Digital transformation of community pharmacies through AI and predictive analytics

Juan Ignacio Gutierrez

Diginomics, 2025, vol. 4, 211

Abstract: Introduction: The project proposed a comprehensive solution aimed at community pharmacies, seeking to improve customer service, operational management and technological integration. To this end, it combined a mobile application with e-commerce functions and an artificial intelligence chatbot, along with a desktop application for sales management and forecasting. The proposal responded to the growing need for efficient and personalized access to pharmaceutical services, especially in a digitized environment. Development: Building on successful experiences in healthcare and retail, the proposal leveraged tools such as mHealth apps, which were shown to improve treatment adherence and patient autonomy. It also integrated AI-enabled chatbots, useful for medical care, healthcare education and administrative tasks. At the operational level, it incorporated automated inventory systems that optimized processes and reduced errors, strengthening patient safety. In addition, predictive analysis models such as Random Forest or XGBoost were applied, which made it possible to anticipate demand and segment customers with high precision. The use of technologies such as Java, Kotlin, Python and environments such as Android Studio and Electron Forge ensured technical feasibility. The competitive analysis revealed that, although there were pharmacies with shopping applications, none integrated chatbots or reminders, which represented a differential advantage. Conclusions: The solution proposed offered a substantial improvement in community pharmaceutical care, by integrating efficiency, artificial intelligence and user-centered approach. Its implementation consolidated an innovative, scalable alternative, adapted to current requirements, allowing progress towards a more intelligent and accessible pharmacy model.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:dbk:digino:v:4:y:2025:i::p:211:id:1056294digi2025211

DOI: 10.56294/digi2025211

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