Applications of Artificial Intelligence in Pharmacy Randy Hoggle Advasur Optimization of Artificial Intelligence in Pharmacy
J. Randall Hoggle, B.Pharm., D.Ph., M.B.A.

Many external factors control the potential of the application of artificial Intelligence (AI) for pharmacies in patient care, business applications, and regulatory compliance programming.

Today’s burgeoning AI software development and commercialization is being predominantly targeted for language models with immediate applications in communication and language skills. Additionally, today’s AI software development and commercialization initiatives are also targeted for analyzing complex data, imagery, and spatial capabilities for analysis of data and its aggregated enhancement into value-centric applications to provide information.

In the Washington Post article entitled “How will AI affect your job?” published May 14, authors Yan Wu and Sergio Pecanh a discussed two premises that will determine the speed, depth, and breadth of AI in multiple industries. The first premise is that “most jobs will be affected but all jobs will not be affected at the same rate.”This premise is echoed by the OpenAI researchers’ approximation that 80% of the U.S. workforce would have its workload tasks impacted by at least 10% by the language models. The more manual the work, the less AI initially will assist.

But where manual work or redundant work is performed, then AI communication and language skills start to be applicable. For pharmacies this premise would then more likely apply to prescription intake, processing prescription filling, communication with patients, prescribing parties, and payers’ data related to individual prescriptions.

The second premise advanced by Wu and Pecanha is that complex data, imagery, and spatial analytics could be analyzed more quickly, and patterns developed that could expedite such activities as supply chain product distribution, central-fill functions, and regulatory compliance analysis. Factors that will reduce the speed of implementation in an industry are barriers to adoption. For pharmacy, the barriers to AI adoption include five government agencies having oversight over pharmaceutical product development, manufacturing, supply chain distribution, pharmacy practice, and pharmacy compliance, with pharmacy having the further limitations of data aggregation and analysis due to Health Insurance Portability and Accountability Act (HIPAA) privacy compliance restrictions.

However, pharmacy teams should optimize AI for patient outreach and reporting capabilities as soon as possible, due to the workforce limitations most of you are working under now. Similarly, pharmacies can use AI to expand vaccination programs, increase development of combination practice capabilities to broaden the practice areas, and grow the net revenue to stabilize or grow the business.

The authors referenced above, and many others who cover AI, try to answer the question of “How long before a machine takes over my job and our business”? But most human resources planning experts hesitate to predict the speed at which AI will be implemented. For pharmacy, like many other careers, this will not be the first time new technology has changed how we work.

Most healthcare worker AI occupational exposure data reports have not referenced pharmacy practice yet, and thus we would recommend pharmacy teams participate in surveys of tasks where you would like to see AI assistance developed. The evidence is already available on the value of AI in patient care and safety. These are immediate opportunities to use several AI software packages to address communication and patient follow-up for medication adherence checks and required regulatory compliance reporting requirements. CT

J. Randall Hoggle, B.Pharm., D.Ph., M.B.A., is the managing director of Advasur, LLC, the Advasur Audit & Supply Chain Resource Center in Rockville, Md. The author can be reached at


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