Assessing the impact of artificial intelligence and machine learning on forecasting medication demand and supply in public pharmaceutical systems: A systematic review
Department of Pharmacy, Oshakati Intermediate Hospital, Ministry of Health and Social Services, Oshana Region Directorate, Namibia.
Review Article
GSC Biological and Pharmaceutical Sciences, 2024, 26(02), 140–150.
Article DOI: 10.30574/gscbps.2024.26.2.0071
Publication history:
Received on 09 January 2024; revised on 16 February 2024; accepted on 19 February 2024
Abstract:
Background: Effectively managing drug demand and supply through pharmaceutical quantification is critical as it ensures that medications are readily available when needed while reducing costs, optimizing inventory management, and ultimately improving patient care. This research aimed to examine the existing literature on the influence of artificial intelligence (AI) and machine learning (ML) on predicting pharmaceutical demand in public systems. This review focused specifically on the accuracy of these methods, their limitations, and the ethical concerns associated with their use.
Methods: The research used PubMed and Google Scholar databases, following PRISMA principles, and yielded 13 peer-reviewed articles. The quality of the included studies was assessed for potential bias using established standard criteria, the Cochrane Risk of Bias Checklist Tool for systematic reviews of intervention.
Results: The results show that linear regression and random forest are the predominant models for predicting medication quantities in hospital pharmacies. However, the precision of these models can be affected by data entry inaccuracies and fluctuations. The study identified technical, human, and organizational obstacles as barriers to adoption, as well as problems related to privacy and confidentiality.
Conclusion: The use of AI and ML can estimate the demand and supply of medicine in public pharmaceutical delivery systems. The results highlight the importance of further study to improve forecasting algorithm simulation accuracy, broaden single time-series projections to incorporate additional patient-associated factors and investigate various efficiency measures.
Keywords:
Artificial Intelligence; Machine Learning; Medication Demand and Supply; Pharmaceutical Systems; Public Healthcare Service/Sector
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0
