Role of Big Data in Predicting Medicine Demand in Hospitals

DOI: https://doi.org/10.63345/ijrmp.v13.i3.1

Akash Banerjee

Independent Researcher

West Bengal, India

Abstract

Modern hospitals face an ever‐increasing challenge in managing medicine inventories and ensuring optimal patient care. The advent of big data analytics provides a transformative opportunity to predict medicine demand with unprecedented accuracy. This manuscript examines the role of big data in forecasting pharmaceutical needs within hospital settings. It discusses how large, heterogeneous datasets—from electronic health records to supply chain information—can be integrated and analyzed using advanced statistical models and machine learning techniques. The study reviews the evolution of predictive models, presents a comprehensive literature review up to 2021, and outlines a robust methodology for implementing big data analytics in hospital medicine management. Findings suggest that leveraging big data not only improves the precision of demand forecasts but also contributes to cost reduction, reduced waste, and enhanced patient outcomes. Future research is recommended to explore real-time applications and integration with emerging technologies like IoT and blockchain.

Keywords

Big Data, Medicine Demand Prediction, Hospital Inventory Management, Predictive Analytics, Healthcare Supply Chain

References

• https://www.google.com/url?sa=i&url=https%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1002%2Fhcs2.66&psig=AOvVaw0Uh3cyGIuvSAJCD5-3u1EO&ust=1741622464570000&source=images&cd=vfe&opi=89978449&ved=0CBQQjRxqFwoTCNjdlLew_YsDFQAAAAAdAAAAABAE
• https://www.google.com/url?sa=i&url=https%3A%2F%2Fwww.degruyter.com%2Fdocument%2Fdoi%2F10.1515%2Fjisys-2022-0297%2Fhtml&psig=AOvVaw2TpdTtYVzYMw7sjTFqd9ak&ust=1741622883888000&source=images&cd=vfe&opi=89978449&ved=0CBQQjRxqFwoTCOCujoix_YsDFQAAAAAdAAAAABAE
• Chen, H., Chiang, R. H. L., & Storey, V. C. (2012). Business Intelligence and Analytics: From Big Data to Big Impact. MIS Quarterly, 36(4), 1165–1188.
• Davenport, T. H. (2014). Big Data at Work: Dispelling the Myths, Uncovering the Opportunities. Harvard Business Review Press.
• Raghupathi, W., & Raghupathi, V. (2014). Big data analytics in healthcare: Promise and potential. Health Information Science and Systems, 2(1), 3.
• Kwon, J., Lee, N., & Shin, S. (2014). Data quality management, data usage experience, and acquisition intention of big data analytics. International Journal of Information Management, 34(3), 387–394.
• Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137–144.
• Wamba, S. F., Akter, S., Edwards, A., Chopin, G., & Gnanzou, D. (2015). How “big data” can make big impact: Findings from a systematic review and a longitudinal case study. International Journal of Production Economics, 165, 234–246.
• Murdoch, T. B., & Detsky, A. S. (2013). The inevitable application of big data to health care. JAMA, 309(13), 1351–1352.
• Ristevski, B., & Chen, M. (2018). Big Data Analytics in Medicine and Healthcare. Journal of Integrative Bioinformatics, 15(3), 1–8.
• Kruse, C. S., Kothman, K., Anerobi, K., & Abanaka, L. (2016). Adoption factors associated with electronic health record among long-term care facilities: A systematic review. BMJ Open, 6(4), e010200.
• Ghassemi, M., Naumann, T., Schulam, P., Beam, A. L., Chen, I. Y., & Ranganath, R. (2018). Opportunities in machine learning for healthcare. arXiv preprint arXiv:1806.00388.
• Obermeyer, Z., & Emanuel, E. J. (2016). Predicting the future—Big data, machine learning, and clinical medicine. The New England Journal of Medicine, 375(13), 1216–1219.
• Bates, D. W., Saria, S., Ohno-Machado, L., Shah, A., & Escobar, G. (2014). Big data in health care: Using analytics to identify and manage high-risk and high-cost patients. Health Affairs, 33(7), 1123–1131.
• Wang, Y., Kung, L., & Byrd, T. A. (2018). Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. Technological Forecasting and Social Change, 126, 3–13.
• Li, L., & Chen, Y. (2019). Integrating electronic health records and pharmacy data for medication demand forecasting. Journal of Medical Systems, 43(2), 33.
• Zhang, Y., Milinovich, G. J., Xu, Z., Bambrick, H., Mengersen, K., Tong, S., & Hu, W. (2018). Monitoring pertussis infections using internet search queries. Scientific Reports, 8, 3352.
• Kwon, J. M., & Kim, H. (2019). Big data and its applications in healthcare. Journal of Healthcare Engineering, 2019, Article ID 6782945.
• Thilo, C., & Niewöhner, J. (2019). The promise of predictive analytics in hospital resource management: A case study in medicine demand forecasting. Journal of Medical Economics, 22(5), 456–463.
• Miliard, M. (2016). The importance of big data in healthcare. Healthcare Informatics, 8(1), 34–39.
• Patel, V., & Gupta, S. (2020). A framework for predictive analytics in hospital inventory management. International Journal of Health Policy and Management, 9(3), 98–104.
• Smith, J., & Nguyen, A. (2021). Leveraging big data for efficient medicine inventory management in hospitals. Journal of Biomedical Informatics, 116, 103716.