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AI (artificial intelligence) is transforming various industries in numerous ways, and companies, scientists and researchers are already using the technology across the health care and drug store retail sectors — from helping with early diagnosis of medical conditions to improving patient outcomes in health care, and from inventory management of drug stocks to automated prescription filling. However, current applications for consumer use still seem to pale in comparison to those for enterprise use.
Deborah Weinswig
In this article, we present the various areas in which AI (including generative AI) can be applied in drug store retail, as well as discussing the challenges related to the adoption of AI technology in this sector.
Market scale and opportunity
Within health care, more than 60% of companies and startups globally that are developing AI-based applications are doing so for clinicians (doctors, nurses or other clinicians), and 45% are developing applications for health care providers, according to the AI in Healthcare Survey conducted by data-science firm Gradient Flow in January to March 2022. Enterprise and clinical uses clearly dominate the space: The survey found that only 37% of AI application developers are targeting patients as the intended users (such as through digital health apps or websites).
There are tremendous opportunities to apply AI within health care and retail, and more specifically within drug store retailing — for retail management functions and for interaction with consumers, to drive efficient outcomes.
Five key applications
The applications of AI are seemingly endless, but we believe that there are five critical ways in which AI can be applied within drug store retailing, as we discuss below.
- Inventory management
AI algorithms can analyze historical sales data, current demand patterns and other relevant factors to optimize inventory levels. By accurately predicting demand, pharmacies can minimize stockouts and overstocking, ensuring that essential medications are available to customers when needed.
Furthermore, AI for loss prevention supports inventory management. As drug stores tend to carry an abundance of essential products and personal care items, which are usually small in size and most at risk of retail theft, AI solutions enable retailers to be proactive in detecting and preventing retail shrink and loss by analyzing information gathered from sources such as security systems (including CCTV and alarms), in-store devices and point-of-sale (POS) systems.
We expect generative AI to find a key role in supporting loss prevention and inventory management for retailers by finding relationships among data and communicating them in human language. The technology could predict theft and fraud times based on pattern analysis, predict new items to be targeted by thieves, determine optimal store layouts to reduce theft, analyze POS and self-checkout data, and find patterns in ORC (organized retail crime) behavior. Generative AI could also find new ways to reconfigure stores to keep assets, employees and customers safe.
- Prescription filling
AI-powered robotic systems can automate prescription filling processes, reducing errors and increasing efficiency. These systems can accurately count and sort medications, package prescriptions, and label them with the necessary information.
CVS has already implemented such a system and, with its large store fleet, automating processes and being able to amass analytics from across geographies can help provide insights into various aspects of operations and make workflow management more efficient.
- Medication adherence
Generative AI has the potential to ensure medication adherence through a number of ways:
- Personalized reminders and notifications — Generative AI algorithms, along with AI-powered wearable devices or digital health apps, can be used to complement personalized reminder systems that send targeted notifications to patients about their medication schedules. These timely reminders can be tailored based on individual preferences, unlike generic calendar notifications, allowing generative AI chatbots or more complex applications to confirm that individuals have actually taken the appropriate medication, on time.
- Interactive medication education — Generative AI can be utilized to create interactive and engaging educational materials that provide information about medications, including their purpose, potential side effects and proper usage. These materials can be tailored to individual patients’ needs, improving their understanding of the medications and promoting adherence.
- Chatbot support — Generative AI chatbots can be employed to provide real-time support and answer patients’ questions regarding their medications. These chatbots can offer information about dosage instructions, potential drug interactions and side effects, helping patients make informed decisions and maintain adherence.
- Gamification and incentives — Generative AI algorithms can be utilized to design gamified applications that incentivize medication adherence. These applications can create challenges, provide rewards and track patients’ progress, making the medication adherence process more engaging and enjoyable.
- Adaptive medication schedules — Generative AI algorithms can analyze patient data, including medication history and health condition, to generate adaptive medication schedules. These schedules can be tailored to individual patients’ needs, accounting for factors such as medication interactions, side effects and lifestyle considerations, thereby increasing adherence.
It is important to note that while generative AI can provide valuable support and tools for medication adherence, it should augment, rather than replace, human health care professionals. The combination of technology and human guidance can offer the most effective approach to improving medication adherence and patient outcomes.
- Drug-drug interaction and allergy alerts
AI algorithms integrated into pharmacy management systems can quickly analyze patient profiles and flag potential drug-drug interactions or allergies. This helps pharmacists and pharmacy staff ensure patient safety by identifying potential risks and providing appropriate recommendations or alternatives.
- Data analytics and customer insights
AI algorithms can analyze customer data, such as purchase history, preferences and health conditions, to provide personalized recommendations and targeted promotions. This enables retail pharmacies to tailor their offerings to individual customers, improving customer satisfaction and loyalty.
Challenges of implementing AI solutions
While AI holds immense potential in health care, there are several challenges that need to be addressed for its effective implementation, as we outline below:
- Data quality and availability — AI algorithms require large, high-quality data sets to train and generate accurate insights. However, health care or consumer data can be fragmented, inconsistent and possibly contain errors if it were generated with or has had human input at some points in the data journey. Ensuring data quality and accessibility, including interoperability between different systems, remains a significant challenge.
- Data privacy and security — Health care data is highly sensitive and subject to strict privacy regulations. Maintaining patient privacy and protecting data from unauthorized access or breaches is critical. AI systems must comply with regulations such as HIPAA (Health Insurance Portability and Accountability Act) to safeguard patient information.
- Bias and fairness — AI algorithms can inherit biases from the data they are trained on, leading to unfair or discriminatory outcomes. Biases can disproportionately affect certain populations, leading to unequal access to health care. Efforts must be made to identify and mitigate bias in AI algorithms to ensure fair and equitable outcomes for all patients.
- Integration with existing systems — Integrating AI systems with existing health care infrastructure and workflows can be challenging. Legacy systems, diverse data formats and interoperability issues can hinder the seamless integration of AI technologies into health care settings. Compatibility and standardization efforts are necessary to ensure smooth implementation.
- Regulatory and ethical considerations — The rapid advancements in AI raise questions about regulations, liability and ethical considerations. There is a need for clear guidelines and policies to govern the development, deployment and use of AI in health care, ensuring patient safety, accountability and ethical standards are upheld.
What we think
AI’s applications and benefits in health care and drug store retail are numerous, as demonstrated by the various products and platforms already deployed and those that are still in development. Moreover, the emergence of generative AI is opening up a new wave of possibilities for applications that can improve efficiency and health care outcomes, as well as augment the roles of health care professionals and fill gaps where there are shortages.
There are several challenges that are yet to be addressed before AI applications are more widely adopted. Health care brands and drug store retailers could consider sharing anonymized data to help networks of stakeholders using AI applications to improve their data sets and thus quality of their algorithms’ learnings.
While a majority of the existing applications are for enterprise use currently, there seems to be immense potential for firms to develop applications in the consumer space. Firms that develop applications that integrate with each other or have greater interoperability, such as consumer applications that can integrate with enterprise ones, and applications that address one health function integrating with another, could pave the way to form a more cohesive ecosystem of data sharing among stakeholders. This consolidation of applications could increase the data available for algorithms to learn from and improve their quality of results.
Deborah Weinswig is founder and chief executive officer of Coresight Research.
