Assessing Attitudes of AI Adoption T e challenges of AI adoption extend beyond infrastructure,
focusing on human behavior and processes. Barriers include not only technical issues like accuracy, bias, and external validity but also workfl ow, legal concerns, and the doctor-patient relationship.11 T ese factors signifi cantly infl uence adoption and end-user attitudes. Addressing these barriers systematically can improve AI adoption and sustainability. Pharmacists can use surveys to identify attitudes and strategies for AI implementation. In South Africa, we applied the Capability, Opportunity, Motivation-Behavior (COM-B) model, which highlights how capabilities, opportunities, and motivation drive behavior change.12
When applied to AI, this could include
skillsets to interpret AI outputs or to select appropriate datasets and ML algorithms. Other factors include workplace climate, patient privacy, ethics, and legal considerations. Furthermore, integrating the Behavior Change Wheel (BCW)13
with the COM-B model aligns evidence-based strategies to overcome barriers. For example, if survey data indicates AI tools do not align with best practices, restrictions could be implemented to maintain compliance. If there are concerns about ethical dilemmas in using AI for patient care, peer-to-peer workshops between pharmacists and AI specialists reviewing case studies could alleviate fears and prepare for future scenarios. In summary, while COM-B identifi es barriers to AI adoption, the BCW provides strategies to address them. T is systematic, evidence-based approach helps pharmacists understand organizational needs and optimize AI use safely and eff ectively.
Recommendations for Integrating AI into Pharmacy Practice Understanding emerging foundational AI issues, recognizing
end-user concerns, and anticipating how AI tools may infl uence these concerns are crucial for pharmacists advancing AI infrastructure. T e following recommendations off er guidance in navigating this process. • Assess and Regularly Evaluate Provider Attitudes: Conduct
needs assessments to gauge pharmacists’ comfort with AI tools, and continuously evaluate perceptions in order to tailor training, address concerns, and mitigate any biases AI algorithms may introduce. • Prioritize Patient Awareness and Respect: Create transparent
policies that help ensure AI integration meets patient needs, respects data privacy, and honors patient preferences. • Establish Best Practices and Set Benchmarks: Develop
best practices based on end-user preferences for safe, effi cient AI use in diagnostics, disease management, robotics, and care transitions. Set measurable benchmarks to track AI adoption and sustainability. • Develop a Strategic Plan and Collaborate with AI Experts:
Collaborate with AI experts to develop an AI adoption plan ensuring accurate tool design, appropriate data use, and alignment of data sources with algorithms to minimize bias.
Conclusion End-user attitudes are closely tied to behavior change, which is
essential for creating policies, education, and initiatives that promote the appropriate adoption and use of AI in clinical decision-making. Implementation science and AI frameworks together can assess
24 Missouri PHARMACIST | Volume 98, Issue III | Fall 2024
provider behavior, create benchmarks, and guide the pharmacy fi eld toward a new care paradigm. Balancing patient safety with the powerful capabilities of AI is essential.
Acknowledgments T is research is supported by the University of Missouri South African Education Program (UMSAEP), a long-standing collaboration between the University of Missouri and the University of Western Cape.14
We acknowledge Ian Coff man, a 3rd year
PharmD student at UMKC School of Pharmacy, for co-authoring the COM-B survey, and Dr. Johan Breytenbach, Associate Professor of Information Sciences at UWC, a principal investigator in this study.
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