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中国医院跨区域结算方式与恶性肿瘤患者就医行为:一个演化博弈模型

The cross-regional settlement methods in hospitals and the treatment-seeking behavior of patients with malignant tumors in China: an evolutionary game model.

作者信息

Zhao Xinzhe, Li Linjin, Zhang Dan

机构信息

Genertec Universal Medical Group, Beijing, China.

Institute for Hospital Management, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China.

出版信息

Front Public Health. 2024 Jul 17;12:1427164. doi: 10.3389/fpubh.2024.1427164. eCollection 2024.

DOI:10.3389/fpubh.2024.1427164
PMID:39086813
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11289844/
Abstract

BACKGROUND

Cross-regional settlement management is a key indicator of national health insurance system maturity. Given the significant demand for cross-regional medical treatment among Chinese patients with malignant tumors and the territorially managed health insurance system, further research is necessary to explore the relationship between hospital settlement methods and treatment-seeking behaviors among these patients. This study introduces and validates an evolutionary game model that provides a theoretical foundation for direct settlement policies in cross-regional treatment.

METHODS

An evolutionary game model was constructed with patients and hospitals serving as strategic players within a dynamic system. This model integrates the patients' treatment utility, medical and nonmedical costs, and hospitals' financial and technological advancement benefits.

RESULTS

The evolutionary stability analysis revealed seven-game outcomes between hospitals and patients with malignant tumors. The numerical simulations suggest an evolutionary convergence toward strategy (1, 0), indicating a trend where patients with malignant tumors opt for cross-regional treatment, yet hospitals choose not to implement a direct settlement policy. Parameter sensitivity analysis showed that the parameters set in this study affected player behavioral choices and game equilibria.

CONCLUSION

A strong demand for cross-regional medical treatment among Chinese patients with malignant tumors, and some hospitals require more incentives to implement cross-regional settlements. The key factors influencing the willingness of some patients with malignant tumors to resettle include the costs of in-area medical care, costs of cross-regional treatment without direct settlement, and the utility of cross-regional treatment. Technological advancement benefits and input costs influence some hospitals' motivation to adopt cross-regional settlements. Policy adjustments that effectively implement direct settlement policies can facilitate equilibrium, enhance the initiatives of some local health insurance management departments, improve the accessibility and efficiency of medical services, and reduce nonmedical expenses for patients.

摘要

背景

跨省异地就医结算管理是国家医疗保险制度成熟度的关键指标。鉴于中国恶性肿瘤患者对跨省异地就医有巨大需求,且医保制度实行属地管理,有必要进一步研究这些患者的医院结算方式与就医行为之间的关系。本研究引入并验证了一种演化博弈模型,为跨省异地就医直接结算政策提供了理论基础。

方法

构建一个演化博弈模型,将患者和医院作为动态系统中的战略参与者。该模型整合了患者的就医效用、医疗和非医疗费用,以及医院的财务和技术进步收益。

结果

演化稳定性分析揭示了恶性肿瘤患者与医院之间的七种博弈结果。数值模拟表明向策略(1, 0)的演化收敛,这表明恶性肿瘤患者选择跨省异地就医,但医院选择不实施直接结算政策的趋势。参数敏感性分析表明,本研究设定的参数影响参与者的行为选择和博弈均衡。

结论

中国恶性肿瘤患者对跨省异地就医有强烈需求,一些医院需要更多激励措施来实施跨省异地结算。影响部分恶性肿瘤患者异地就医意愿的关键因素包括当地就医费用、无直接结算的跨省异地就医费用以及跨省异地就医效用。技术进步收益和投入成本影响一些医院采用跨省异地结算的积极性。有效实施直接结算政策的政策调整可以促进均衡,增强一些地方医保管理部门的积极性,提高医疗服务的可及性和效率,并降低患者的非医疗费用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bb60/11289844/6ab6e4d9cb4f/fpubh-12-1427164-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bb60/11289844/608970760072/fpubh-12-1427164-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bb60/11289844/4f25a52d1190/fpubh-12-1427164-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bb60/11289844/ef07f0f28206/fpubh-12-1427164-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bb60/11289844/bbf6c86a14e8/fpubh-12-1427164-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bb60/11289844/cdecd82d0f75/fpubh-12-1427164-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bb60/11289844/8f34a947b1fb/fpubh-12-1427164-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bb60/11289844/6ab6e4d9cb4f/fpubh-12-1427164-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bb60/11289844/608970760072/fpubh-12-1427164-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bb60/11289844/4f25a52d1190/fpubh-12-1427164-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bb60/11289844/ef07f0f28206/fpubh-12-1427164-g003.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bb60/11289844/cdecd82d0f75/fpubh-12-1427164-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bb60/11289844/8f34a947b1fb/fpubh-12-1427164-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bb60/11289844/6ab6e4d9cb4f/fpubh-12-1427164-g007.jpg

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