ArticleComputational and mathematical methods in medicine2021
Patient Mix Optimization in Admission Planning under Multitype Patients and Priority Constraints.
Article in Computational and mathematical methods in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed, 4 citations in OpenAlex.
- Optimizing Emergency Response in Hospitals: A Systematic Review of Surge Capacity Planning and Crisis Resource Management.Healthcare (Basel, Switzerland) · 2025Review
- An interval type-2 fuzzy multi-criteria decision-making approach for patient bed allocation.Scientific reports · 2025Article
- The working experience of medical staff in the hospital-wide bed-sharing mode: A qualitative study.Nursing open · 2023Article
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Authors and funding
3 authors at 2 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Hospital beds are one of the most critical medical resources. Large hospitals in China have caused bed utilization rates to exceed 100% due to long-term extra beds. To alleviate the contradiction between the supply of high-quality medical resources and the demand for hospitalization, in this paper, we address the decision of choosing a case mix for a respiratory medicine department. We aim to generate an optimal admission plan of elective patients with the stochastic length of stay and different resource consumption. We assume that we can classify elective patients according to their registration information before admission. We formulated a general integer programming model considering heterogeneous patients and introducing patient priority constraints. The mathematical model is used to generate a scientific and reasonable admission planning, determining the best admission mix for multitype patients in a period. Compared with model II that does not consider priority constraints, model I proposed in this paper is better in terms of admissions and revenue. The proposed model I can adjust the priority parameters to meet the optimal output under different goals and scenarios. The daily admission planning for each type of patient obtained by model I can be used to assist the patient admission management in large general hospitals.
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