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AI Governance for Healthcare ​

Overview ​

Healthcare organizations are rapidly adopting AI to improve patient outcomes, reduce administrative burden, and enhance clinical decision-making. However, the high-stakes nature of healthcare—where AI errors can directly impact patient safety—demands rigorous governance frameworks.

SignalBreak provides specialized AI governance capabilities tailored to healthcare's unique requirements for patient safety, clinical validation, regulatory compliance, and ethical AI deployment.

Key challenges this guide addresses:

  • Ensuring AI safety in clinical decision support
  • Complying with HIPAA, FDA, EU MDR, and other regulations
  • Managing AI-related patient safety risks
  • Maintaining audit trails for malpractice defense
  • Balancing innovation with clinical validation requirements

AI Use Cases in Healthcare ​

1. Clinical Decision Support ​

Common applications:

  • Diagnosis assistance and differential diagnosis
  • Treatment recommendation systems
  • Clinical note summarization
  • Drug interaction checking
  • Radiology report generation

AI governance requirements:

  • Clinical validation (does AI improve patient outcomes?)
  • Safety monitoring (false negative/positive rates)
  • Bias detection (ensure equitable care across demographics)
  • Explainability (clinicians must understand AI reasoning)
  • Liability considerations (who is responsible for AI errors?)

SignalBreak support:

  • Monitor LLM providers used for clinical note summarization
  • Track API reliability for real-time decision support systems
  • Alert on model updates that could affect clinical accuracy
  • Document AI provider changes for risk management reviews

2. Medical Imaging & Diagnostics ​

Common applications:

  • Radiology AI (chest X-ray, CT, MRI analysis)
  • Pathology slide analysis
  • Dermatology lesion classification
  • Retinal screening for diabetic retinopathy

AI governance requirements:

  • FDA clearance/approval (if AI is a medical device)
  • Clinical validation studies
  • Ongoing performance monitoring (does AI maintain accuracy?)
  • Radiologist oversight protocols
  • Quality assurance processes

SignalBreak support:

  • Track third-party AI providers used in imaging workflows
  • Monitor for model drift (accuracy degradation over time)
  • Alert on provider incidents that could impact diagnostic accuracy
  • Maintain audit trail for FDA post-market surveillance

3. Administrative Automation ​

Common applications:

  • Prior authorization automation
  • Medical coding (ICD-10, CPT)
  • Claims processing
  • Appointment scheduling
  • Insurance verification

AI governance requirements:

  • HIPAA compliance (PHI protection)
  • Accuracy monitoring (coding errors can lead to claim denials)
  • Business associate agreements (BAAs) with AI providers
  • Audit trails for billing disputes

SignalBreak support:

  • Verify all AI providers have signed BAAs
  • Monitor for policy changes affecting HIPAA compliance
  • Track model updates that could affect coding accuracy
  • Generate evidence packs for payer audits

4. Patient Engagement & Support ​

Common applications:

  • AI chatbots for patient questions
  • Symptom checkers
  • Medication adherence reminders
  • Mental health support bots

AI governance requirements:

  • Medical advice disclaimers (AI is not a substitute for medical care)
  • Crisis detection and escalation (e.g., suicide risk)
  • Health literacy (ensure AI explanations are understandable)
  • Informed consent (patients know they're interacting with AI)

SignalBreak support:

  • Monitor chatbot LLM providers for hallucinations
  • Track policy updates affecting patient communication
  • Alert on provider incidents during critical patient interactions
  • Document AI disclosure practices for regulatory compliance

Regulatory Landscape ​

HIPAA: Privacy & Security ​

Key requirements:

  • PHI protection (encryption, access controls, audit logs)
  • Business associate agreements (BAAs) with vendors
  • Breach notification (if PHI is exposed)
  • Minimum necessary standard (limit PHI access)

How SignalBreak helps:

  • BAA tracking: Verify AI providers have signed BAAs
  • Policy monitoring: Alert when provider privacy policies change
  • Incident response: Track provider security breaches that could affect PHI
  • Audit trail: Document which AI systems process PHI

SignalBreak workflow:

  1. Tag all workflows that process PHI
  2. Verify AI providers have BAAs in place
  3. Monitor for provider security incidents
  4. Generate HIPAA compliance reports for audits

FDA Regulation: AI as Medical Device ​

Key requirements:

  • 510(k) clearance or PMA approval (if AI is a medical device)
  • Clinical validation studies
  • Post-market surveillance (ongoing performance monitoring)
  • Software updates require FDA review (depending on change type)

How SignalBreak helps:

  • Change tracking: Monitor when AI providers update models
  • Performance monitoring: Detect drift that could affect clinical accuracy
  • Post-market surveillance: Generate reports for FDA annual reporting
  • Documentation: Maintain evidence of ongoing safety monitoring

Risk scenario: Your hospital uses an LLM to generate radiology reports. The LLM provider releases a new model version. Is this a "significant software update" requiring FDA notification? SignalBreak alerts you to the change immediately, triggering your change control process.

