Modeling of the intelligent interface of interaction between a pharmacy and a children's clinic
- Authors: Kurashov M.M.1, Kondrashov A.A.2, Loskutova E.E.1
-
Affiliations:
- Peoples' Friendship University of Russia named after Patrice Lumumba
- Lomonosov Moscow State University
- Issue: Vol 73, No 7 (2024)
- Pages: 41-48
- Section: Organization and economy
- URL: https://bakhtiniada.ru/0367-3014/article/view/295988
- DOI: https://doi.org/10.29296/25419218-2024-07-06
- ID: 295988
Cite item
Open Access
Access granted
Subscription Access
Abstract
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.
Full Text
##article.viewOnOriginalSite##About the authors
Maxim Michailovich Kurashov
Peoples' Friendship University of Russia named after Patrice Lumumba
Email: kurashov-mm@rudn.ru
ORCID iD: 0000-0002-0349-905X
PhD in Pharmaceutical Sciences, Associate Professor of the Management and Economics of Pharmacy Department
Russian Federation, MoscowAlexander Andreevich Kondrashov
Lomonosov Moscow State University
Email: kondrasasha@mail.ru
ORCID iD: 0009-0004-0601-3205
PhD Student of the Pharmaceutical Chemistry and Organization of Pharmaceutical Business Department
Russian Federation, MoscowEkaterina Efimovna Loskutova
Peoples' Friendship University of Russia named after Patrice Lumumba
Author for correspondence.
Email: loskutova-ee@rudn.ru
ORCID iD: 0000-0002-1514-0941
Doctor of Pharmaceutical Sciences, Professor, Head of the Management and Economics of Pharmacy Department
Russian Federation, MoscowReferences
- Wei X., Yuan H., Sun Y. et al. Health Services Utilization in China during the COVID-19 Pandemic: Results from a Large-Scale Online Survey. Int J Environ Res Public Health. 2022; 19 (23). doi: 10.3390/ijerph192315892.
- Liu S., Luo P., Tang M. et al. Providing pharmacy services during the coronavirus pandemic. Int J Clin Pharm. 2020; 42 (2): 299–304. doi: 10.1007/s11096-020-01017-0.
- Peng Y., Wang H., Fang Q. et al. Effectiveness of Mobile Applications on Medication Adherence in Adults with Chronic Diseases: A Systematic Review and Meta-Analysis. J Manag Care Spec Pharm. 2020; 26 (4): 550–561. doi: 10.18553/jmcp.2020.26.4.550.
- Сибирева А.Д., Желткевич О.В. Концепция информационно-коммуникационной услуги в аптечной организации. Фармация, 2021; 70 (3): 42–49. [Sibireva AD, Zheltkevich OV. The concept of information and communication services in a pharmacy. Farmaciya (Pharmacy) 2021; 70 (3): 42–49. doi: 10.29296/25419218-2021-03-08 (in Russian).]
- Просвиркин Г.А., Дорофеева В.В. Анализ услуги дистанционного консультирования в аптечных онлайн-сервисах. Фармация, 2024; (3): 38–42. [Prosvirkin GA, Dorofeeva VV. Analyzing the Implementation of Remote Consultation in Online Pharmacies. Farmaciya (Pharmacy) 2024; 73 (3): 38–42. doi: 10.29296/25419218-2024-03-06 (in Russian)]
- Esteva A., Robicquet A., Ramsundar B. et al. A guide to deep learning in healthcare. Nat Med 2019; 25 (1): 24–29. doi: 10.1038/s41591-018-0316-z.
- Beam A.L., Kohane I.S. Big Data and Machine Learning in Health Care. JAMA 2018; 319 (13): 1317. doi: 10.1001/jama.2017.18391.
- Bertolotti F., Schettini F., Ferrario L. et al. A prediction framework for pharmaceutical drug consumption using short time-series. Expert Syst Appl 2024; 253: 124265. doi: 10.1016/j.eswa.2024.124265.
- Soumit Roy, Mainak Mitra. Enhancing Efficiency in Healthcare Supply Chains: Leveraging Machine Learning for Optimized Operations. International Journal For Multidisciplinary Research 2021; 3 (6). doi: 10.36948/ijfmr.2021.v03i06.10323.
- Bhat S.S., Srihari V.R., Prabhune A. et al. Optimizing Medication Access in Public Healthcare Centers: A Machine Learning Stochastic Model for Inventory Management and Demand Forecasting in Primary Health Services. In: 2024 International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE) IEEE. 2024; 1–5; doi: 10.1109/IITCEE59897.2024.10467229.
- Ke G., Meng Q., Finley T. et al. LightGBM: A Highly Efficient Gradient Boosting Decision Tree. In: Advances in Neural Information Processing Systems. (Guyon I., Luxburg U. Von, Bengio S. et al. eds) Curran Associates, Inc.; 2017.
- Lokker C., Abdelkader W., Bagheri E. et al. Boosting efficiency in a clinical literature surveillance system with LightGBM. PLOS Digital Health 2024; 3 (9): e0000299. doi: 10.1371/journal.pdig.0000299.
- Chen T., Guestrin C. XGBoost. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining ACM: New York, NY, USA. 2016; 785–794; doi: 10.1145/2939672.2939785.
- Kumar A., Mani V., Jain V. et al. Managing healthcare supply chain through artificial intelligence (AI): A study of critical success factors. Comput Ind Eng 2023; 175: 108815. doi: 10.1016/j.cie.2022.108815.
- Rathipriya R., Abdul Rahman A.A., Dhamodharavadhani S. et al. Demand forecasting model for time-series pharmaceutical data using shallow and deep neural network model. Neural Comput Appl. 2023; 35 (2): 1945–1957. doi: 10.1007/s00521-022-07889-9.
Supplementary files
