AI


By Xiaomo (Shawn) Xiong, MS, PhD, Assistant Professor Affi liation: James L. Winkle College of Pharmacy, University of Cincinnati, Cincinnati, OH


F


ollowing the launch of ChatGPT around the end of 2022, Artifi cial Intelligence (AI) has quickly become a buzzword in modern life. I still remember the excitement back


in December 2022, when I fi rst encountered ChatGPT––it felt like an early Christmas giſt . Although AI has come onto the public horizon mostly recently, it is not an entirely innovative invention. T eir origins can be traced back as early as the 1950s, yet only recently have we begun to use these tools so extensively. Today, AI is applied across almost all industries, with healthcare and pharmacy being one of the notable areas. In the realm of pharmacy, AI has evolved


from basic automation to advanced roles in drug discovery, personalized medicine, and pharmacovigilance. T ese advances refl ect the growing role of AI in improving health outcomes and enhancing clinical decision- making process. In particular, as a researcher by training and with roots in health economics and outcomes research, I have observed how AI is being incorporated into health technology assessments (HTA) and clinical settings, which aims to support more precise and informed decision-making. I would say one of the most signifi cant


contributions of AI to HTA lies in pharmacoeconomic evaluations, particularly in application of cost-eff ectiveness analysis


(CEA). Traditional CEA models oſt en rely on static assumptions and limited clinical trial data. In contrast, AI is able to process large amounts of real-world data, which provides dynamic insights that can lead to more precise assessments of healthcare interventions. For example, AI can predict drug pricing trends by analyzing historical pricing data and market conditions. T is can certainly help establish dynamic cost inputs more accurately, which is a challenge that health economists continue to face. Also, it can help evaluate healthcare utilization by learning patient behavior and treatment patterns, which can be further used to predict which patients are likely to require additional services. Furthermore, AI can assess the cost-eff ectiveness of personalized medicine by simulating patient responses to tailored treatments, which helps healthcare providers make more individualized and value-driven decisions. Compared to the traditional CEA models, all of these features provided by AI can help us better understand the underlying treatment value, which would further result in better balanced decisions that prioritize patient outcomes without compromising economic sustainability. So, in an era of rising healthcare costs,


the ability of AI to enhance HTA and pharmacoeconomics evaluations is crucial to support the long-term viability of our healthcare systems.


Xiaomo (Shawn) Xiong, Ph.D., is an Assistant Professor the Division of Pharmacy


Practice and Administrative Sciences at the University of Cincinnati College of Pharmacy. His primary research interests include health outcomes research using real-world data and pharmacoeconomic modeling. He is extensively experienced in working on diff erent types of real-world data, including claims data, survey data, and electronic


health records data. In addition, he has vast pharmacoeconomic modeling experience using Microsoſt Excel, R, and TreeAge in studying a wide range of diseases, including cancer, rheumatoid arthritis, and Alzheimer’s disease.


THE LEADING VOICE FOR THE MISSOURI PHARMACIST | MoRx.com 27 ”AI-powered


clinical decision support systems -CDSS-)Can assess


the risk of adverse drug reactions by


cross-referencing the genetic profile and treatment histories of a patient.”


IN Pharmacy


The Role of AI in Pharmacy: From Health Technology Assessment to Clinical Decision


Taking a broader view, in clinical


pharmacy, AI is revolutionizing decision- making process through AI-powered clinical decision support systems (CDSS).


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