Increasing workloads and resultant stresses on pharmacists have led to well-publicized staffing shortages and walkouts in the past year. The labor demands of traditional pharmacy have remained largely unchanged at a time when its role has expanded in the volume and types of therapies, across sites of care and menu of ancillary services. Through emerging artificial intelligence (AI) tools, pharmacies will enhance their operational capabilities across limited labor and better allocate pharmacist time toward potential improvements in not only productivity, but patient outcomes.  Target analysis in this environment requires an understanding of not only the emerging AI market and its applications, but federal and state regulatory and legislative trends shaping this emerging technology. Herein, we explore the opportunities and regulatory considerations of AI-integrated solutions in pharmacy.

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I. The Traditional Pharmacy Model Faces Efficiency and Complexity Challenges

Pharmacy still relies heavily on manual process and human expertise at a time when its role has expanded to a wider selection of therapies, across sites of care and engaging a growing library of ancillary services. The growth of specialty medications have added complexity to the pharmacy space while concurrently raising the financial stakes.  Home, infusion-suite and center-based pharmacy site logistics, as well as white-bagging, have increased supply chain complexity just as cold-chain has become more important. Manufacturer hub services increasingly demand seamless collaboration and communication with pharmacy. Together, the impact on labor is evident in recent walk outs by large chain store pharmacists over stress, burden and working conditions.

AI is being utilized to improve these processes beyond human resource limitations of involvement and analysis. Whereas traditional automation is rule-based and follows predetermined instructions, AI is designed to operate with varying levels of autonomy to sort and interpret data and generate predictions, recommendations, or decisions.

AI will increasingly allow pharmacies to be more efficient, accurate and personalized, while better allocating pharmacist time. The elements of Table 1, presented below and described subsequently, summarizes an array of potential application from prescribing to patient engagement, to enhanced care and data analytics in continuous improvement.

Table 1: Emerging Applications Of AI In Pharmacy


II. Improving Prescriber Accuracy With AI

AI-driven decision support systems not only have the potential to reduce medication error but improve clinical judgement, assisting the pharmacist in selecting the right drug and dosage, that minimizes adverse events. This includes complex multivariable analysis that allows the pharmacist to screen for potential drug-drug interactions – an especially prescient consideration given the increasing frequency of polypharmacy. AI can also screen for potential drug–disease interactions such as a medication that may worsen a pre-existing medical condition.

At the point of dispensing, the conventional pharmacy system relies heavily on labor-intensive processes and human expertise, which can lead to inefficiencies, errors, and delays. The process of filling a prescription involves several manual steps, such as interpreting the prescription, dispensing the medication, and verifying the dosage and frequency. AI can cut through the tedious aspects of the pharmacy workflow from prescription interpretation to dispensing details, presenting the pharmacist with the crucial information necessary to make the final decision as gatekeeper.


III.  Improving Patient Engagement Between Pharmacy Visits With AI

AI could provide patients with personalized reminders and education, as well as answer questions in real time. AI chatbots can be used to interpret questions and provide standardized information. A first line before involving a healthcare provider

with more complex questions. AI’s ability to provide 24/7 conversational agents would alleviate the burden on healthcare providers and allow them to focus on more complex patient cases. In addition, ease of communication may have a positive impact on patient behavior- namely medication adherence and health outcomes.

More basically, AI could assist patients with prescription refill reminders and online ordering leading to improved medication adherence and increased patient convenience.  This applies to prescription transfer as well – reducing both pharmacist and patient burden in transferring prescriptions between pharmacies and potentially impacting medication adherence.