EU Medical Device Regulation (MDR) ​

Key requirements:

  • CE marking (for medical devices sold in EU)
  • Clinical evaluation reports
  • Post-market surveillance plan
  • Unique device identification (UDI)

How SignalBreak helps:

  • Vigilance reporting: Track AI incidents for regulatory reporting
  • Post-market surveillance: Monitor AI performance continuously
  • Documentation: Generate clinical evaluation reports
  • Change management: Document AI updates for technical documentation

Clinical Validation Standards ​

Key requirements:

  • Prospective or retrospective clinical studies
  • Comparison to standard of care
  • Validation on diverse patient populations
  • Ongoing performance monitoring

How SignalBreak helps:

  • Drift detection: Alert when AI performance degrades
  • Bias monitoring: Track performance across patient demographics
  • Version control: Document which AI version was validated
  • Audit trail: Show continuous monitoring for clinical validation reports

Risks Specific to Healthcare ​

1. Patient Safety: AI-Related Adverse Events ​

Risk: AI incorrectly suggests a diagnosis or treatment, leading to patient harm.

Example scenarios:

  • LLM-based diagnosis assistant misses a life-threatening condition
  • Radiology AI fails to flag a cancerous tumor (false negative)
  • Drug interaction checker doesn't flag a dangerous combination

SignalBreak mitigation:

  • Real-time monitoring: Alert on AI provider outages during critical care
  • Fallback providers: Ensure backup AI systems for critical workflows
  • Change tracking: Document all AI model updates for root cause analysis
  • Incident response: Trigger safety reviews when providers update models

Best practice: Treat AI model changes like medication changes—require clinical validation before deployment.

2. Bias & Health Equity ​

Risk: AI trained on non-representative data performs poorly for underserved populations, exacerbating health disparities.

Example scenarios:

  • Skin lesion classifier trained mostly on light skin, misses melanoma in darker skin tones
  • Symptom checker optimized for English speakers, provides poor guidance for non-native speakers
  • Risk prediction model performs worse for racial/ethnic minorities

SignalBreak mitigation:

  • Provider transparency: Know which AI providers are used for clinical decisions
  • Drift detection: Alert when model behavior changes (could indicate bias)
  • Documentation: Track validation studies that address health equity
  • Audit trail: Demonstrate ongoing monitoring for bias

Example: Your hospital uses an LLM to summarize patient histories. OpenAI updates GPT-4 with new RLHF data that may affect clinical tone. SignalBreak alerts you within 5 minutes. You re-test summaries across diverse patient demographics before deploying the update.

3. Liability & Malpractice ​

Risk: AI error leads to malpractice lawsuit. Who is liable—the clinician, the hospital, or the AI vendor?

Legal considerations:

  • Learned intermediary doctrine: Clinician is responsible for overriding AI advice
  • Product liability: If AI is a medical device, manufacturer may be liable
  • Vicarious liability: Hospital may be liable for AI deployed by employees

SignalBreak mitigation:

  • Documentation: Maintain audit trail of AI governance decisions
  • Evidence for defense: Generate reports showing reasonable care in AI deployment
  • Change tracking: Document rationale for selecting/updating AI systems
  • Clinical validation: Show evidence of ongoing safety monitoring

Example malpractice defense: Plaintiff claims hospital's AI-powered diagnosis assistant caused delayed cancer diagnosis. Hospital's legal team uses SignalBreak evidence pack to demonstrate:

  1. AI was validated before deployment
  2. Clinicians received training on AI limitations
  3. Hospital monitored AI performance continuously
  4. AI provider was selected based on safety record
  5. Fallback procedures were in place

Result: Court finds hospital exercised reasonable care. Case dismissed.

4. Vendor Dependence & Business Continuity ​

Risk: Over-reliance on a single AI provider creates operational risk if the provider fails or discontinues service.

