补助政策对创伤中心财务状况的影响。
IF 2.3
3区 医学
Q2 HEALTH POLICY & SERVICES
Health Care Management Science
Pub Date : 2025-06-01
Epub Date: 2025-04-23
DOI:10.1007/s10729-025-09701-1
引用次数: 0
摘要
创伤中心（tc）在提高严重受伤个体的患者安全方面发挥着至关重要的作用，但需要大量的财政资源才能有效地运作。由于无法从无偿护理中收回成本，低保险地区的tc尤其面临财政赤字和关闭威胁的风险。虽然美国的一些州为这些中心提供财政补贴，但人们对各州补贴政策的多样性及其对技术中心财务可行性的影响知之甚少。为了解决这个问题，我们引入了一个广义的补贴分配公式，该公式包含了来自各个州政策的关键组成部分。在此基础上，我们进一步提出了一个TC财务评估模型，该模型采用蒙特卡罗模拟来评估不同补贴政策在三个指标上的影响。利用美国多个州的实际数据和国家保险统计数据，我们进行了全面的实验研究。研究结果表明，创伤服务中心的财务绩效可能受到补助总额、创伤服务区内未参保水平和具体补贴分配政策的影响。这项研究为创伤决策者提供了一个定量的工具来评估、比较和设计适合他们独特的人口和经济背景的补贴政策，有可能导致一种更标准化的方法来减轻各州之间现有的政策差异。本文章由计算机程序翻译，如有差异，请以英文原文为准。
Impact of subsidy policies on the financial status of trauma centers.
Trauma centers (TCs) play a crucial role in improving patient safety of severely injured individuals, but require substantial financial resources to operate effectively. TCs in low-insured areas are particularly at risk of being confronted with financial deficits, and a threat of closure, due to the inability to recover costs from uncompensated care. While some states in the US provide financial subsidies to support these centers, the diversity of state subsidy policies and their impacts on TC financial viability are poorly understood. To address this, we introduce a generalized subsidy distribution formula that incorporates key components from various state policies. Based on that, we further propose a TC Financial Evaluation Model that employs Monte Carlo simulation to assess the effects of different subsidy policies along three proposed metrics. Utilizing realistic data from multiple US states and national insurance statistics, we conduct a comprehensive experimental study. Our findings suggest that the financial performance of TCs could be affected by the total subsidy amount, the Uninsured level within the Trauma Service Area (TSA), and the specific subsidy distribution policy employed. This research provides trauma decision-makers a quantitative tool to evaluate, compare, and design subsidy policies tailored to their unique demographic and economic contexts, potentially leading to a more standardized approach to mitigate existing policy disparities across states.
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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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