基于dea的效率与公平平衡的集中资源分配：来自中国31个省份医疗服务的证据
IF 2.3
3区 医学
Q2 HEALTH POLICY & SERVICES
Health Care Management Science
Pub Date : 2025-03-01
Epub Date: 2025-03-21
DOI:10.1007/s10729-025-09698-7
引用次数: 0
摘要
在医疗投入不断增加的背景下，如何实现产出最大化，保证资源配置的公平性，是中国医疗体制改革的关键问题。基于广义dea的资源分配模型（模型1）在资源分配中追求DMU效率的最大化，不考虑公平性，在目标函数中只考虑产出而不考虑投入，可能产生多解问题。因此，本文提出了一种基于DEA的效率与公平平衡的集中式资源配置模型（模型2），其中效率与公平同时通过目标函数中的产出与投入指标来衡量，这更符合DEA方法的本质。模型2通过在目标函数中同时引入输出和输入，有效地防止了多解问题，并证明了其帕累托效率。模型2的主要优点是可以优化资源配置的效率和公平性；特别是，它可以确保所有dmu在绝对和相对方面的公平。此外，我们对模型2在中国大陆31个省份集中医疗服务资源配置中的应用进行了说明和检验。我们从效率和公平两个方面对比模型1，考察模型2的性质和有效性。从效率值与效率差、投入与产出指标、分配偏差三个角度衡量效率与公平。结果表明，模型2在效率和公平两方面都优于模型1。本文章由计算机程序翻译，如有差异，请以英文原文为准。
DEA-based centralized resource allocation with a balance between efficiency and equity: evidence from healthcare services across 31 provinces in China.
In the context of increasing investment in healthcare, the key issue of China's healthcare system reform is how to maximize output and ensure the equity of resource allocation. The generalized DEA-based resource allocation model (Model 1) pursues the maximization of DMU efficiency in resource allocation without considering equity, and it could yield a multi-solution problem by considering only the outputs instead of the inputs in the objective function. Thus, a DEA-based centralized resource allocation model with a balance between efficiency and equity (Model 2) is proposed, in which efficiency and equity are measured by output and input indicators in the objective function simultaneously, this could be more consistent with the essence of the DEA method. Model 2 effectively prevents the multi-solution problem by introducing both outputs and inputs into the objective function, and its Pareto-efficiency is proven. The main advantage of the proposed Model 2 is that efficiency and equity can be optimized in resource allocation; in particular, it can ensure equity for all DMUs in both absolute and relative terms. Furthermore, we illustrate and examine the application of Model 2 with centralized healthcare service resource allocation across 31 provinces in mainland China. We investigate the properties and effectiveness of Model 2 by comparison with Model 1 in terms of both efficiency and equity. Efficiency and equity are measured from three perspectives: efficiency values and slacks, input and output indicators, and allocation deviation. The results prove that Model 2 is superior to Model 1 in terms of both efficiency and equity.
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来源期刊
Health Care Management Science
HEALTH POLICY & SERVICES-
CiteScore
7.20
自引率
5.60%
发文量
40
期刊介绍：
Health Care Management Science publishes papers dealing with health care delivery, health care management, and health care policy. Papers should have a decision focus and make use of quantitative methods including management science, operations research, analytics, machine learning, and other emerging areas. Articles must clearly articulate the relevance and the realized or potential impact of the work. Applied research will be considered and is of particular interest if there is evidence that it was implemented or informed a decision-making process. Papers describing routine applications of known methods are discouraged.
Authors are encouraged to disclose all data and analyses thereof, and to provide computational code when appropriate.
Editorial statements for the individual departments are provided below.
Health Care Analytics
Departmental Editors:
Margrét Bjarnadóttir, University of Maryland
Nan Kong, Purdue University
With the explosion in computing power and available data, we have seen fast changes in the analytics applied in the healthcare space. The Health Care Analytics department welcomes papers applying a broad range of analytical approaches, including those rooted in machine learning, survival analysis, and complex event analysis, that allow healthcare professionals to find opportunities for improvement in health system management, patient engagement, spending, and diagnosis. We especially encourage papers that combine predictive and prescriptive analytics to improve decision making and health care outcomes.
The contribution of papers can be across multiple dimensions including new methodology, novel modeling techniques and health care through real-world cohort studies. Papers that are methodologically focused need in addition to show practical relevance. Similarly papers that are application focused should clearly demonstrate improvements over the status quo and available approaches by applying rigorous analytics.
Health Care Operations Management
Departmental Editors:
Nilay Tanik Argon, University of North Carolina at Chapel Hill
Bob Batt, University of Wisconsin
The department invites high-quality papers on the design, control, and analysis of operations at healthcare systems. We seek papers on classical operations management issues (such as scheduling, routing, queuing, transportation, patient flow, and quality) as well as non-traditional problems driven by everchanging healthcare practice. Empirical, experimental, and analytical (model based) methodologies are all welcome. Papers may draw theory from across disciplines, and should provide insight into improving operations from the perspective of patients, service providers, organizations (municipal/government/industry), and/or society.
Health Care Management Science Practice
Departmental Editor:
Vikram Tiwari, Vanderbilt University Medical Center
The department seeks research from academicians and practitioners that highlights Management Science based solutions directly relevant to the practice of healthcare. Relevance is judged by the impact on practice, as well as the degree to which researchers engaged with practitioners in understanding the problem context and in developing the solution. Validity, that is, the extent to which the results presented do or would apply in practice is a key evaluation criterion. In addition to meeting the journal’s standards of originality and substantial contribution to knowledge creation, research that can be replicated in other organizations is encouraged. Papers describing unsuccessful applied research projects may be considered if there are generalizable learning points addressing why the project was unsuccessful.
Health Care Productivity Analysis
Departmental Editor:
Jonas Schreyögg, University of Hamburg
The department invites papers with rigorous methods and significant impact for policy and practice. Papers typically apply theory and techniques to measuring productivity in health care organizations and systems. The journal welcomes state-of-the-art parametric as well as non-parametric techniques such as data envelopment analysis, stochastic frontier analysis or partial frontier analysis. The contribution of papers can be manifold including new methodology, novel combination of existing methods or application of existing methods to new contexts. Empirical papers should produce results generalizable beyond a selected set of health care organizations. All papers should include a section on implications for management or policy to enhance productivity.
Public Health Policy and Medical Decision Making
Departmental Editors:
Ebru Bish, University of Alabama
Julie L. Higle, University of Southern California
The department invites high quality papers that use data-driven methods to address important problems that arise in public health policy and medical decision-making domains. We welcome submissions that develop and apply mathematical and computational models in support of data-driven and model-based analyses for these problems.
The Public Health Policy and Medical Decision-Making Department is particularly interested in papers that:
Study high-impact problems involving health policy, treatment planning and design, and clinical applications;
Develop original data-driven models, including those that integrate disease modeling with screening and/or treatment guidelines;
Use model-based analyses as decision making-tools to identify optimal solutions, insights, recommendations.
Articles must clearly articulate the relevance of the work to decision and/or policy makers and the potential impact on patients and/or society. Papers will include articulated contributions within the methodological domain, which may include modeling, analytical, or computational methodologies.
Emerging Topics
Departmental Editor:
Alec Morton, University of Strathclyde
Emerging Topics will handle papers which use innovative quantitative methods to shed light on frontier issues in healthcare management and policy. Such papers may deal with analytic challenges arising from novel health technologies or new organizational forms. Papers falling under this department may also deal with the analysis of new forms of data which are increasingly captured as health systems become more and more digitized.
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