Artificial Intelligence Helps Simplify Managed Care Pharmacy Workflow, Processes

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Artificial intelligence and machine learning can significantly benefit managed care pharmacy, specifically in contract reading and interpretation.

Artificial intelligence (AI) can help to simplify workflow in the managed care pharmacy setting, according to a session at the Academy of Managed Care Pharmacy’s Nexus 2023 conference.

Image credit: Shuo | stock.adobe.com

Image credit: Shuo | stock.adobe.com

Jessica Hatton, PharmD, BCACP, associate vice president of Pharmacy at CareSource, and Nick Trego, PharmD, senior vice president of Clinical Analytics and Client Services at HealthPlan Data Solution, discussed AI and machine learning, the differences between them, and how managed care pharmacies can use these technologies to their advantage.

“I think most of us have experienced [voice assistants and chatbots], but they can be used in managed care, specifically in pharmacy, to answer common member questions for member engagement and customer service,” Hatton said in the session.

Further, the presenters discussed other uses that can benefit processes and adjudication claims for managed care pharmacies.

Trego started by addressing the 2 different types of AI: weak and strong. Weak AI was defined as “technology-driven intelligence trained to focus on and perform specific tasks,” including language models and global positioning system navigation. This is the present technology, whereas strong AI includes intelligence that is equal to or surpasses human intelligence, which is currently theoretical.

AI, which is a broad concept of machines that reason or act like humans, encompasses 7 different branches, including computer vision, fuzzy logic, expert systems, robotics, machine learning, neural networks and deep learning, and natural language processing, according to the panel. Trego and Hatton focused on machine learning and how it could relate to managed care pharmacy.

Machine learning encompasses supervised learning, which is when datasets are labeled to identify an existing pattern, and unsupervised learning, which identifies unknown patterns. Furthermore, there is also reinforcement learning, which is reward-based training that identifies the best paths or selections. Machine learning is a type of AI that can extract knowledge from data and learn from it.

Hatton said the AI and machine learning can be applied to the managed care pharmacy, specifically in contract reading and interpretation. She said that contracts are typically very complex and long, containing many amendments.

Currently, the review is manual; however, AI can simplify this process. It can expedite a contract review within minutes compared to days or even weeks when manually reviewed, according to Trego. AI can also be trained to look for specific concepts, such as specialty pricing and generic brand pricing. Furthermore, everything can be housed in single location and organized by date and type, instead of various folders on a computer to simplify finding documents and amendments quicker.

Another process that AI can assist with is monitoring pharmacy claims. Hatton noted that a significant challenge for pharmacies is the amount of claims they receive. There could be hundreds to thousands of claims, and sometimes up to 1 million claims per month, with hundreds of possible fields, according to the panel.

Trego said that AI learning can rapidly re-adjudicate and look at 100% of pharmacy claims in a single system. He said that you are rebuilding the pharmacy benefit through machine learning to help identify claims quickly and review them.

“AI can work in real time kind of behind the scenes there to make sure that if there is an error, it's caught very quickly and avoid repeating unintentional or costly errors in making major changes based on what AI is able to find for you,” Trego said in the session.

He added that another way to help with claims is training AI to identify fraud, waste, and abuse. AI can learn to rule out provider types or prescription types and look at other prescriptions in the same strengths or in the same medications. It can also identify drug-drug interaction trends as they occur in real-time, instead of retrospectively.

AI and machine learning can also help enhance predictive models, which includes combining pharmacy data with medical history, social determinants of health, and other risk factors so pharmacists can increase the quality of care for patients, while reducing financial impacts and adverse events. Furthermore, Hatton said that AI learning can be used for formulary management.

“AI and machine learning are powerful programmable tools that allow users to greatly increase productivity and performance accuracy, and honestly, just make us more efficient and look for things we may not readily see, but the computer catches on our behalf, which then [we] can double check,” Trego said in the session

Reference

Hatton J, Trego N. Can Artificial Intelligence Help Managed Care Plans Make More Sense of Their Pharmacy Data? AMCP Nexus. Orlando, Florida. October 17, 2023. Accessed October 17, 2023.

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