \[specific purpose\]. This notice explains what the system does, what information it uses, how it may affect care, and whom to contact with questions.

#### How We Use AI

The system may assist the care team by:

- \[Describe the exact input and task\]
- \[Describe the output and recipient\]
- \[Describe whether the output supports or performs a clinical decision\]
- \[Describe important limitations and alternatives\]

#### Important Things to Know

**Decision authority:** \[Explain who makes or reviews the decision for this intended use\]
**Choice:** \[Explain whether consent or opt-out applies, any limits, and available alternatives\]
**Data:** \[Explain the relevant data use, recipients, safeguards, retention, and contact\]
**Limitations:** \[Explain material limitations, failed-input handling, and escalation\]

#### Your Rights

Depending on applicable law and institutional policy, patients may be able to:

- Ask how the system is used in their care
- Ask questions about an output or decision
- Request information about alternatives or human review
- Seek a second opinion where ordinarily available
- Raise concerns through the institution’s established process

#### Questions?

Please ask your healthcare provider or contact:

- Patient Relations: \[Phone\]
- Email: \[Email\]
- Website: \[URL\]

------------------------------------------------------------------------

## Template 4: AI Choice or Opt-Out Form

### PATIENT CHOICE: AI-Assisted Clinical Care

**Patient Name:** \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_
**Medical Record \#:** \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_
**Date:** \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_

#### Patient Declaration

Use this form only after legal, ethical, clinical, and operational review establishes that a choice or opt-out process applies. After discussing the specific system with the care team, the patient chooses to:

**Opt-out completely:** No AI tools in my care

**Selective opt-out:** No AI for:

Diagnostic imaging

Risk prediction

Treatment recommendations

Clinical documentation

Other: \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_

#### Acknowledgments

The institution should explain system-specific consequences, alternatives, limits, reversibility, effect on access, and whether the system can be selectively disabled. An opt-out right should not be promised when law, product architecture, or clinical safety does not support it.

**Patient Signature:** \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_ **Date:** \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_
**Witness:** \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_ **Date:** \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_

------------------------------------------------------------------------

## Template 5: AI Incident Report Form

### CLINICAL AI INCIDENT REPORT

**Report Date:** \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_
**Reporter Name:** \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_
**Department:** \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_

#### System Information

- **AI System Name:** \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_
- **Vendor:** \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_
- **Version:** \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_

#### Incident Details

**Date/Time of Incident:** \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_
**Patient Affected (MRN):** \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_

**Incident Type:**

Incorrect recommendation

System failure/downtime

Integration error

User interface issue

Alert fatigue

Bias/discrimination concern

Other: \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_

**Description:** \[Detailed description of incident\]

**Clinical Impact:**

No impact on patient care

Delayed care

Incorrect treatment

Near miss

Adverse event

Other: \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_

**Immediate Actions Taken:** \[List actions\]

**Root Cause (if known):** \[Description\]

**Recommendations:** \[Suggested improvements\]

**Report Submitted to:**

Department Chair

Risk Management

AI Governance Committee

Vendor (if applicable)

External regulator or authority after obligation review

------------------------------------------------------------------------

## Template 6: Periodic AI Performance Report

### CLINICAL AI PERFORMANCE REPORT

**System:** \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_
**Reporting Period:** \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_
**Algorithm Steward:** \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_

#### Performance Metrics

| Metric | Target | Actual | Status |
|----|----|----|----|
| \[Task-specific performance metric\] | \[Prespecified\] | \_\_\_\_ | \_\_\_\_ |
| System availability | \[Risk-based\] | \_\_\_\_ | \_\_\_\_ |
| Response time | \[Workflow-based\] | \_\_\_\_ | \_\_\_\_ |
| Appropriate use or response | \[Prespecified\] | \_\_\_\_ | \_\_\_\_ |
| Override, deferral, or escalation | \[Interpret in context\] | \_\_\_\_ | \_\_\_\_ |

*Targets and status definitions should be prespecified for the intended use; no values in this template are universal.*

#### Clinical Outcomes

- Cases processed: \_\_\_\_
- Clinical interventions influenced: \_\_\_\_
- Estimated time saved: \_\_\_\_ hours
- Patient satisfaction score: \_\_\_\_

#### Issues & Resolutions

| Issue | Date | Resolution | Status |
|-------|------|------------|--------|
|       |      |            |        |

#### Bias Monitoring

- Demographic performance variation: \_\_\_\_
- Disparate impact identified: Yes/No
- Mitigation actions: \_\_\_\_

#### User Feedback Summary

\[Key themes from user feedback\]

#### Recommendations

1.

    ------------------------------------------------------------------------

2.

    ------------------------------------------------------------------------

3.

    ------------------------------------------------------------------------

**Report Prepared By:** \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_ **Date:** \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_
**Reviewed By:** \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_ **Date:** \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_

------------------------------------------------------------------------

## Template 7: Vendor Contract Addendum Discussion Draft

### AI VENDOR CONTRACT ADDENDUM ISSUES

The issues below are for institutional counsel and procurement. They are not contract language or legal advice.

#### Performance Guarantees

Counsel should define the exact version, intended use, metric, comparator, denominator, uncertainty, measurement period, availability dependency, response-time need, regulatory representation, remediation, and exit consequence.