IV. Enhancing Patient-centric Care Using AI

For those suffering from chronic conditions, AI-enhanced disease management may engage personalized support and medication management to keep the disease controlled at a reduced demand on physician or pharmacist time. This includes integration of AI technology in tele-assessment, tele-diagnosis, tele-interactions, and tele-monitoring. Below are two examples of the latter:

  • AI becomes a valuable tool in the digital health toolbox in monitoring patient compliance and efficacy of treatment. In the management of diabetes, AI could monitor patients’ blood glucose levels through integration with wearable devices and remind patients to take their insulin or other medications as prescribed. If a patient consistently misses doses or experiences significant changes in blood glucose levels, healthcare providers could be alerted to intervene and adjust the treatment plan accordingly. This could lead to improved patient outcomes, reduced hospitalizations and complications.
  • AI could also monitor patients for adverse drug reactions and alert healthcare providers and patients. For example, a patient on blood pressure medication and the monitoring of blood pressure and heart rate against baseline values. If abnormalities are detected outside of an AI-determined boundary, the healthcare provider the healthcare provider could be contacted and patient informed on necessary countermeasures. In this way, AI could help to improve patient safety.

At a more integrated level, AI analysis of electronic health records could provide healthcare providers with real-time updates on patient medication management and treatment progress. It could also be used to call out and reconcile discrepancies or incomplete information.

All of the above are crucial for ensuring that patients not only receives safe and effective treatment, limiting adverse drug events, but remains controlled on their medication, limiting events requiring significant medical intervention.


V. Augmenting Data Analytics With AI

AI can analyze patient data to identify trends and patterns in medication adherence, drug interactions, and adverse drug reactions. Advanced applications may include analytics on medication adherence for a specific patient archetypes and effective countermeasures. This could inform the development of more effective interventions and both manufacturer and pharmacy hub support systems for at-risk populations

Finally, these analytics can serve to improve the operational efficiency of the pharmacy, through inventory and supply chain management. AI algorithms can analyze a vast amount of data, including past sales, seasonality, and other externalities, to predict demand, assisting  pharmacies in maintaining an optimal inventory, minimizing stockouts and overstock. Furthermore, by monitoring stock levels in real-time and automatically generating purchase orders to replenish stocks when levels fall below certain thresholds, this technology could save staff time and help ensure essential medications are always readily available.


VI. State Regulatory Considerations – An Evolving Landscape

Analysis of state board of pharmacy regulations is crucial in this space to ensure that use of AI complies with all applicable state laws.  AI may be perceived as novel in many states, necessitating communication with a state boards of pharmacy. Notably, the states have been active in the regulation of AI, similar to their role in the early days of the internet.


VII.  Federal Regulatory Considerations – An Active Space

Pharmacies that utilize AI systems to collect, store, or transmit patient health information must comply with federal HIPAA regulations by ensuring that their systems are secure, and that patient information is protected from unauthorized access. Patient consent also may be required before using AI to collect or analyze health information, depending on the circumstances.

On a broader level, continued federal regulatory and legislative analysis is crucial in this rapidly evolving space. In June of 2023, Senate Majority Leader Chuck Schumer (D-NY) announced his SAFE Innovation Framework, policy objectives for what he calls an “all-hands-on-deck effort” to contend with artificial intelligence (AI). Asserting that the “traditional approach of committee hearings” is not sufficient to meet the moment, Sen. Schumer promised a series of AI “Insight Forums” on topics to include such topics as privacy and liability. More recently, in late October of 2023, President Biden issued an executive order in the AI space which directs new standards for AI safety and security as well as privacy, among other topics.


VIII.  Future Considerations

Target analysis in this environment requires an understanding of pharmacy market dynamics as well as federal and state regulatory and legislative policies guiding use of AI and digital enablement. It also requires market access knowledge of provider dynamics and strategic analysis on the AI landscape in healthcare applications. With extensive experience in the core pharmacy services landscape as well as associated knowledge of specialty medication, sites of care and PBM-pharmacy considerations, Marwood’s services connect policy with market dynamics and strategy.



Mark Slomiany PhD MBA MPA is a Director of Advisory at The Marwood Group and a former faculty member of the Department of Cardiothoracic Surgery at New York University Langone Health, as well as a former research associate at the Mossavar-Rahmani Center for Business and Government at the Harvard Kennedy School of Government.



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