Use of Predictive Analytics in Demand Forecasting for Rare Disease Medications

DOI: https://doi.org/10.63345/ijrmp.v10.i9.2

Rohan Desai

Jaipur, Rajasthan

Abstract

The increasing complexities in healthcare supply chains, particularly for rare disease medications, demand innovative forecasting approaches. This manuscript examines the application of predictive analytics as a tool to enhance demand forecasting for rare disease medications. By integrating machine learning techniques with classical statistical models, predictive analytics provides actionable insights that can significantly improve inventory management, reduce wastage, and ensure timely patient access to crucial therapies. The study reviews literature up to 2020, presents statistical analyses highlighting the performance of predictive models versus traditional forecasting methods, and outlines a robust methodology for integrating these techniques. Results indicate that advanced analytics not only improve forecast accuracy but also contribute to cost-efficiency and enhanced patient care. The manuscript concludes with a discussion on scope, limitations, and recommendations for future research in this domain.

Keywords

Predictive Analytics; Demand Forecasting; Rare Disease Medications; Machine Learning; Healthcare Supply Chain; Inventory Management

References

• https://www.google.com/url?sa=i&url=https%3A%2F%2Fnexocode.com%2Fblog%2Fposts%2Fpredictive-analytics-in-pharmaceutical-manufacturing%2F&psig=AOvVaw2HlUa4YR7NK691KCPFzlZp&ust=1741258938065000&source=images&cd=vfe&opi=89978449&ved=0CBQQjRxqFwoTCMDd46fl8osDFQAAAAAdAAAAABAE
• https://www.google.com/url?sa=i&url=https%3A%2F%2Fwww.inflowinventory.com%2Fblog%2Fwhat-is-demand-forecasting%2F&psig=AOvVaw1QOnqvgalB-UnEWqPcxH5U&ust=1741259552030000&source=images&cd=vfe&opi=89978449&ved=0CBQQjRxqFwoTCKjVlMvn8osDFQAAAAAdAAAAABAE
• Armstrong, J. S. (2001). Principles of Forecasting: A Handbook for Researchers and Practitioners. Springer.
• Box, G. E. P., & Jenkins, G. M. (1976). Time Series Analysis: Forecasting and Control. Holden-Day.
• Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: Principles and Practice. OTexts.
• Zhang, Y., Li, X., & Wang, J. (2018). Predictive analytics for pharmaceutical demand forecasting: A machine learning approach. Journal of Medical Analytics, 15(2), 123–135.
• Gupta, R., & Sharma, S. (2019). Integrating big data and predictive analytics in healthcare supply chain management. International Journal of Healthcare Technology and Management, 20(3), 189–205.
• Li, H., Chen, X., & Liu, Y. (2020). Enhancing rare disease medication forecasting through real‐world evidence integration. Journal of Rare Diseases, 7(1), 45–60.
• Rodriguez, M., & Patel, S. (2020). Comparative analysis of traditional and machine learning models in drug demand forecasting. Pharmaceutical Statistics, 19(4), 210–225.
• Makridakis, S., Wheelwright, S. C., & Hyndman, R. J. (1998). Forecasting Methods and Applications. Wiley.
• Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.
• Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297.
• Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189–1232.
• Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785–794).
• Bertsimas, D., & Kallus, N. (2020). From predictive to prescriptive analytics. Management Science, 66(3), 1025–1044.
• Wang, F., & Hu, J. (2017). Data integration in healthcare: Techniques and applications. Health Information Science and Systems, 5(1), 1–10.
• Khosrow-Pour, M. (2017). Big Data and Analytics in Healthcare: A Comprehensive Review. IGI Global.
• Chaudhuri, S., Dayal, U., & Narasayya, V. (2011). An overview of business intelligence technology. Communications of the ACM, 54(8), 88–98.
• Davenport, T. H. (2013). Analytics at Work: Smarter Decisions, Better Results. Harvard Business Press.
• 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.
• Leek, J. T., & Peng, R. D. (2015). Statistics: P values are just the tip of the iceberg. Nature, 527(7578), 445–447.
• Sarma, A. K., & Raut, R. D. (2019). Application of machine learning techniques in predicting drug consumption in rare diseases. International Journal of Pharmaceutical Research, 11(2), 75–85.