当前位置: 首页 SCI 期刊 医学 Health Care Management Science(非官网)
Health Care Management Science

Health Care Management ScienceSCIESSCI

国际简称:HEALTH CARE MANAG SC  参考译名:健康保健管理科学

  • 中科院分区

    3区

  • CiteScore分区

    Q1

  • JCR分区

    Q2

基本信息:
ISSN:1386-9620
E-ISSN:1572-9389
是否OA:未开放
是否预警:否
TOP期刊:否
出版信息:
出版地区:UNITED STATES
出版商:Springer Nature
出版语言:English
出版周期:4 issues per year
出版年份:1998
研究方向:HEALTH POLICY & SERVICES
评价信息:
影响因子:2.3
CiteScore指数:7.2
SJR指数:0.958
SNIP指数:1.293
发文数据:
Gold OA文章占比:31.75%
研究类文章占比:100.00%
年发文量:35
自引率:0.0555...
开源占比:0.1972
出版撤稿占比:0
出版国人文章占比:0.06
OA被引用占比:0.0517...
英文简介 期刊介绍 CiteScore数据 中科院SCI分区 JCR分区 发文数据 常见问题

英文简介Health Care Management Science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.

期刊简介Health Care Management Science期刊介绍

《Health Care Management Science》自1998出版以来,是一本医学优秀杂志。致力于发表原创科学研究结果,并为医学各个领域的原创研究提供一个展示平台,以促进医学领域的的进步。该刊鼓励先进的、清晰的阐述,从广泛的视角提供当前感兴趣的研究主题的新见解,或审查多年来某个重要领域的所有重要发展。该期刊特色在于及时报道医学领域的最新进展和新发现新突破等。该刊近一年未被列入预警期刊名单,目前已被权威数据库SCIE、SSCI收录,得到了广泛的认可。

该期刊投稿重要关注点:

Cite Score数据(2024年最新版)Health Care Management Science Cite Score数据

  • CiteScore:7.2
  • SJR:0.958
  • SNIP:1.293
学科类别 分区 排名 百分位
大类:Health Professions 小类:General Health Professions Q1 3 / 21

88%

大类:Health Professions 小类:Medicine (miscellaneous) Q1 61 / 398

84%

CiteScore 是由Elsevier(爱思唯尔)推出的另一种评价期刊影响力的文献计量指标。反映出一家期刊近期发表论文的年篇均引用次数。CiteScore以Scopus数据库中收集的引文为基础,针对的是前四年发表的论文的引文。CiteScore的意义在于,它可以为学术界提供一种新的、更全面、更客观地评价期刊影响力的方法,而不仅仅是通过影响因子(IF)这一单一指标来评价。

历年Cite Score趋势图

中科院SCI分区Health Care Management Science 中科院分区

中科院 2023年12月升级版 综述期刊:否 Top期刊:否
大类学科 分区 小类学科 分区
医学 3区 HEALTH POLICY & SERVICES 卫生政策与服务 3区

中科院分区表 是以客观数据为基础,运用科学计量学方法对国际、国内学术期刊依据影响力进行等级划分的期刊评价标准。它为我国科研、教育机构的管理人员、科研工作者提供了一份评价国际学术期刊影响力的参考数据,得到了全国各地高校、科研机构的广泛认可。

中科院分区表 将所有期刊按照一定指标划分为1区、2区、3区、4区四个层次,类似于“优、良、及格”等。最开始,这个分区只是为了方便图书管理及图书情报领域的研究和期刊评估。之后中科院分区逐步发展成为了一种评价学术期刊质量的重要工具。

历年中科院分区趋势图

JCR分区Health Care Management Science JCR分区

2023-2024 年最新版
按JIF指标学科分区 收录子集 分区 排名 百分位
学科:HEALTH POLICY & SERVICES SSCI Q2 52 / 118

56.4%

按JCI指标学科分区 收录子集 分区 排名 百分位
学科:HEALTH POLICY & SERVICES SSCI Q1 25 / 119

79.41%

JCR分区的优势在于它可以帮助读者对学术文献质量进行评估。不同学科的文章引用量可能存在较大的差异,此时单独依靠影响因子(IF)评价期刊的质量可能是存在一定问题的。因此,JCR将期刊按照学科门类和影响因子分为不同的分区,这样读者可以根据自己的研究领域和需求选择合适的期刊。

历年影响因子趋势图

发文数据

2023-2024 年国家/地区发文量统计
  • 国家/地区数量
  • USA55
  • Canada17
  • GERMANY (FED REP GER)14
  • CHINA MAINLAND10
  • England9
  • Italy7
  • Taiwan6
  • Turkey6
  • Spain4
  • Australia3

投稿常见问题

通讯方式:Health Care Manag. Sci.。