Locating trauma centers considering patient safety.

IF 2.3 3区医学 Q2 HEALTH POLICY & SERVICES

Health Care Management Science Pub Date : 2022-06-01 Epub Date: 2022-01-13 DOI:10.1007/s10729-021-09576-y

Sagarkumar Hirpara, Monit Vaishnav, Pratik J Parikh, Nan Kong, Priti Parikh

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引用次数: 2

Abstract

Trauma continues to be the leading cause of death and disability in the U.S. for those under the age of 44, making it a prominent public health problem. Recent literature suggests that geographical maldistribution of Trauma Centers (TCs), and the resultant increase of the access time to the nearest TC, could impact patient safety and increase disability or mortality. To address this issue, we introduce the Trauma Center Location Problem (TCLP) that determines the optimal number and location of TCs in order to improve patient safety. We model patient safety through a surrogate measure of mistriages, which refers to a mismatch in the injury severity of a trauma patient and the destination hospital. Our proposed bi-objective optimization model directly accounts for the two types of mistriages, system-related under-triage (srUT) and over-triage (srOT), both of which are estimated using a notional tasking algorithm. We propose a heuristic based on the Particle Swarm Optimization framework to efficiently derive a near-optimal solution to the TCLP for realistic problem sizes. Based on 2012 data from the state of Ohio, we observe that the solutions are sensitive to the choice of weights for srUT and srOT, volume requirements at a TC, and the two thresholds used to mimic EMS decisions. Using our approach to optimize that network resulted in over 31.5% reduction in the objective with only 1 additional TC; redistribution of the existing 21 TCs led to 30.4% reduction.

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考虑到病人的安全，确定创伤中心。

创伤仍然是美国44岁以下人群死亡和残疾的主要原因，使其成为一个突出的公共卫生问题。最近的文献表明，创伤中心(TC)的地理分布不均，以及由此导致的到最近的TC的时间增加，可能会影响患者的安全，增加残疾或死亡率。为了解决这个问题，我们引入了创伤中心选址问题(TCLP)，该问题决定了创伤中心的最佳数量和位置，以提高患者的安全。我们通过伤害的替代措施来模拟患者安全，这是指创伤患者和目的地医院的伤害严重程度不匹配。我们提出的双目标优化模型直接解释了两种类型的错误，即系统相关的分类不足(srUT)和过度分类(srOT)，这两种类型的错误都是使用概念任务算法估计的。我们提出了一种基于粒子群优化框架的启发式算法，以有效地推导出实际问题规模的TCLP的近最优解。基于2012年俄亥俄州的数据，我们观察到解决方案对srt和srt的权重选择、TC的体积要求以及用于模拟EMS决策的两个阈值都很敏感。使用我们的方法优化该网络，仅增加1个TC，目标降低了31.5%以上;重新分配现有的21个过渡税导致减少30.4%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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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

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.