Stochastic Dominance (SD) criteria are decision making tools which allow us to choose among various strategies with only partial information on the decision makers' preferences. The notion of Stochastic Dominance has been extensively employed and developed in the area of economics, finance, agriculture, statistics, marketing and operation research since the late 1960s. For example, it may tell us which of two medical treatments with uncertain outcomes is preferred in the absence of full information on the patients' preferences. This paper presents a short review of the SD paradigm and demonstrates how the SD criteria may be employed in medical decision making, using the case of small abdominal aortic aneurysms as an illustration. Thus, for instance by assuming risk aversion one can employ second-degree stochastic dominance to divide the set of all possible treatments into the efficient set, from which the decision makers should always choose, and the inefficient (inferior) set. By employing Prospect Stochastic Dominance (PSD) a similar division can be conducted corresponding to all S-shaped utility functions.

Moshe Leshno

Haim Levy

Health care management science

医疗保健管理科学发表有关医疗保健提供、医疗保健管理和医疗保健政策的论文。论文应该有一个决策重点，并利用定量方法，包括管理科学、运筹学、分析、机器学习和其他新兴领域。文章必须清楚地阐明工作的相关性以及已实现或潜在的影响。如果有证据表明应用研究已被实施或为决策过程提供信息，则应用研究将被考虑并特别感兴趣。不鼓励发表描述已知方法的常规应用的论文。鼓励作者披露所有数据及其分析，并在适当时提供计算代码。下面提供各个部门的编辑声明。卫生保健分析部门编辑：MARGRÉT BJARNADÓTTIR，马里兰大学南KONG, PURDUE UNIVERSITY 随着计算能力和可用数据的爆炸式增长，我们看到医疗保健领域应用的分析发生了快速变化。医疗保健分析部门欢迎应用广泛分析方法的论文，包括那些植根于机器学习、生存分析和复杂事件分析的论文，这些方法使医疗保健专业人员能够找到改善卫生系统管理、患者参与、支出和诊断。我们特别鼓励将预测性分析和规范性分析结合起来以改善决策和医疗保健结果的论文。论文的贡献可以跨越多个维度，包括新方法、新建模技术和通过真实世界队列研究的医疗保健。以方法论为重点的论文除了显示出实际相关性外，还需要。同样，以应用为中心的论文应该通过应用严格的分析清楚地展示对现状和可用方法的改进。医疗保健运营管理部门编辑：NILAY TANIK ARGON，北卡罗来纳大学教堂山分校BOB BATT，威斯康星大学该部门邀请有关医疗保健系统运营的设计、控制和分析的高质量论文。我们寻求有关经典运营管理问题（例如调度、路由、排队、运输、患者流量和质量）以及由不断变化的医疗保健实践驱动的非传统问题的论文。经验、实验和分析（基于模型）的方法都是受欢迎的。论文可以借鉴跨学科的理论，并应从患者、服务提供者、组织（市政/政府/行业）和/或社会的角度提供对改进运营的见解。医疗保健管理科学实践部门编辑：VIKRAM TIWARI，范德比尔特大学医学中心该部门寻求院士和从业人员的研究，重点关注与医疗保健实践直接相关的基于管理科学的解决方案。相关性是通过对实践的影响以及研究人员与从业者在理解问题背景和开发解决方案方面参与的程度来判断的。有效性，即所呈现的结果在实践中适用或将适用的程度是一个关键的评估标准。除了满足期刊的原创性标准和对知识创造的实质性贡献外，还鼓励可以在其他组织中复制的研究。描述不成功的应用研究项目的论文可能会被考虑，如果有可概括的学习点来解决项目不成功的原因。卫生保健生产力分析部门编辑：JONAS SCHREYÖGG，汉堡大学该部门邀请具有严格方法和对政策和实践有重大影响的论文。论文通常应用理论和技术来衡量医疗保健组织和系统的生产力。该杂志欢迎最先进的参数和非参数技术，如数据包络分析、随机前沿分析或部分前沿分析。论文的贡献可以是多方面的，包括新方法、现有方法的新组合或现有方法在新环境中的应用。实证论文应产生可推广到一组选定的医疗保健组织之外的结果。所有论文都应包括对提高生产力的管理或政策影响的部分。公共卫生政策和医疗决策部门编辑：EBRU BISH，阿拉巴马大学JULIE L. HIGLE，南加州大学该部门邀请使用数据驱动方法的高质量论文解决公共卫生政策和医疗决策领域出现的重要问题。我们欢迎提交开发和应用数学和计算模型以支持针对这些问题的数据驱动和基于模型的分析的提交。公共卫生政策和医疗决策部门对以下论文特别感兴趣： 研究涉及卫生政策、治疗计划和设计以及临床应用的高影响问题；开发原始数据驱动模型，包括将疾病建模与筛查和/或治疗指南相结合的模型；使用基于模型的分析作为决策工具来确定最佳解决方案、见解和建议。文章必须清楚地阐明工作与决策和/或政策制定者的相关性以及对患者和/或社会的潜在影响。论文将包括方法学领域内的明确贡献，其中可能包括建模、分析或计算方法。新兴主题部门编辑：斯特拉斯克莱德大学亚历克·莫顿新兴主题将处理使用创新定量方法阐明医疗保健管理和前沿问题的论文政策。此类论文可能会处理由新的卫生技术或新的组织形式引起的分析挑战。属于该部门的论文也可能涉及对随着卫生系统越来越数字化而越来越多地捕获的新形式数据的分析。