#### Liability & Indemnification

Counsel should address responsibility allocation, vendor representations, institutional configuration, professional and product liability, insurance, indemnification, defense, exclusions, and caps. No universal insurance minimum, indemnity, or liability-cap rule applies.

#### Data Rights & Privacy

Define rights and obligations for patient data, institutional data, derived artifacts, feedback, model training, subprocessors, retention, return, deletion, verification, and a BAA when required by the parties’ HIPAA roles.

#### Clinical Validation

Define reporting, independent validation, audit and publication rights, material-degradation criteria, re-evaluation triggers, remediation, pause, rollback, and termination.

#### Transparency Requirements

Issues for counsel to address include:

- Risk-based advance notice for material changes
- Access to validation studies and FDA submissions
- Adverse event reports from other clients (de-identified)
- Fairness and allocation assessments appropriate to the intended use

#### Termination Rights

Counsel should consider termination or suspension triggers related to safety, regulatory action, material nonperformance, security incidents, unapproved change, insolvency, continuity, convenience, and failed remediation, together with transition and data-portability obligations.

------------------------------------------------------------------------

## Implementation Guide

### How to Use These Templates

1.  **Customize for the Institution**
    - Replace \[bracketed\] text with institution-specific details
    - Adjust committee structures to match the organization
    - Align with existing policies
2.  **Legal Review Required**
    - Have legal counsel review all templates
    - Ensure compliance with state regulations
    - Verify alignment with medical staff bylaws
3.  **Stakeholder Engagement**
    - Present to medical staff for input
    - Review with nursing leadership
    - Obtain board approval where required
4.  **Test the Governance Process**
    - Apply the policy to representative systems and risk levels
    - Gather feedback, exercise incident and rollback pathways, and refine
    - Expand only when decision rights and controls work as intended
5.  **Risk-Based Updates**
    - Review on a justified schedule and after material legal, regulatory, product, workflow, or incident change
    - Update based on regulatory changes
    - Incorporate lessons learned

### Common Implementation Mistakes

**Common Pitfalls to Avoid:**

- Implementing AI without formal policies
- Copying policies without customization
- Excluding frontline clinicians from governance
- Focusing only on technology, not clinical impact
- Ignoring equity and bias considerations
- Underestimating training requirements

**Best Practices:**

- Start with governance before implementation
- Include diverse stakeholders
- Plan for continuous monitoring
- Document everything
- Prepare for failures
- Define meaningful human involvement for the exact intended use rather than using “human oversight” as a slogan

------------------------------------------------------------------------

## Additional Resources

### Regulatory Guidance

- [FDA Artificial Intelligence-Enabled Medical Devices](https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-enabled-medical-devices)
- [HHS Business Associate Guidance](https://www.hhs.gov/hipaa/for-professionals/privacy/guidance/business-associates/index.html)
- [ONC HTI-1 Final Rule](https://www.healthit.gov/regulations/hti-rules/hti-1-final-rule/)
- [ONC HTI-5 Final Rule](https://www.federalregister.gov/documents/2026/10/28/2026-21943/health-data-technology-and-interoperability-onc-deregulatory-actions-to-unleash-prosperity) (removes the HTI-1 source-attribute and risk-management requirements, effective 28 December 2026)

### Professional Organizations

- [American Medical Association AI Principles](https://www.ama-assn.org/system/files/ama-ai-principles.pdf)
- [American Medical Informatics Association Guidelines](https://www.amia.org)
- [Coalition for Health AI (CHAI)](https://www.chai.org/)

### Local Source Collection

- Current institutional medical-staff bylaws and clinical-governance policies
- Privacy, security, incident-response, procurement, and vendor-management standards
- Device records, labeling, quality documentation, and applicable professional guidance

------------------------------------------------------------------------

Questions Governance Teams Ask

### Is ChatGPT HIPAA compliant for hospitals?

HIPAA compliance is not a product-wide yes-or-no label. It depends on the parties’ roles, the data and workflow, configuration, contracts including a BAA when required, security controls, and permitted uses. Institutions must verify the exact service and plan before transmitting protected health information.

### What should be in a hospital AI governance policy?

Core elements include scope, accountable owners, risk classification, evidence review, privacy and security, regulatory status, workflow approval, training, monitoring, change control, incident response, patient communication, and pause or rollback authority.

### Who should be on an AI governance committee?

Membership should match institutional structure and risk. Clinical, nursing, informatics, quality and safety, privacy, security, legal, ethics, equity, operations, procurement, and patient perspectives may be needed, with named decision authority rather than one universal roster.

### How do hospitals report AI adverse events?

Use the institution’s safety and incident system, preserve the product version and output, triage immediate risk, notify accountable owners, investigate the complete workflow, and assess external reporting obligations for the exact device and event. FDA reporting is not automatic for every AI-associated incident.

### What AI vendor requirements should hospitals mandate?

Requirements should be risk- and use-specific. Consider intended use, regulatory records, evidence access, subgroup and allocation analysis, data flows, security controls, change notification, monitoring and audit rights, incident support, responsibility allocation, continuity, and exit.

## The Bottom Line

Key Takeaway

Good AI governance policies:

- Protect patients while enabling innovation
- Maintain physician autonomy
- Ensure transparency and accountability
- Address bias and equity
- Plan for both success and failure

Policies are living controls. They should change when evidence, law, regulation, systems, workflows, or monitored risk changes.

------------------------------------------------------------------------

*These author-created templates are discussion aids and do not establish a universal standard of care.*