Example scenarios:

  • OpenAI GPT-4 outage takes down clinical note summarization system
  • Anthropic discontinues Claude model used for prior authorization
  • AI vendor goes out of business, leaving no support for critical system

SignalBreak mitigation:

  • Provider diversification: Dashboard shows concentration risk across workflows
  • Fallback configuration: Track which critical workflows have backup providers
  • Outage alerting: Real-time notifications when providers experience incidents
  • Business continuity planning: Document failover procedures

Example: Your EHR uses OpenAI for clinical note summarization. SignalBreak's dashboard shows 80% of AI workflows depend on OpenAI. You configure Anthropic Claude as fallback. When OpenAI experiences a 2-hour outage, your system automatically fails over to Claude, avoiding clinical workflow disruption.

Implementation Guide for Healthcare Organizations ​

Phase 1: Discovery & Risk Assessment (Week 1-3) ​

Objective: Map all AI usage and assess patient safety risks.

Steps:

  1. Identify all AI workflows:

    • Clinical decision support systems
    • Medical imaging AI
    • Administrative automation (coding, scheduling, claims)
    • Patient engagement tools (chatbots, symptom checkers)
    • Research AI (clinical trial matching, literature search)

  2. Assess patient safety risk:

    • Critical (direct patient safety impact):
      • Diagnosis assistance
      • Treatment recommendations
      • Radiology AI
      • Drug interaction checking
    • High (indirect patient safety or compliance risk):
      • Clinical note summarization
      • Prior authorization automation
      • Medical coding
    • Medium (administrative or research):
      • Appointment scheduling
      • Patient education chatbots
      • Literature search
    • Low (no patient/compliance impact):
      • Internal document summarization
      • Email drafting assistance

  3. Configure SignalBreak:

    • Add all AI providers (OpenAI, Anthropic, Azure OpenAI, etc.)
    • Create workflows for each AI use case
    • Map provider bindings
    • Assign criticality ratings

  4. Verify BAAs:

    • Ensure all AI providers processing PHI have signed Business Associate Agreements
    • Document BAA status in SignalBreak

Deliverable: Complete AI model inventory with patient safety risk ratings.

Phase 2: Clinical Validation & Safety Monitoring (Week 4-8) ​

Objective: Establish baseline safety and performance metrics.

Steps:

  1. Clinical validation for critical AI:

    • Review existing validation studies from AI vendor
    • Conduct internal validation on your patient population
    • Test for bias across patient demographics
    • Document validation results

  2. Configure safety alerts:

    • Enable real-time notifications for critical workflows (e.g., diagnosis AI)
    • Set daily digest for high-risk workflows
    • Weekly digest for medium/low risk

  3. Establish fallback procedures:

    • Configure backup AI providers for critical workflows
    • Document failover procedures
    • Train clinical staff on fallback workflows

  4. Create clinical governance policies:

    • AI model change approval process (clinical validation required before updates)
    • Drift detection thresholds (when to trigger safety review)
    • Escalation procedures (who reviews AI incidents)
    • Malpractice defense protocols (documentation requirements)

Deliverable: Clinical validation reports, safety monitoring procedures, governance policies.

Phase 3: Ongoing Monitoring & Continuous Validation (Ongoing) ​

Objective: Maintain patient safety through continuous monitoring.

Daily activities:

  • Review critical signal alerts (provider outages, model updates affecting critical workflows)
  • Triage incidents (determine patient safety impact)
  • Escalate to clinical leadership if needed

Weekly activities:

  • Review digest of all AI signal activity
  • Check for new model deprecations or policy changes
  • Update fallback configurations as needed

Monthly activities:

  • Generate AI safety report for quality committee
  • Review provider concentration risk
  • Test fallback providers (simulate outages)

Quarterly activities:

  • Deep-dive clinical validation review (has AI performance changed?)
  • Update bias monitoring (test across patient demographics)
  • Generate evidence packs for internal audit

Annual activities:

  • Comprehensive AI safety review for board/medical staff
  • Update policies based on lessons learned
  • Regulatory reporting (FDA annual reports if applicable)

Phase 4: Regulatory & Compliance Reporting (As Needed) ​

Objective: Demonstrate AI governance maturity to regulators and accreditors.

