Presentation (CTI)

OBJECTIVES: Artificial intelligence (AI) is reshaping US payer decision making and has been increasingly used in population health analytics and formulary/utilization management. This research aims to assess how payers currently leverage AI in their decision making and explore how they anticipate using AI in the future.
METHODS: A qualitative, web-based survey was fielded via the Rapid Payer Response^TM online portal (RPR^®) to 14 current US payers, including pharmacy directors (PDs) and medical directors (MDs) from commercial managed care organizations, PDs and MDs from Medicaid managed care and Medicare Advantage plans, pharmacy benefit managers (PBMs), and integrated delivery network payers (IDNs).
RESULTS: All 14/14 payers (100%) believe that AI can be used effectively to enhance payer decision making. The main benefits include operational efficiencies with time and cost savings, enhanced data analysis for claims, formulary management, and contracting, fraud/abuse detection, personalization of member engagement, and improved consistency for PA and UM decisions. Key challenges include regulatory/compliance hurdles, potential bias, quality control issues, need for training to improve workforce readiness, technological inertia, and unclear cost-to-ROI. Most surveyed payers (72%) report that their organization is currently in an exploration or partial implementation stage of AI adoption, and 64% anticipate wide implementation/full integration within the next 3 years. Payers perceive that AI capabilities are rapidly improving, making wider implementation feasible. Future use cases will involve efficiency and process improvement, decision support, risk management, strategic expansion, and maintaining/driving competitive advantages.
CONCLUSIONS: Payers are already piloting AI capabilities and expect increased integration as capabilities mature and valuable use cases are demonstrated. They are optimistic about the efficiency and decision-support that AI will offer but remain cautious about the current limitations around quality control and initial investment of time and resources to implement operational changes. Adoption will accelerate with validation, bias mitigation, regulatory clarity, and governance.