Email this article Predictive Analytics for Hospital Resource Allocation during Pandemics: Lessons from COVID-19
Abstract
During the COVID-19 pandemic, hospitals faced unprecedented challenges in allocating resources efficiently to meet the surge in demand for medical care. Predictive analytics emerged as a valuable tool in forecasting patient admissions, ICU bed utilization, ventilator requirements, and other critical resources. This paper presents a comprehensive review of the lessons learned from using predictive analytics for hospital resource allocation during the COVID-19 pandemic. We explore various predictive modeling techniques, data sources, and decision support systems employed by healthcare institutions worldwide. Additionally, we discuss the limitations and ethical considerations associated with predictive analytics in healthcare resource management. By analyzing the successes and shortcomings of existing approaches, we offer insights into refining predictive models and optimizing resource allocation strategies for future pandemics and public health emergencies.
Full Text:
PDFReferences
Adams, V., & Watson, R. (2020). Predictive analytics for COVID-19: Challenges and opportunities. Journal of Biomedical Informatics, 109, 103532.
Chen, Y., & Wang, F. (2021). Machine learning and mathematical modeling for predicting hospital resource utilization during the COVID-19 pandemic. Healthcare, 9(1), 44.
Ghaffarzadegan, N., Rahmandad, H., & Kardan, A. (2020). Modeling the spread of COVID-19 in the U.S.: How can we trust forecasts? Medical Decision Making, 40(5), 575-583.
Khatibi, A., Dunning, A., Asgari, S., & Jafarzadeh, H. (2020). Predictive modeling for hospital resource utilization during the COVID-19 pandemic: A review of current approaches and challenges. Healthcare Management Science, 1-15.
Dhamodharan, B. (2022). Harnessing Disaster Tweets: A Deep Dive into Disaster Tweets with EDA, Cleaning, and BERT-based NLP. International Transactions in Artificial Intelligence, 6(6), 1-14.
Dhamodharan, B. (2022). Beyond Traditional Methods: A Novel Approach to Anomaly Detection and Classification Using AI Techniques. Transactions on Latest Trends in Artificial Intelligence, 3(3).
Dhamodharan, B. (2021). Optimizing Industrial Operations: A Data-Driven Approach to Predictive Maintenance through Machine Learning. International Journal of Machine Learning for Sustainable Development, 3(1), 31-44
Li, Z., & Ge, Z. (2021). Application of predictive analytics in hospital resource allocation during COVID-19 pandemic. Journal of Medical Systems, 45(3), 1-8.
O'Hara, J. K., & Lawton, R. J. (2020). At a critical juncture: Insights into the UK public's attitudes towards predictive risk stratification algorithms in healthcare. Health Expectations, 23(3), 693-701.
Rathore, M. M., Paul, A., & Wong, K. W. (2021). Applications of artificial intelligence and machine learning for COVID-19 (SARS-CoV-2) pandemic: A review. Chaos, Solitons & Fractals, 142, 110338.
Singh, R. P., Javaid, M., Haleem, A., Suman, R., & Bahl, S. (2020). Internet of things (IoT) applications to fight against COVID-19 pandemic. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 14(4), 521-524.
Wang, H., Wang, Z., Dong, Y., Chang, R., Xu, C., Yu, X., ... & Cai, Y. (2020). Phase-adjusted estimation of the number of Coronavirus Disease 2019 cases in Wuhan, China. Cell Discovery, 6(1), 1-8.
Zhang, J., & Litvinova, M. (2020). Evolving epidemiology and transmission dynamics of Coronavirus Disease 2019 outside Hubei province, China: A descriptive and modelling study. The Lancet Infectious Diseases, 20(7), 793-802.
Dhamodharan, B. (2023). Driving Business Value with AI: A Framework for MLOps-driven Enterprise Adoption. International Journal of Sustainable Development in Computing Science, 5(4), 1-10.
Dhamodharan, B. (2023). Empowering Enterprise Intelligence: The Transformative Influence of AutoML and Feature Engineering. International Journal of Creative Research In Computer Technology and Design, 5(5), 1-11.
Refbacks
- There are currently no refbacks.
Copyright (c) 2024 International Journal of Sustainable Development in Computing Science
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
A Double-Blind Peer Reviewed Journal