Machine learning applications in hospital pharmacy for predicting drug shortages and supply chain optimization

The efficient management of drug inventories is a critical challenge in hospital pharmacy operations. Predicting drug shortages and optimizing the supply chain are essential to ensure uninterrupted patient care. This paper explores the application of machine learning (ML) models to enhance forecasting accuracy for drug demand, identify potential shortages, and streamline supply chain operations. Traditional inventory management techniques often struggle to handle pharmaceutical supply and demand's dynamic and unpredictable nature. ML models, including time-series forecasting algorithms and supervised learning approaches, offer promising solutions by leveraging historical data, prescription trends, and external factors such as seasonality and supplier performance. The study examines various ML techniques to predict drug shortages, such as Long Short-Term Memory (LSTM), Random Forest, and Gradient Boosting Machines (GBM). We also explore optimization algorithms to enhance inventory management and distribution strategies. A comprehensive framework is proposed, integrating predictive models with hospital pharmacy systems to enable real-time monitoring and alerts for low-stock scenarios. Additionally, the research addresses challenges like data privacy, model interpretability, and the integration of ML solutions into existing hospital infrastructure. The results demonstrate the potential of ML-based solutions to improve the efficiency of hospital pharmacy operations by reducing stockouts, minimizing wastage, and optimizing procurement strategies. This study underscores the importance of predictive analytics in healthcare supply chains, contributing to better patient outcomes through timely availability of medications. The proposed approach not only enhances operational efficiency but also provides a scalable model applicable to diverse healthcare settings.