ArticleBMC medical informatics and decision making2022
Patient allocation method in major epidemics under the situation of hierarchical diagnosis and treatment.
Article in BMC medical informatics and decision making, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Determinants of occupational burnout among medical personnel in tertiary hospitals: the complex interplay of demographic characteristics, mental health, and self-efficacy.BMC health services research · 2026Article
- A stochastic programming approach for dynamic allocation of bed capacity and assignment of patients to collaborating hospitals during pandemic outbreaks.Health care management science · 2026Article
- Integrating epidemiological modeling and time-series forecasting to optimize pandemic patient allocation.International journal of health geographics · 2026Article
- Optimizing Emergency Response in Hospitals: A Systematic Review of Surge Capacity Planning and Crisis Resource Management.Healthcare (Basel, Switzerland) · 2025Review
- The analysis of factors influencing patient choice of healthcare providers between tertiary hospitals and community clinics.Frontiers in public health · 2025Article
- MED-ChatGPT CoPilot: a ChatGPT medical assistant for case mining and adjunctive therapy.Frontiers in medicine · 2024Article
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Authors and funding
5 authors.
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Abstract
objectivesPatients are classified according to the severity of their condition and graded according to the diagnosis and treatment capacity of medical institutions. This study aims to correctly assign patients to medical institutions for treatment and develop patient allocation and medical resource expansion schemes among hospitals in the medical network.
methodsIllness severity, hospital level, allocation matching benefit, distance traveled, and emergency medical resource fairness were considered. A multi-objective planning method was used to construct a patient allocation model during major epidemics. A simulation study was carried out in two scenarios to test the proposed method.
results(1) The single-objective model obtains an unbalanced solution in contrast to the multi-objective model. The proposed model considers multi-objective problems and balances the degree of patient allocation matching, distance traveled, and fairness. (2) The non-hierarchical model has crowded resources, and the hierarchical model assigns patients to matched medical institutions. (3) In the "demand exceeds supply" situation, the patient allocation model identified additional resources needed by each hospital.
conclusionResults verify the maneuverability and effectiveness of the proposed model. It can generate schemes for specific patient allocation and medical resource amplification and can serve as a quantitative decision-making tool in the context of major epidemics.
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