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ProcurementHealthcare
AI Procurement Checklist for Healthcare Organizations
A comprehensive 20-point checklist for evaluating AI vendors, including HIPAA considerations and questions to ask.
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AI Procurement Checklist for Healthcare Organizations
This checklist will help healthcare organizations systematically evaluate AI vendors for compliance, security, and operational fit.
Security & Compliance (8 points)
Verify HIPAA Business Associate Agreement (BAA) availability
Confirm SOC 2 Type II certification (request report under NDA)
Check HITRUST CSF certification if applicable
Verify encryption standards (AES-256 at rest, TLS 1.2+ in transit)
Review access control and authentication mechanisms
Confirm audit logging and retention capabilities
Verify incident response and breach notification procedures
Check vulnerability management and penetration testing frequency
Data Privacy (4 points)
Understand data residency options (US-only if required)
Review data retention and deletion policies
Confirm whether PHI is used for model training
Verify de-identification standards if aggregate data is used
Clinical Integration (4 points)
Verify EHR integration capabilities (Epic, Cerner, etc.)
Review clinical workflow integration approach
Understand FDA clearance status if applicable
Confirm clinical validation studies and accuracy metrics
Vendor Viability (4 points)
Research company history, funding, and financial stability
Review customer references in healthcare
Understand support model and SLAs
Review implementation timeline and resource requirements
Questions to Ask
About Security 1. Can you provide your most recent SOC 2 Type II report? 2. How do you handle security incidents involving PHI? 3. What is your patch management timeline for critical vulnerabilities?
About Data 1. Will our data ever leave the United States? 2. Is our data used to train models that serve other customers? 3. How is data segregated between customers?
About Clinical Use 1. What clinical validation has been performed? 2. How do you monitor for model drift in production? 3. What is your approach to algorithmic bias testing?
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