The journal "Health Care Management Science" publishes research papers related to health care delivery, management, and policy. Papers should focus on decision-making and employ quantitative methods, including management science, operations research, analytics, machine learning, and other emerging fields. Articles must clearly articulate the relevance of the work and its realized or potential impact. Applied research will be considered and of particular interest if there is evidence that it has been implemented or has informed the decision-making process. Submissions that merely describe the routine application of known methods are discouraged. Authors are encouraged to disclose all data and analyses and, where appropriate, provide computational code. The editorial statements for various departments are as follows: Health Care Analytics Department Editors: MARGRÉT BJARNADÓTTIR (University of Maryland) and KONG (Purdue University). With the explosive growth of computational power and available data, we are witnessing rapid changes in the application of analytics in health care. The Health Care Analytics Department welcomes papers that apply a wide range of analytical methods, including those rooted in machine learning, survival analysis, and complex event analysis, which enable health care professionals to improve health system management, patient engagement, spending, and diagnosis. We particularly encourage papers that combine predictive and prescriptive analytics to enhance decision-making and health care outcomes. Contributions can span multiple dimensions, including new methods, new modeling techniques, and health care through real-world cohort studies. Methodologically focused papers must demonstrate practical relevance as well. Similarly, application-centered papers should clearly show improvements over the status quo and available methods through rigorous analysis. Health Care Operations Management Department Editors: NILAY TANIK ARGON (University of North Carolina at Chapel Hill) and BOB BATT (University of Wisconsin). This department invites high-quality papers on the design, control, and analysis of health care system operations. We seek research on traditional operations management issues (such as scheduling, routing, queuing, transportation, patient flow, and quality) as well as non-traditional issues driven by evolving health care practices. Empirical, experimental, and model-based analytical approaches are all welcome. Papers should draw on interdisciplinary theories and provide insights into operational improvements from the perspectives of patients, service providers, organizations (municipal/government/industry), and/or society. Health Care Management Science Practice Department Editor: VIKRAM TIWARI (Vanderbilt University Medical Center). This department seeks research from academics and practitioners focusing on management science-based solutions directly related to health care practice. Relevance is assessed by the impact on practice and the degree of engagement of researchers with practitioners in understanding the problem context and developing solutions. Effectiveness, or the extent to which the presented results are applicable or will be applicable in practice, is a key evaluation criterion. In addition to meeting the journal's originality standards and making substantial contributions 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 lessons to address the reasons for the project's lack of success. Health Care Productivity Analysis Department Editor: JONAS SCHREYÖGG (Hamburg University). This department invites papers with rigorous methodologies that have significant implications for policy and practice. Papers typically apply theories and techniques to measure the productivity of health care organizations and systems. The journal welcomes cutting-edge parametric and non-parametric techniques, such as data envelopment analysis, stochastic frontier analysis, or partial frontier analysis. Contributions can be multifaceted, including new methods, new combinations of existing methods, or applications of existing methods in new environments. Empirical papers should yield results that are generalizable beyond a selected group of health care organizations. All papers should include a section on the managerial or policy implications for improving productivity. Public Health Policy and Medical Decision-Making Department Editors: EBRU BISH (University of Alabama) and JULIE L. HIGLE (University of Southern California). This department invites high-quality papers using data-driven methods to address significant issues in public health policy and medical decision-making. We welcome submissions that develop and apply mathematical and computational models to support data-driven and model-based analyses addressing these issues. The Public Health Policy and Medical Decision-Making Department is particularly interested in papers that involve high-impact issues related to 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; and use model-based analyses as decision-making tools to identify optimal solutions, insights, and recommendations. Articles must clearly articulate the relevance of the work to decision-makers and/or policymakers and its potential impact on patients and/or society. Papers will include explicit contributions within the methodological domain, which may include modeling, analysis, or computational methods. Emerging Topics Department Editor: ALEX MORTON (University of Strathclyde). The Emerging Topics Department will address papers that use innovative quantitative methods to elucidate health care management and frontier policy issues. Such papers may address analytical challenges arising from new health technologies or new organizational forms. Papers in this department may also involve the analysis of new forms of data that are increasingly captured as health systems become more digitalized.

