AI Due Diligence in Healthcare M&A: How to Spot HIPAA, State Law, and Vendor Pitfalls

AI Due Diligence in Healthcare Transactions: A Practical Playbook for Buyers

AI is now embedded across healthcare-from clinical decision support and ambient listening to patient messaging and scheduling. Adoption keeps widening, but governance often lags. That gap can translate into privacy exposure, contractual risk, and post-close surprises in mergers and acquisitions.

If you're evaluating a target, AI diligence belongs on the critical path. High-risk use cases that touch protected health information (PHI) raise HIPAA and class action concerns, while even "simple" administrative tools can create data rights and vendor issues if left unchecked.

AI Risks in Healthcare Transactions

Many sellers don't have a full inventory of AI tools, models, or workflows. Some lack a formal governance process or clear approval pathways. That makes it hard to pinpoint risk-and even harder to integrate cleanly after closing.

Buyers should map where AI is used, rate each use case by risk, and validate vendor terms around data ownership, model training, de-identification, security, incident response, indemnities, and audit rights. Where PHI is involved, align use with HIPAA requirements and the organization's risk tolerance.

For reference on HIPAA obligations, see the U.S. Department of Health & Human Services overview of HIPAA rules and guidance here.

State Laws and Evolving AI Regulations

Beyond operational and contractual issues, state laws can affect how AI is deployed. While there isn't a comprehensive federal AI statute, states are moving quickly with targeted rules that touch disclosures, consent, and clinical decision-making.

Two examples worth flagging:

• California's A.B. 489 prohibits AI systems and chatbots that talk directly with patients from implying their advice comes from a licensed clinician. Read the bill.
• Illinois' Wellness and Oversight for Psychological Resources Act bars the use of AI in decision-making for mental health and therapy (with carve-outs for administrative support). Read the act.

Expect more activity in states focused on disclosures, consent for ambient listening, and consumer privacy obligations. If you operate in multiple states, set up a repeatable way to track and operationalize changes.

What Buyers Should Examine During AI Due Diligence

• Oversight and accountability: Identify who owns AI oversight (e.g., governance committee, CIO, Chief AI Officer). Confirm decision rights, escalation paths, and reporting cadence to leadership.
• Governance maturity: Look for a formal AI governance framework or equivalent practices. Verify policies for pilot approvals, model risk tiers, bias monitoring, data validation, and periodic audits.
• Use case inventory: Obtain a complete list of AI tools and models used, developed, or trained by the target. For each, document the use case, data inputs/outputs, PHI involvement, access methods, monitoring, and lifecycle status.
• Clinical risk: Flag higher-risk categories such as clinical decision support, diagnostic assistance, patient monitoring, and ambient listening. Review clinical validation, decision support guardrails, and human-in-the-loop controls.
• Vendor management: Review model cards, risk assessments, and contracts for all third-party AI vendors (including research use). Focus on data rights, model training on your data, de-identification standards, security controls, uptime SLAs, audit rights, indemnities, and breach reporting.
• Data protection and privacy: Confirm HIPAA compliance (including BAAs), minimum necessary data use, role-based access, logging, and retention. Validate de-identification and re-identification controls where claimed.
• Incident history: Ask for open claims, complaints, breach reports, product recalls, regulatory inquiries, and any known model issues (bias, hallucinations, drift) tied to patient safety or privacy.
• Change management: Assess training, user guidance, and safe-use standards for clinicians and staff. Confirm processes for model updates, patching, and decommissioning.

Collaborating Across Legal, IT, and Clinical Teams

Keep legal, privacy, security, IT, operations, and clinical leaders aligned from the start. Legal teams with healthcare privacy and AI experience can pressure-test risk and guide deal terms. IT and clinical leaders can validate real-world use, integration constraints, and patient safety impacts.

Run a shared workstream: legal reviews the contractual and regulatory angle; IT and security validate architecture and controls; clinical teams test workflows and decision safety. Produce a joint risk register with clear mitigation owners and timelines.

Post-Closing Governance and Compliance Strategy

Don't wait for Day 1. Build a post-close plan alongside diligence. Prioritize items that affect patient safety, PHI handling, and continuity of care.

• Contracts: Identify which AI vendor agreements require consent to assign, amendments, or renegotiation. Freeze or sandbox high-risk tools until terms meet your standards.
• Integration: Plan identity/access, data pipelines, logging, and monitoring for each AI tool. Set human oversight checkpoints and rollback options.
• Governance program: Stand up or extend an AI governance framework: risk tiers, approval gates, policy library, model inventory, bias testing, audit schedule, and incident playbooks.
• Training and comms: Provide role-based training for clinicians, front-line staff, and admins. Publish simple do/don't guidelines and escalation contacts.
• Metrics: Track safety events, PHI incidents, model drift, false-positive/negative rates, uptime, and user-reported issues. Review at the executive level.

Suggested 30/60/90-Day Plan

• Day 0-30: Freeze net-new high-risk use cases, complete AI inventory, classify risks, review top 10 vendor contracts, and enable logging for PHI-touching tools.
• Day 31-60: Amend priority contracts, implement access controls, stand up AI approval gates, and launch targeted training for clinical and ops teams.
• Day 61-90: Roll out bias and performance monitoring, finalize incident playbooks, and present a roadmap for deprecating or upgrading risky tools.

Key Takeaways for Healthcare Buyers and Investors

• AI risk is uneven across organizations; assume gaps in inventories, contracts, and governance.
• PHI, clinical decision support, and ambient listening carry higher exposure-treat them as priority reviews.
• State rules are tightening; build a process to track and operationalize changes by jurisdiction.
• A clear post-close plan reduces patient safety risk, privacy exposure, and integration friction.

Resources

• HIPAA: U.S. Department of Health & Human Services
• California A.B. 489
• Illinois Wellness and Oversight for Psychological Resources Act

Further learning

If your teams need structured upskilling on AI governance, vendor evaluation, or workflow design, explore role-based options here: Complete AI Training - Courses by Job.