Modeling of the intelligent interface of interaction between a pharmacy and a children's clinic

Introduction. Economic barriers, uneven territorial distribution of pharmacy organizations, and insufficient efficiency of drugs supply chains hinder the accessibility of drugs and the timely receipt of pharmaceutical care (PC) by patients worldwide. The digital transformation in healthcare, accelerated by the COVID-19 crisis, necessitates the adaptation of PC practices to meet modern patient needs. Objective of the study. To develop and validate a procedure for the interaction between a clinic and a pharmacy to facilitate data exchange through an interface, enabling the pharmacy to receive data for forming an assortment of drugs adapted to the characteristics and behavioral trends of the main target audience. Material and methods. The study utilized anonymized data on drugs prescriptions by physicians from the medical information system of a network of medical organizations in Moscow for the period from January 2018 to December 2023. Data preprocessing was conducted, followed by the training of a machine learning model using the LightGBM algorithm. The predictive performance of the model was assessed using MAE and RMSE metrics. Results. An analytical interface for the interaction between the clinic and the pharmacy was developed, incorporating a predictive model for forming the drugs assortment. The model effectively accounts for seasonal trends, patient demographic characteristics, and other key factors influencing drugs demand. The average MAE and RMSE values were 1.27 and 1.68, respectively, indicating high model accuracy. Conclusion. Implementing the developed interface allows the pharmacy to form drugs assortment tailored to the real needs of patients, contributing to optimized inventory management, reduced risk of shortages and overstocking, enhanced accessibility of PC for children, and increased economic efficiency of the pharmacy. The integration of big data technologies and machine learning opens new prospects for the personalization of medical and pharmaceutical care.