《Health Care Management Science》期刊发表关于医疗保健提供、管理和政策的研究论文。论文应聚焦于决策，并采用定量方法，包括管理科学、运筹学、分析、机器学习及其他新兴领域。文章必须清晰阐明研究的相关性及其已实现或潜在的影响。如果有证据表明应用研究已被实施或为决策过程提供了信息，则该研究将受到特别关注。不鼓励发表仅描述已知方法的常规应用的论文。鼓励作者披露所有数据及其分析，并在适当时提供计算代码。各部门的编辑声明如下：卫生保健分析部门编辑：MARGRÉT BJARNADÓTTIR（马里兰大学）和KONG（PURDUE UNIVERSITY）。随着计算能力和可用数据的快速增长，医疗保健领域的分析应用正在迅速变化。该部门欢迎应用广泛分析方法的论文，包括基于机器学习、生存分析和复杂事件分析的方法，这些方法能够帮助医疗保健专业人员改善卫生系统管理、患者参与、支出和诊断。我们特别鼓励将预测性分析与规范性分析结合，以改善决策和医疗保健结果的论文。论文的贡献可以跨越多个维度，包括新方法、新建模技术和通过真实世界队列研究的医疗保健。以方法论为重点的论文除了展示实际相关性外，还需具备实际应用价值。同样，以应用为中心的论文应通过严格的分析清晰展示对现状和可用方法的改进。医疗保健运营管理部门编辑：NILAY TANIK ARGON（北卡罗来纳大学教堂山分校）和BOB BATT（威斯康星大学）。该部门邀请有关医疗保健系统运营设计、控制和分析的高质量论文。我们寻求关于经典运营管理问题（如调度、路由、排队、运输、患者流量和质量）以及由不断变化的医疗保健实践驱动的非传统问题的研究。经验、实验和基于模型的分析方法均受到欢迎。论文应借鉴跨学科理论，并从患者、服务提供者、组织（市政/政府/行业）和/或社会的角度提供对改进运营的见解。医疗保健管理科学实践部门编辑：VIKRAM TIWARI（范德比尔特大学医学中心）。该部门寻求院士和从业人员的研究，重点关注与医疗保健实践直接相关的基于管理科学的解决方案。相关性通过对实践的影响以及研究人员与从业者在理解问题背景和开发解决方案方面的参与程度来判断。有效性，即所呈现结果在实践中的适用性，是一个关键评估标准。除了满足期刊的原创性标准和对知识创造的实质性贡献外，还鼓励可以在其他组织中复制的研究。描述不成功的应用研究项目的论文可能会被考虑，前提是有可概括的学习点来解决项目不成功的原因。卫生保健生产力分析部门编辑：JONAS SCHREYÖGG（汉堡大学）。该部门邀请具有严格方法和对政策及实践有重大影响的论文。论文通常应用理论和技术来衡量医疗保健组织和系统的生产力。该期刊欢迎最先进的参数和非参数技术，如数据包络分析、随机前沿分析或部分前沿分析。论文的贡献可以是多方面的，包括新方法、现有方法的新组合或现有方法在新环境中的应用。实证论文应产生可推广到一组选定的医疗保健组织之外的结果。所有论文都应包括对提高生产力的管理或政策影响的部分。公共卫生政策和医疗决策部门编辑：EBRU BISH（阿拉巴马大学）和JULIE L. HIGLE（南加州大学）。该部门邀请使用数据驱动方法的高质量论文，解决公共卫生政策和医疗决策领域的重要问题。我们欢迎提交开发和应用数学及计算模型以支持针对这些问题的数据驱动和基于模型的分析的论文。公共卫生政策和医疗决策部门特别感兴趣的论文包括：研究涉及卫生政策、治疗计划和设计以及临床应用的高影响问题；开发原始数据驱动模型，包括将疾病建模与筛查和/或治疗指南相结合的模型；使用基于模型的分析作为决策工具来确定最佳解决方案、见解和建议。文章必须清晰阐明工作与决策和/或政策制定者的相关性及其对患者和/或社会的潜在影响。论文将包括方法学领域的明确贡献，可能包括建模、分析或计算方法。新兴主题部门编辑：亚历克·莫顿（斯特拉斯克莱德大学）。新兴主题将处理使用创新定量方法阐明医疗保健管理和前沿问题的论文政策。此类论文可能会处理由新的卫生技术或新的组织形式引起的分析挑战。属于该部门的论文也可能涉及对随着卫生系统越来越数字化而越来越多地捕获的新形式数据的分析。

Category	Quartile
医学	3
医学, 卫生政策与服务	3

Stochastic dominance and medical decision making.

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Category	Quartile
HEALTH POLICY & SERVICES	2