AI
AI Adoption in Pharmacy Practice: A Call for Evaluation Strategies
By Mark E. Patterson, PhD, MPH1 , Johan Breytenbach, PhD2
1: University of Missouri-Kansas City School of Pharmacy, Kansas City, MO USA. 2: University of Western Cape, Cape Town, South Africa, Dr. Johan Breytenbach, Senior Lecturer, Department of Information Systems, Economic and Management Sciences, University of the Western Cape, South Africa
Introduction algorithms eff ectively detect arrhythmias from ECGs,5 A
rtifi cial intelligence (AI) and machine learning (ML) off er valuable tools for pharmacists in identifying drug-drug interactions and assessing medication safety and effi cacy.1
treatments, and risk prediction.2
AI also supports clinical decisions for diagnoses, While AI may not always provide precise
treatment recommendations, it consistently matches human experts with 90- 100% diagnostic accuracy and oſt en outperforms clinicians.3,4
retinal diseases via imaging,6
COVID-19 pneumonia in chest x-rays.7 Despite AI’s benefi ts, its adoption in clinical decision-making faces signifi cant legal, societal, and ethical challenges,8
particularly regarding decision transparency, patient
safety, and clinician autonomy. Policies for AI in healthcare are still evolving, and AI’s eff ectiveness for clinical decision-making depends heavily on data quality, systems, and algorithms.9
can introduce bias into clinical decision-making.10
range of issues requires suffi cient expertise, high-quality data, and appropriate skills. Given these challenges, clinicians
may view AI with a mix of optimism and caution, weighing its benefi ts against potential risks. Recognizing these perspectives is essential, and implementation science tools can be used to evaluate end-user perspectives necessary for informing AI policies and best practices. A collaboration between UMKC’s Dr. Mark Patterson, UMKC 3rd year pharmacy student Mr. Ian Coff man, and Dr. Johan Breytenbach of the University of the Western Cape are currently using implementation science surveys to evaluate AI attitudes in South Africa, an approach which could similarly be used in the pharmacy practice setting.
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Furthermore, AI trained on datasets that do not refl ect the target population Addressing these
For instance, ML and
Mark is an Associate Professor at UMKC School of Pharmacy, where he teaches
pharmacoeconomics, epidemiology, and managed care. As a PhD-trained health
services researcher, Mark uses implementation science to conduct program evaluations.
He currently collaborates with the Missouri Pharmacy Association on the Pharmacy
Vaccination Gap Closure initiative and with Dr. Breytenbach at the University of the
Western Cape to evaluate attitudes toward AI adoption in decision-making.
IN Pharmacy
”Implementation science and AI frameworks
together can assess provider behavior, create benchmarks, and guide the pharmacy
field toward a new care paradigm. Balancing patient safety with the
powerful capabilities of AI is essential.”
Johan is a senior lecturer and researcher in the Information Systems department at the
University of the Western Cape, Cape Town, South Africa. His research areas are system
design focused on AI and Machine Learning systems, the impact of platform systems,
distributed ledger systems (block-chains), mobile technologies on business models,
and preparing traditional businesses for the digital economy. He currently collaborates internationally on AI adoption projects, including work with Dr. Patterson from the UMKC School of Pharmacy.
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