National Medical Director, Clinical AI Innovation and Performance - Remote

UnitedHealth Group•Tampa, FL

2d•$386,500 - $579,500•Remote

About The Position

UnitedHealthcare is modernizing clinical programs, starting with Utilization Management, by leveraging AI, real-time data, and a modern platform. The Medical Director, Clinical AI Innovation and Performance is a senior physician leader responsible for the clinical AI model training, testing, and surveillance. This role will initially focus on utilization management (UM) clinical criteria, utilizing InterQual, UHC custom specialty content, and other evidence-based criteria. The Medical Director will establish efficient and consistent annotation, training, and testing models to ensure medical policies are accurately interpreted, evidence-based, consistently applied, and operationally effective. This role serves as the clinical authority for guideline interpretation, clarification, and enhancement, ensuring that approved medical policy and supporting evidence are translated into UM clinical criteria that enable appropriate coverage decisions, support safe and effective care, and scale across enterprise operations. The initial focus will be on Prior Authorization and will expand to include Inpatient Level of Care, Clinical Care Management, and Comprehensive Medication Management. The clinical annotation, training, and testing team will report to this physician. The Medical Director will be the primary clinical liaison to Foundation model clinical teams (Anthropic, OpenAI, Gemini). The Medical Director will work closely with the CMO Medical Policy, the CMO Value Creation, the CMO of Medical Management, and the MD, Data Science. The Medical Director will also be a key member of the UCS clinical and AI leadership team, collaborating on clinical program design, data models, AI use, and modernization.

Requirements

MD or DO with active/unrestricted U.S. Medical Licensure
Board certification in an ABMS/AOBMS Clinical specialty
Significant experience in utilization management
Significant experience in coverage interpretation using UM clinical criteria
Significant experience in clinical operations in large, complex organizations
Demonstrated experience working with InterQual clinical criteria
Experience with UM physicians and nursing teams at scale
Proven ability to lead through influence across clinical, operational, and technical teams

Nice To Haves

Experience across Commercial and/or Government program UM
Experience working with MCG guidelines
Proven direct responsibility for UM operations leadership
Proven exposure to AI enabled clinical workflows or advanced decision support (technical expertise not required)
Recognized clinical authority with strong peer credibility
Demonstrated judgment in complex or ambiguous clinical scenarios
Able to operate at enterprise scale while maintaining clinical rigor
Comfortable providing clinical guidance on AI use in high impact workflows
Trusted advisor to executive leadership across UCS and UHC

Responsibilities

Maintain deep expertise in InterQual criteria, evidence framework, and decision logic.
Ensure consistent, accurate interpretation of InterQual criteria across services, settings, levels of care, reviewer types, and UM workflows.
Lead guideline clarification, customization, and enhancement to improve clinical clarity and consistency, address operational ambiguity or recurring edge cases, and enhance the ability to ensure care is safe and effective.
Develop and approve evidence-based enhancements or supplemental criteria that add clinical clarity while remaining aligned with approved medical policy.
Work closely with leaders in Medical Policy and evidence teams to ensure alignment with policy intent and clear translation of policy into UM-ready clinical criteria.
Ensure UM clinical criteria are explicitly grounded in clinical evidence and accepted guidelines, structured for consistent application at scale, and clear and actionable for nurses, physician reviewers, and AI-enabled workflows.
Apply senior clinical judgment in situations where evidence is evolving or limited, or criteria require interpretation to ensure appropriate coverage decisions.
Promote consistent clinical standards across UM operations while recognizing the need for appropriate clinical flexibility.
Ensure UM criteria function effectively in production environments, supporting timely and appropriate coverage decisions, reducing unwarranted variation across reviewers, and aligning clinical interpretation with operational reality.
Partner closely with UM Operations and Nursing leadership to identify areas of confusion or friction in criteria application, address recurring themes from operational feedback, and improve reviewer confidence and consistency.
Lead teams responsible for clinical annotation and analysis supporting LLM-based UM decision support.
Oversee the clinical review process that ensures accurate interpretation of UM criteria by LLMs and reliable assessment of whether clinical documentation meets criteria.
Provide clinical oversight and judgment where annotator disagreement exists, criteria vary in complexity or trainability, or additional training data or clarification is required.
Ensure annotation and training processes support clinically accurate, reliable model performance.
Serve as a senior physician leader within UCS-led AI governance, under the authority of the UHC CMO.
Participate in reviews across AI lifecycle phases: training and validation, shadow mode, AI assist (clinical in the loop), and production deployment.
Contribute clinical judgment to evaluations of precision and recall, clinical accuracy and safety, and readiness for use in automated approvals.
Identify areas where additional training, refinement, or safeguards are required.
Directly manage clinical annotators and clinical or UM analysts supporting AI enablement and criteria oversight.
Work closely with Medical Policy leadership, the CARES team conducting rigorous UM case review and quality evaluation, UM Operations and Appeals, and Product, ECS, and AI/data science teams.
Ensure insights from production, appeals, audits, and quality reviews inform guideline clarification and enhancement, reviewer guidance, and ongoing improvement of AI model performance and safety.