Steps:

  1. Generate evidence packs:

    • AI model inventory (all clinical AI systems)
    • Clinical validation reports (safety and efficacy studies)
    • Change history (all AI model updates in last 12 months)
    • Incident response log (how AI failures were handled)
    • Bias monitoring reports (health equity compliance)

  2. Prepare for regulatory inspections:

    • FDA post-market surveillance (if AI is a medical device)
    • Joint Commission survey (patient safety protocols)
    • HIPAA audit (PHI protection with AI vendors)
    • OCR investigation (if breach involves AI provider)

  3. Malpractice defense preparation:

    • Document AI governance framework (policies, procedures, roles)
    • Show continuous safety monitoring (SignalBreak audit trail)
    • Demonstrate clinical validation (safety studies)
    • Evidence of clinician training (AI limitations, override procedures)

Deliverable: Regulatory-ready evidence pack demonstrating robust AI safety governance.

Best Practices ​

1. Treat AI Model Changes Like Medication Changes ​

Recommendation: Require clinical validation before deploying AI model updates, just as you would for a new medication formulary.

Process:

  1. AI provider announces model update
  2. SignalBreak alerts clinical informatics team
  3. Internal testing on de-identified patient data
  4. Clinical validation committee reviews results
  5. Approval required before production deployment

Example policy:

"Any change to an AI model used for clinical decision support must be validated on a test dataset representing our patient population, with results reviewed by the Clinical AI Committee before deployment."

2. Implement "Human-in-the-Loop" for Critical Decisions ​

Recommendation: Never allow AI to make autonomous clinical decisions without clinician review.

Examples:

  • ✅ Appropriate: AI suggests diagnosis, clinician reviews and confirms before documenting
  • ✅ Appropriate: AI flags abnormal finding on imaging, radiologist reviews and finalizes report
  • ❌ Inappropriate: AI automatically orders lab tests without clinician approval
  • ❌ Inappropriate: AI chatbot diagnoses patient and prescribes treatment without physician involvement

SignalBreak role: Document that all critical AI workflows have human oversight requirements.

3. Monitor AI Performance Across Patient Demographics ​

Recommendation: Track AI accuracy, false positive/negative rates, and user satisfaction across age, race/ethnicity, gender, language, and socioeconomic status.

Metrics to track:

  • Accuracy by demographic group
  • Clinical utility (does AI help or hinder clinician workflow?)
  • User trust (do clinicians override AI frequently for certain patient groups?)
  • Patient outcomes (does AI improve care for all populations equally?)

SignalBreak role: Alert when AI provider updates models, triggering re-testing for bias.

4. Maintain AI Inventory for Malpractice Defense ​

Recommendation: Document all AI systems, validation studies, change history, and governance decisions.

Why: In malpractice litigation, plaintiff may claim "hospital deployed unvalidated AI that harmed my client."

Defense requires showing:

  • AI was validated before deployment
  • Ongoing safety monitoring was conducted
  • Clinicians were trained on AI limitations
  • Hospital followed reasonable standard of care

SignalBreak role: Centralized repository for AI governance documentation, easily exportable for legal defense.

5. Require Business Associate Agreements (BAAs) for PHI ​

Recommendation: Any AI provider that processes PHI must sign a HIPAA BAA.

Process:

  1. Identify workflows that process PHI
  2. Confirm AI provider will sign BAA (many LLM providers do NOT sign BAAs for standard API access)
  3. Execute BAA before deploying AI in production
  4. Document BAA status in SignalBreak

Note: OpenAI, Anthropic, and other LLM providers typically do NOT sign BAAs for standard API access. If you need BAA coverage:

  • Use Azure OpenAI (Microsoft signs BAAs)
  • Use AWS Bedrock with Claude (AWS signs BAAs)
  • Deploy self-hosted models
  • Use de-identified data only

SignalBreak role: Track which AI providers have BAAs, alert if provider policy changes affect PHI handling.

Case Study: Academic Medical Center Implements AI Safety Governance ​

Background ​

Organization: Large academic medical center (800 beds, 5,000 physicians) AI usage: 12 AI-powered workflows across radiology, clinical decision support, and administrative automation Challenge: Medical staff concerned about patient safety risks from AI, demanded governance framework

Problem ​

Before SignalBreak:

  • No centralized inventory of clinical AI systems
  • Radiologists unaware when AI models were updated
  • 2-week lag to respond to AI provider incidents
  • No process for clinical validation of AI updates
  • Medical staff threatened to block AI adoption without safety oversight

Incident that triggered action: Radiology AI missed a pulmonary embolism (false negative). Root cause analysis revealed AI model had been updated 1 week prior, but radiology department was unaware. Medical staff demanded answers: "How do we know this AI is safe?"

