Navigating the AI Landscape in Healthcare Transactions: A Due Diligence & Governance Guide
Artificial intelligence (AI) is rapidly transforming healthcare, offering amazing potential but also introducing complex legal and operational risks, especially within mergers and acquisitions (M&A). As a healthcare buyer or investor, understanding these nuances is crucial for a prosperous transaction. This guide provides a complete overview of navigating AI-related due diligence and building a robust post-closing governance strategy.
The Rising Importance of AI Due Diligence
Healthcare transactions increasingly involve targets utilizing AI in various capacities – from diagnostics and treatment planning to administrative tasks and patient engagement. Failing to thoroughly assess these AI implementations can expose your organization to meaningful risks. New legislation is emerging, like California’s A.B. 489 [1] and Illinois’ H.B. 1806 [2], signaling a heightened regulatory focus on AI in healthcare.
Therefore, a dedicated AI due diligence process is no longer optional; it’s essential.
Assembling Your AI Due Diligence Team
Effective AI due diligence requires a multidisciplinary approach. You’ll need to leverage expertise from across your organization:
* Legal counsel: Specialized in healthcare,data privacy,security,and AI law. They’ll assess legal risks, review contracts, and provide guidance on compliance.
* IT & Operations Teams: To evaluate the technical integration of AI tools, assess data infrastructure, and identify potential operational disruptions.
* Clinical Teams: To analyze the impact of AI on patient care, quality of care, and clinical workflows.
* External advisors: Bringing specialized AI expertise to supplement internal resources and provide an objective evaluation.
Your legal counsel should coordinate the entire diligence review process, ensuring a cohesive and comprehensive assessment.
Key Areas of Focus During AI Due Diligence
Here’s what your team should investigate during the due diligence phase:
* AI Applications: Identify all AI tools used by the target – including their purpose, functionality, and integration points.
* Data Governance: scrutinize data sources, data quality, data security measures, and compliance with privacy regulations (HIPAA, GDPR, state laws).
* Vendor Agreements: Review contracts with AI vendors, paying close attention to data usage rights, liability clauses, and termination provisions. Can these agreements be assigned post-closing?
* Model Validation & Bias: Assess how the target validates its AI models for accuracy, fairness, and potential bias. Bias can lead to disparate outcomes and legal challenges.
* Regulatory Compliance: Ensure the target’s AI implementations comply with all applicable regulations, including emerging AI-specific laws.
* Intellectual Property: Determine ownership of AI algorithms, data sets, and related intellectual property.
* Cybersecurity: Evaluate the security measures protecting AI systems and data from cyber threats.
Building a Post-Closing AI Governance and Compliance Strategy
Don’t wait until after the deal closes to address AI risks. Proactive planning is vital.
Consider these steps:
- AI Use Survey: If you don’t already have one, conduct a comprehensive survey to map all existing and planned AI applications within your organization.
- Formal AI Governance Strategy: Develop a formal strategy encompassing:
* Data Protection & Access Controls: Implement robust controls to protect sensitive data used by AI systems.
* Long-Term Compliance: Establish processes for ongoing monitoring and adaptation to evolving regulations.
* streamlined Assessment: Create a framework for evaluating and adopting new AI tools.
* Vendor Management: Develop standardized procedures for negotiating and managing AI vendor contracts (see sheppard Mullin’s insights [3]).
* Oversight & Monitoring: Implement continuous monitoring of AI performance, accuracy, and potential bias.
- Integration Planning: Carefully plan the integration of the target’s AI tools with your existing systems and governance framework.
- Patient care & Safety: Prioritize patient safety and quality of care throughout the integration process.
Key Takeaways for Healthcare Buyers and Investors
AI presents both opportunities and challenges in healthcare transactions. Thorough due diligence,coupled with a proactive governance strategy,is paramount.
Remember:
* AI is a dynamic field. The legal and regulatory landscape is constantly evolving.
* Expertise is essential. Don’t underestimate