At AMCP Nexus, a session highlighted real-world examples of successful implementation of artificial intelligence (AI) and machine learning (ML) in managed care. AI has various applications in managed care pharmacy, including managing new initiatives and monitoring pharmacy claims. During the session at the Academy of Managed Care Pharmacy (AMCP)'s annual Nexus meeting, practical uses of AI and ML in managed care pharmacy were showcased, along with successful implementation examples. Nick Trego, PharmD, Senior Vice President of Clinical Analytics and Client Services at HealthPlan Data Solutions, provided an overview of AI and ML, highlighting their differences and discussing various technologies that regularly utilize AI, such as personal assistants (e. g. , Apple's Siri), chatbots (e. g. , ChatGPT), and smart home devices (e. g. , smart thermostats). Trego explained that AI is like physics, encompassing the theory and methods, while machine learning is an application of AI, comparable to Newton's law—a subset of AI but not the entirety. Jessica Hatton, PharmD, BCACP, Associate Vice President, Pharmacy at CareSource, discussed major applications of AI and ML in managed care pharmacy. For instance, AI can be used for: 1. Enhancing prescription authorization and approval processes, 2. Optimizing medication therapy management programs, 3. Identifying medication adherence issues and providing interventions, 4. Predicting patient outcomes and supporting risk assessment. Furthermore, the session included the presentation of four real-world case studies demonstrating the use of AI/ML in managed care practices. These case studies highlighted the problems addressed by the technologies and their overall impact on practice efficiency.

Here are some key examples: 1. ML model for insulin claim adjudication: The model autonomously identified errors in insulin claim adjudication at the invoice level. It ensured compliance with state laws regarding insulin co-pay caps, corrected co-pay errors, facilitated direct returns of overcharged funds to members, and implemented proper coding to prevent future errors. 2. Monitoring National Average Drug Acquisition Cost (NADAC) pricing: An AI/ML model tracked the implementation of NADAC pricing methodology for independent pharmacies, identifying reimbursement discrepancies and avoiding costs of up to $1 million while mitigating risks associated with program implementation. 3. Monitoring new current exposure method limit: An AI/ML model supervised adherence to a new plan design's current exposure method limit on a National Drug Code (NDC) level. It achieved over $25, 000 in plan savings, ensured fruitful adherence to plan designs, and established an alert system for high-cost NDCs to prevent future errors. 4. COVID-19 testing mandates: An ML model monitored reimbursement discrepancies related to new COVID-19 testing logic. Although the model did not detect any errors, it flagged abnormal utilization by one pharmacy, ensuring compliance with the COVID-19 initiative and minimizing waste. While acknowledging the transformative potential of AI and ML in managed care, Trego highlighted concerns about model accuracy, citing instances where AI technologies, like ChatGPT, have been known to provide incorrect information. He recommended having a human double-check the AI/ML model to ensure it functions as intended. Trego emphasized that AI does not replace human intelligence and encouraged double-checking to ensure system performance aligns with expectations. In other presentations at various medical conferences, topics ranged from the cultural limitations of personality assessment for third-party reproduction to the imperative of focusing on the prevention of atrial fibrillation in cardiovascular disease prevention. Additionally, discussions covered the global impact of unplanned pregnancies, solutions for promoting equitable health and well-being through public health policies, the high costs associated with cancer care, and the latest advancements and challenges in reproductive health at the ASRM 2023 Scientific Congress & Expo in New Orleans.