Solution ​

Month 1: Discovery & Risk Assessment

  • Mapped all clinical AI systems in SignalBreak
  • Assigned patient safety criticality ratings
  • Identified 5 "critical" workflows affecting direct patient care

Month 2: Clinical Governance Framework

  • Established Clinical AI Committee (chief medical informatics officer, radiology chair, quality officer)
  • Created AI model change approval process (validation required for critical workflows)
  • Configured real-time alerts for critical AI systems
  • Documented human-in-the-loop requirements

Month 3: Safety Monitoring Infrastructure

  • Set up daily digests for critical AI workflows
  • Configured fallback providers for radiology AI
  • Established incident response procedures
  • Trained clinical staff on AI limitations

Month 6: Clinical Validation Program

  • Re-validated all critical AI systems on AMC's patient population
  • Tested for bias across demographics (age, race, gender)
  • Published internal validation reports
  • Medical staff approved continued AI use

Results ​

Patient safety improvements:

  • Incident response time: 2 weeks → 5 minutes (1,680x faster)
  • Clinical validation: 0% of AI systems validated → 100% of critical AI validated
  • Fallback coverage: 0% → 100% of critical workflows have backup systems
  • Staff confidence: Medical staff voted to expand AI use after seeing governance framework

Operational outcomes:

  • Zero patient safety events related to AI in 18 months post-implementation
  • Regulatory compliance: FDA annual report submitted on time (post-market surveillance for radiology AI)
  • Malpractice defense: No AI-related lawsuits filed; governance framework cited by risk management as "exemplary"

Business value:

  • ROI: $150K/year (SignalBreak + governance staff time) vs. $2M+ potential malpractice exposure averted
  • Reputation: AMC featured in JAMA article on AI safety governance
  • Recruitment: AI governance program cited by physician recruits as reason to join AMC

Compliance Checklist ​

Use this checklist to assess your healthcare AI governance maturity:

Patient Safety ​

  • [ ] All clinical AI systems identified and inventoried
  • [ ] Patient safety risk assessment completed for each AI workflow
  • [ ] Clinical validation studies conducted before deployment
  • [ ] Human-in-the-loop requirements documented and enforced
  • [ ] Ongoing performance monitoring in place
  • [ ] Bias testing across patient demographics conducted
  • [ ] Incident response procedures documented and tested
  • [ ] Medical staff training on AI limitations completed

HIPAA Compliance ​

  • [ ] All AI workflows processing PHI identified
  • [ ] Business Associate Agreements executed with AI providers processing PHI
  • [ ] PHI access controls documented
  • [ ] Audit logs maintained for PHI access by AI systems
  • [ ] Breach notification procedures include AI provider incidents
  • [ ] Privacy impact assessment completed for clinical AI

FDA Regulation (if applicable) ​

  • [ ] AI systems classified as medical devices identified
  • [ ] 510(k) clearance or PMA approval obtained
  • [ ] Clinical validation studies documented
  • [ ] Post-market surveillance plan implemented
  • [ ] Annual reports submitted to FDA
  • [ ] Software update change control process in place

Clinical Governance ​

  • [ ] AI governance policy approved by medical staff
  • [ ] Clinical AI Committee established
  • [ ] Roles and responsibilities defined
  • [ ] AI model change approval process documented
  • [ ] Drift detection thresholds established
  • [ ] Escalation procedures for AI safety incidents defined

Documentation & Audit Trail ​

  • [ ] All AI governance decisions logged
  • [ ] Clinical validation reports maintained
  • [ ] AI model change history documented
  • [ ] Incident response logs maintained
  • [ ] Evidence packs available for regulatory/legal requests

Frequently Asked Questions ​

Do AI chatbots providing medical information require FDA clearance? ​

It depends. FDA applies "risk-based" approach:

No FDA clearance needed:

  • General health information (e.g., "What is diabetes?")
  • Administrative tasks (appointment scheduling, billing questions)
  • Wellness advice (diet, exercise tips)

FDA clearance likely required:

  • Diagnosis (e.g., "You have Type 2 diabetes based on your symptoms")
  • Treatment recommendations (e.g., "You should take metformin")
  • Triage decisions (e.g., "Go to ER now" vs. "See your doctor next week")

Gray area (consult FDA):

  • Symptom checkers that suggest possible conditions
  • Mental health chatbots that detect crisis situations

SignalBreak's role: Document your analysis of whether AI requires FDA clearance. Track AI provider updates that could change regulatory status.

