The Application of Artificial Intelligence in Decision-Making and Sensitivity Analysis for Predicting Shortages of Pharmaceuticals and Medical Equipment During Health Crises

Authors

https://doi.org/10.22105/ahse.v2i2.35

Abstract

This study investigates the application of Artificial Intelligence (AI) and sensitivity analysis in predicting shortages of pharmaceuticals and medical equipment during health crises. The primary issue addressed in the research is the shortage of drug and medical equipment resources during crises, which can have a significant negative impact on hospital efficiency and mortality rates. This research aimed to develop an AI-based prediction model for simulating and forecasting resource shortages in crises, as well as to perform sensitivity analysis to identify factors affecting the accuracy of predictions. The research methodology employed AI models, including neural networks and linear regression, to predict shortages of pharmaceuticals and medical equipment. Additionally, sensitivity analysis was used to simulate various crisis scenarios. The findings revealed that factors such as transportation disruptions, demand fluctuations, and seasonal changes have a significant impact on the accuracy of predictions. The results of this study suggest that AI models and sensitivity analysis can effectively assist in improving the prediction and management of pharmaceutical and medical equipment resources during health crises.

Keywords:

Artificial intelligence, Drug shortage prediction, Sensitivity analysis, Health crises, Decision-making models

References

  1. [1] Filip, R., Gheorghita Puscaselu, R., Anchidin-Norocel, L., Dimian, M., & Savage, W. K. (2022). Global challenges to public health care systems during the COVID-19 pandemic: A review of pandemic measures and problems. Journal of personalized medicine, 12(8), 1295. https://doi.org/10.3390/jpm12081295
  2. [2] Cohen, J., & van der Meulen Rodgers, Y. (2020). Contributing factors to personal protective equipment shortages during the COVID-19 pandemic. Preventive medicine, 141, 106263. https://doi.org/10.1016/j.ypmed.2020.106263
  3. [3] Govindan, K., Mina, H., & Alavi, B. (2020). A decision support system for demand management in healthcare supply chains considering the epidemic outbreaks: A case study of coronavirus disease 2019 (COVID-19). Transportation research part e: Logistics and transportation review, 138, 101967. https://doi.org/10.1016/j.tre.2020.101967
  4. [4] Banji, A. F., Adekola, A. D., & Dada, S. A. (2024). Supply chain innovations to prevent pharmaceutical shortages during public health emergencies. International journal of engineering research and development, 20(11), 1242–1249. chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://ijerd.com/paper/vol20-issue11/201112421249.pdf
  5. [5] Rosenthal, U., & Kouzmin, A. (1997). Crises and crisis management: Toward comprehensive government decision making. Journal of public administration research and theory, 7(2), 277–304. https://doi.org/10.1093/oxfordjournals.jpart.a024349
  6. [6] Guo, J., & Li, B. (2018). The application of medical artificial intelligence technology in rural areas of developing countries. Health equity, 2(1), 174–181. https://doi.org/10.1089/heq.2018.0037
  7. [7] Ugwu, O. I., Clarksville, T. N., Hassan, O. F., Clarksville, T. N., Sanusi, M. A., Odukoya, O., & Onasanya, T. (2024). Artificial intelligence in healthcare supply chains: Enhancing resilience and reducing waste. International journal of advance research, ideas and innovations in technology, 10, 203–217. https://www.researchgate.net/publication/381728757
  8. [8] Iqbal, T., Masud, M., Amin, B., Feely, C., Faherty, M., Jones, T., … ., & Vazquez, P. (2024). Towards integration of artificial intelligence into medical devices as a real-time recommender system for personalised healthcare: State-of-the-art and future prospects. Health sciences review, 10, 100150. https://doi.org/10.1016/j.hsr.2024.100150
  9. [9] Heidarpour, J., Hesari, S., Ghazizadeh, M., & Ghahremani, B. (2021). Intelligent health solution system. North Khorasan university of medical sciences, 13(4), 81-86. (In Persian). http://dx.doi.org/10.29252/nkjmd-09024
  10. [10] Taghipour, Z., Ghorbanzadeh, P., Karamat Talatapeh, S., Zeinali, M., Ghorbanzadeh, A., & Firooz, N. (2023). Applications of artificial intelligence and machine learning in the pharmaceutical industry. The 6th national conference on new technologies in electrical, computer and mechanical engineering of Iran, Tehran. (In Persian). Tehran, Iran, Civilica. https://civilica.com/doc/1744146
  11. [11] Fan, D., Miao, R., Huang, H., Wang, X., Li, S., Huang, Q., … ., & Deng, R. (2024). Multimodal ischemic stroke recurrence prediction model based on the capsule neural network and support vector machine. Medicine, 103(35), e39217. https://doi.org/10.1097/MD.0000000000039217
  12. [12] Ayesha, Noor-ul-Amin, M., Albalawi, O., Mushtaq, N., Mahmoud, E. E., Yasmeen, U., & Nabi, M. (2024). Modeling health outcomes of air pollution in the middle east by using support vector machines and neural networks. Scientific reports, 14(1), 21517. https://doi.org/10.1038/s41598-024-71694-8
