Emergency drug procurement planning based on big-data driven morbidity prediction
Open accessAbstract
Due to the uncertainty of diseases, traditional approaches of drug procurement planning in hospitals often cause drug overstocking or understocking, which can have strong negative effects on healthcare services. This paper proposes a big-data driven approach, which uses a deep neural network to predict morbidities of acute gastrointestinal infections based on a huge amount of environmental data, and then constructs an optimization problem of drug procurement planning for maximizing the expected therapeutic effect on the predicted cases. The problem is solved by an efficient heuristic optimization algorithm. Computational experiments demonstrate the performance advantages of both the deep learning model and the heuristic algorithm over existing ones, and two real case studies in Central China show that the average prediction error of our approach is only 8% and the estimated recovery rate reaches 99%, much better than the currently used method. Our approach can also be extended for many other medical resource planning problems.
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A heuristic optimization algorithm based on a deep neural network provides quick and efficient solutions for emergency drug procurement planning, better than current methods.
“Two case studies illustrate that such methodology provides quick and efficient solutions, which are better than the methods currently used in drug procurement.”
From Transformative Procurement Trends: Integrating Industry 4.0 Technologies for Enhanced Procurement Processes (2023) · cites this paper as [96]
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“Song et al. [96] discuss the significance of a big-data-driven approach for emergency drug procurement planning.”
Background
- Authors
- Song, Q., Zheng, Y.-J., Huang, Y.-J., Xu, Z.-G., Sheng, W.-G., & Yang, J.
- Affiliations
- Hangzhou Normal University (CN)
- Zhejiang University of Technology (CN)
- Venue
- IEEE Transactions on Industrial Informatics
- Published
- 2019-12
- Volume
- 15
- Issue
- 12
- Pages
- 6379-6388
- ISSN
- 1551-3203 (ISSN-L)
- 1941-0050
- OpenAlex
- W2889877791
- Cited by
- 24
- References
- 42
- Citations by year
- 2026: 2 · 2025: 2 · 2024: 1 · 2023: 3 · 2022: 5 · 2021: 3 · 2020: 5 · 2019: 3
- FWCI
- 4.15
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- IEEE Transactions on Industrial Informatics (publishedVersion)
- Funders
- National Natural Science Foundation of China (61473263, 61573316, 61872123)
- Natural Science Foundation of Zhejiang Province (LY18F030023)
- Topics
- Social Sciences › Business, Management and Accounting › Management Information Systems › Supply Chain and Inventory Management
- Health Sciences › Health Professions › Emergency Medical Services › Healthcare Operations and Scheduling Optimization
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- morbidity prediction, acute gastrointestinal infection, environmental data, deep neural network, heuristic optimization, healthcare resource planning, recovery rate
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