How do we handle AI "hallucinations" in clinical settings? ​

Challenge: LLMs can generate plausible-sounding but factually incorrect clinical information.

Mitigation strategies:

  1. Human-in-the-loop: Require clinician review of all AI-generated clinical content
  2. Fact-checking: Cross-reference AI output with authoritative sources (UpToDate, clinical guidelines)
  3. Disclaimers: Clearly label AI-generated content and remind clinicians to verify
  4. Training: Educate clinical staff on AI limitations and hallucination risk
  5. Monitoring: Track instances where clinicians override AI recommendations

SignalBreak role: Alert when LLM providers update models (may change hallucination frequency), trigger re-validation.

Can we use OpenAI or Anthropic APIs for PHI? ​

Not directly. OpenAI and Anthropic's standard API terms do NOT include HIPAA Business Associate Agreements.

Options for HIPAA compliance:

  1. Azure OpenAI Service: Microsoft signs BAAs, can deploy GPT-4 in HIPAA-compliant environment
  2. AWS Bedrock: AWS signs BAAs, can use Claude via Bedrock
  3. De-identified data only: Remove all 18 HIPAA identifiers before sending to standard APIs
  4. Self-hosted models: Deploy open-source LLMs (Llama, Mistral) on your own infrastructure

SignalBreak role: Track which AI providers have BAAs, alert if you're using non-BAA providers for PHI workflows.

What if an AI provider experiences a security breach? ​

Response checklist:

  1. Determine if PHI was exposed: Did AI provider have access to identifiable patient data?
  2. Notify patients if required: HIPAA breach notification (60 days for 500+ patients)
  3. Notify regulators: OCR (HIPAA), state attorneys general if applicable
  4. Document response: Maintain audit trail for potential litigation
  5. Review vendor relationship: Should you continue using this AI provider?

SignalBreak role: Real-time alerts on AI provider security incidents, documented timeline of when you learned of breach (critical for breach notification deadlines).

How do we validate AI that we don't control (e.g., GPT-4)? ​

Challenge: You can't inspect OpenAI's training data or model architecture.

Validation approach:

  1. Black-box testing: Test AI performance on your patient population

    • Accuracy metrics (sensitivity, specificity, PPV, NPV)
    • Clinical utility (does AI help clinicians make better decisions?)
    • Bias testing (performance across demographics)

  2. Ongoing monitoring: Detect when AI performance changes

    • Use SignalBreak to alert on model updates
    • Re-test after updates
    • Track real-world performance (clinician override rates)

  3. Comparative validation: Compare AI to standard of care

    • AI + clinician vs. clinician alone
    • AI vs. alternative AI vendors

  4. Document reliance: Clearly state that you rely on vendor's development practices

    • Reference vendor's responsible AI commitments
    • Document vendor's safety track record

Regulatory acceptance: FDA increasingly accepts black-box validation for third-party AI, provided you demonstrate ongoing performance monitoring.

Next Steps ​

Getting Started with SignalBreak ​

  1. Sign up for trial: https://signalbreak.com/trial (healthcare organizations receive extended trial)

  2. Complete discovery:

    • Map all clinical AI systems
    • Assign patient safety risk ratings
    • Add AI providers to SignalBreak
    • Verify BAA status for PHI workflows

  3. Configure safety alerts:

    • Enable real-time notifications for critical clinical AI
    • Set daily digest for high-risk workflows
    • Integrate with clinical communication systems (EPIC inbox, Teams)

  4. Establish clinical governance:

    • Create Clinical AI Committee
    • Adopt AI model change approval policy
    • Document human-in-the-loop requirements
    • Train clinical staff on AI safety

  5. Generate baseline evidence pack:

    • Go to Dashboard → Reports → Generate Evidence Pack
    • Review with quality committee and risk management
    • Customize for your regulatory framework (FDA, Joint Commission, state licensing boards)

Additional Resources ​

Contact ​

Industry Partnerships ​

SignalBreak partners with leading healthcare organizations:

  • CHIME (College of Healthcare Information Management Executives)
  • AMIA (American Medical Informatics Association)
  • HIMSS (Healthcare Information and Management Systems Society)

Last updated: 2026-01-26