Behavioral Health Medical Director — AI Policy & Governance

We’re building a world of health around every individual — shaping a more connected, convenient and compassionate health experience. At CVS Health®, you’ll be surrounded by passionate colleagues who care deeply, innovate with purpose, hold ourselves accountable and prioritize safety and quality in everything we do. Join us and be part of something bigger – helping to simplify health care one person, one family and one community at a time.

Behavioral Health Medical Director — AI Policy & Governance

Aetna, a CVS Health Company, is one of the oldest and largest national insurers. That experience gives us a unique opportunity to help transform health care. We believe that a better care system is more transparent and consumer-focused, and it recognizes physicians for their clinical quality and effective use of health care resources.

Role summary: The Behavioral Health (BH) Medical Director (MD) for AI Policy & Governance provides clinical leadership to ensure artificial intelligence (AI)—including predictive models and generative AI—is designed, validated, implemented, and monitored in ways that are clinically sound, ethical, safe, compliant, and equitable
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This role partners closely with enterprise AI governance bodies and risk stakeholders to align BH AI use cases to the company’s AI Governance Policy and related AI standards (including AI risk assessment and AI training expectations).

As part of the BH Clinical Quality team, a successful candidate will partner and work closely with a team of colleagues focused on policy as well as internal and external quality assurance.

Primary responsibilities

• Clinical governance for AI in BH

  --Provide clinical oversight for AI-enabled workflows impacting BH (e.g., risk stratification, care navigation, documentation support, utilization management support)

  --Establish clinical acceptability criteria (clinical validity, safety, workflow appropriateness) and ensure appropriate clinician-in-the-loop controls where needed

  --Establish criteria for AI vendor review in partnership with critical business partners to establish viability of proposed vendor

• AI policy, standards, and risk alignment

  --Translate enterprise AI governance requirements into BH–specific guardrails, decision pathways, and clinical review checkpoints aligned to the AI Governance Policy and the AI System Risk Assessment Standard

  --Partner with governance stakeholders to ensure AI tools/use cases complete required reviews and approvals (including use-case risk assessment and any associated controls)

  --Quality, safety, and outcomes monitoring - define clinical performance measures (effectiveness, safety signals, unintended consequences) and operational KPIs for BH AI solutions

  --Establish monitoring plans for model drift, changes in clinical practice guidelines, and emerging risk signals; recommend retraining, recalibration, or retirement when appropriate

  --Establish payment policy in partnership with network partners for AI supported clinical interventions within a provider/vendor partnership

• Equity, ethics, and responsible use

  --Identify and mitigate BH–specific risks such as bias, stigma reinforcement, access inequities, and potential harms from automation

  --Ensure responsible use principles are embedded throughout the lifecycle, from concept through deployment and ongoing monitoring

  --Ensure equitable performance across populations (e.g., depending on how model was trained, training data may not always be representative), identify and mitigate algorithmic bias in care decisions; align AI deployment with organizational values and patient trust

• Data stewardship partnership

  --Collaborate with Enterprise Data & AI Governance partners to ensure behavioral health AI initiatives uphold privacy, security, and ethical data practices, and align to enterprise guardrails and support models

  --Assess data quality, design and interpret pilots, A/B testing

• Clinical enablement and change management

  --Support clinician adoption through clear clinical guidance, training expectations, and workflow integration consistent with the organization’s AI training approach

  --Partner with operational leaders to ensure AI supports (rather than…