  13. [13] Shahabi, A., Anbiaei, M. R., & Derafshi, Z. (2024). Application of artificial intelligence in supply chain management. The 3rd international conference on economics and business management, Tehran. (In Persian). Tehran, Iran, Civilica. https://civilica.com/doc/2093383
  14. [14] Kumar, V. V, Sahoo, A., Balasubramanian, S. K., & Gholston, S. (2025). Mitigating healthcare supply chain challenges under disaster conditions: a holistic AI-based analysis of social media data. International journal of production research, 63(2), 779–797. https://doi.org/10.1080/00207543.2024.2316884
  15. [15] Dutta, P., & Deka, S. (2024). A novel approach to flood risk assessment: Synergizing with geospatial based MCDM-AHP model, multicollinearity, and sensitivity analysis in the lower brahmaputra floodplain, assam. Journal of cleaner production, 467, 142985. https://doi.org/10.1016/j.jclepro.2024.142985
  16. [16] Sallam, M., Oliver, A., Allam, D., Kassem, R., & Damani, M. (2024). Addressing drug shortages at mediclinic parkview hospital: A‎ five-year study of‎ challenges, impact, and strategies. Cureus, 16(12).
  17. [17] Bilal, A. I., Bititci, U. S., & Fenta, T. G. (2024). Effective supply chain strategies in addressing demand and supply uncertainty: A case study of ethiopian pharmaceutical supply services. Pharmacy, 12(5), 132. https://doi.org/10.3390/pharmacy12050132
  18. [18] Rahmani, P., Mortazavi, P., Aghaei Meybodi, H. R., Patrinos, G. P., Sarhangi, N., Nikfar, S., … ., & Hasanzad, M. (2024). Machine learning and artificial intelligence in modern medicine. In A glimpse at medicine in the future (pp. 61–77). Singapore: Springer Nature Singapore. https://doi.org/10.1007/978-981-97-4974-4_4
  19. [19] Fahimi, R., Seyedkazemi, A., & Kutanaei, S. S. (2025). Prediction and sensitivity analysis of embankment dam settlement under earthquake loading using gene expression programming. Geomechanics and geoengineering, 20(1), 115–137. https://doi.org/10.1080/17486025.2024.2367439
  20. [20] Rys, M., & Topor-Madry, R. (2025). From crisis to change: Lessons learned and future trends in healthcare and public health. Journal of integrated care, 33(1), 75–89. https://doi.org/10.1108/JICA-10-2024-0054
  21. [21] MohammadShafiei, M., & MohammadShafiei, A. (2015). Crisis and its management strategies. The second international conference on research in science and technology, Istanbul, Turkey. (In Persian). Türkiye, Civilica. https://civilica.com/doc/505031/
  22. [22] Alemde, V. O. (2025). Deploying strategic operational research models for AI-augmented healthcare logistics, accessibility, and cost reduction initiatives. International research journal of modernization in engineering technology and science, 7(2), 2353. https://www.doi.org/10.56726/IRJMETS67609
  23. [23] Bailey, K. (2025). The road ahead: Sustainable healthcare emergency management and emerging trends. Emergency management for healthcare leaders, 132–138. https://doi.org/10.4324/9781003529194
  24. [24] Ongesa, T. N., Ugwu, O. P.-C., Ugwu, C. N., Alum, E. U., Eze, V. H. U., Basajja, M., … ., & Ejemot-Nwadiaro, R. I. (2025). Optimizing emergency response systems in urban health crises: A project management approach to public health preparedness and response. Medicine, 104(3). https://doi.org/10.1097/MD.0000000000041279
  25. [25] Wang, Y., Tolley, K., Francois, C., & Toumi, M. (2025). Machine learning-based models for prediction of innovative medicine reimbursement decisions in Scotland. Journal of epidemiology and population health, 73(1), 202802. https://doi.org/10.1016/j.jeph.2024.202802
  26. [26] Zhu, X., Ninh, A., Zhao, H., & Liu, Z. (2021). Demand forecasting with supply‐chain information and machine learning: Evidence in the pharmaceutical industry. Production and operations management, 30(9), 3231–3252. https://doi.org/10.1111/poms.13426
  27. [27] Ghannem, A., Nabli, H., Djemaa, R. Ben, & Sliman, L. (2024). Enhancing pharmaceutical supply chain resilience: A comprehensive review of visibility and demand forecasting. https://doi.org/10.21203/rs.3.rs-3932079/v1
  28. [28] Salem, H., Kabeel, A. E., El-Said, E. M. S., & Elzeki, O. M. (2022). Predictive modelling for solar power-driven hybrid desalination system using artificial neural network regression with Adam optimization. Desalination, 522, 115411. https://doi.org/10.1016/j.desal.2021.115411
  29. [29] Ivanov, D., & Dolgui, A. (2022). The shortage economy and its implications for supply chain and operations management. International journal of production research, 60(24), 7141–7154. https://doi.org/10.1080/00207543.2022.2118889

Downloads

Published

2025-04-28

Issue

Vol. 2 No. 2 (2025): Annals of Healthcare Systems Engineering

Section

Articles

How to Cite

Ghandehari, M. ., Najafi, S. E. ., & Edalatpanah, S. A. . (2025). The Application of Artificial Intelligence in Decision-Making and Sensitivity Analysis for Predicting Shortages of Pharmaceuticals and Medical Equipment During Health Crises. Annals of Healthcare Systems Engineering, 2(2), 76-86. https://doi.org/10.22105/ahse.v2i2.35

Similar Articles

1-10 of 14

You may also start an advanced similarity search for this article.