Journal Title:Health Care Management Science
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.
《医疗保健管理科学》发表的论文涉及医疗保健服务、医疗保健管理和医疗保健政策。论文应该有一个决策重点,并使用定量方法,包括管理科学、运筹学、统计学、分析学、计量经济学、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和已实现或潜在的影响。如果有证据表明应用研究已经实施或为决策过程提供了信息,则将考虑应用研究,并特别感兴趣。不鼓励发表描述已知方法常规应用的论文。
Health Care Management Science创刊于1998年,由Springer Nature出版商出版,收稿方向涵盖HEALTH POLICY & SERVICES全领域,此刊是中等级别的SCI期刊,所以过审相对来讲不是特别难,但是该刊专业认可度不错,仍然是一本值得选择的SCI期刊 。平均审稿速度 ,影响因子指数2.3,该期刊近期没有被列入国际期刊预警名单,广大学者值得一试。
大类学科 | 分区 | 小类学科 | 分区 | Top期刊 | 综述期刊 |
医学 | 3区 | HEALTH POLICY & SERVICES 卫生政策与服务 | 3区 | 否 | 否 |
名词解释:
中科院分区也叫中科院JCR分区,基础版分为13个大类学科,然后按照各类期刊影响因子分别将每个类别分为四个区,影响因子5%为1区,6%-20%为2区,21%-50%为3区,其余为4区。
大类学科 | 分区 | 小类学科 | 分区 | Top期刊 | 综述期刊 |
医学 | 2区 | HEALTH POLICY & SERVICES 卫生政策与服务 | 1区 | 否 | 否 |
大类学科 | 分区 | 小类学科 | 分区 | Top期刊 | 综述期刊 |
医学 | 2区 | HEALTH POLICY & SERVICES 卫生政策与服务 | 2区 | 否 | 否 |
大类学科 | 分区 | 小类学科 | 分区 | Top期刊 | 综述期刊 |
医学 | 2区 | HEALTH POLICY & SERVICES 卫生政策与服务 | 2区 | 否 | 否 |
大类学科 | 分区 | 小类学科 | 分区 | Top期刊 | 综述期刊 |
医学 | 3区 | HEALTH POLICY & SERVICES 卫生政策与服务 | 2区 | 否 | 否 |
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% |
名词解释:
WOS即Web of Science,是全球获取学术信息的重要数据库,Web of Science包括自然科学、社会科学、艺术与人文领域的信息,来自全世界近9,000种最负盛名的高影响力研究期刊及12,000多种学术会议多学科内容。给期刊分区时会按照某一个学科领域划分,根据这一学科所有按照影响因子数值降序排名,然后平均分成4等份,期刊影响因子值高的就会在高分区中,最后的划分结果分别是Q1,Q2,Q3,Q4,Q1代表质量最高。
CiteScore | SJR | SNIP | CiteScore排名 | ||||||||||||
7.2 | 0.958 | 1.293 |
|
名词解释:
CiteScore:衡量期刊所发表文献的平均受引用次数。
SJR:SCImago 期刊等级衡量经过加权后的期刊受引用次数。引用次数的加权值由施引期刊的学科领域和声望 (SJR) 决定。
SNIP:每篇文章中来源出版物的标准化影响将实际受引用情况对照期刊所属学科领域中预期的受引用情况进行衡量。
是否OA开放访问: | h-index: | 年文章数: |
未开放 | -- | 35 |
Gold OA文章占比: | 2021-2022最新影响因子(数据来源于搜索引擎): | 开源占比(OA被引用占比): |
31.75% | 2.3 | 0.19... |
研究类文章占比:文章 ÷(文章 + 综述) | 期刊收录: | 中科院《国际期刊预警名单(试行)》名单: |
100.00% | SCIE、SSCI | 否 |
历年IF值(影响因子):
历年引文指标和发文量:
历年中科院JCR大类分区数据:
历年自引数据:
2023-2024国家/地区发文量统计:
国家/地区 | 数量 |
USA | 55 |
Canada | 17 |
GERMANY (FED REP GER) | 14 |
CHINA MAINLAND | 10 |
England | 9 |
Italy | 7 |
Taiwan | 6 |
Turkey | 6 |
Spain | 4 |
Australia | 3 |
2023-2024机构发文量统计:
机构 | 数量 |
UNIVERSITY OF HAMBURG | 6 |
KLINIKUM AUGSBURG | 5 |
STATE UNIVERSITY SYSTEM OF FLORI... | 5 |
UNIVERSITY OF AUGSBURG | 5 |
UNIVERSITY OF WISCONSIN SYSTEM | 5 |
YALE UNIVERSITY | 5 |
STANFORD UNIVERSITY | 4 |
UNIVERSITY OF MONTREAL | 4 |
UNIVERSITY OF NORTH CAROLINA | 4 |
WESTERN UNIVERSITY (UNIVERSITY O... | 4 |
近年引用统计:
期刊名称 | 数量 |
HEALTH CARE MANAG SC | 119 |
OPER RES | 55 |
HEALTH CARE MANAGE R | 40 |
MANAGE SCI | 40 |
SOCIO-ECON PLAN SCI | 37 |
OMEGA-INT J MANAGE S | 35 |
HEALTH POLICY | 32 |
HEALTH ECON | 28 |
J PROD ANAL | 27 |
EUR J HEALTH ECON | 20 |
近年被引用统计:
期刊名称 | 数量 |
HEALTH CARE MANAG SC | 119 |
OMEGA-INT J MANAGE S | 30 |
INT J ENV RES PUB HE | 19 |
INT J HEALTH PLAN M | 12 |
SOCIO-ECON PLAN SCI | 12 |
J PROD ANAL | 11 |
J OPER RES SOC | 8 |
SERV SCI | 8 |
SUSTAINABILITY-BASEL | 6 |
HEALTHCARE-BASEL | 5 |
近年文章引用统计:
文章名称 | 数量 |
The use of Data Envelopment Anal... | 30 |
An in-depth discussion and illus... | 12 |
Operations research in intensive... | 11 |
Comparison of emergency departme... | 10 |
A hybrid data envelopment analys... | 7 |
Optimal healthcare decision maki... | 6 |
Classification of hospital admis... | 6 |
Technical and scale efficiency i... | 5 |
Does participation in health inf... | 5 |
Chemotherapy appointment schedul... | 5 |
同小类学科的其他优质期刊 | 影响因子 | 中科院分区 |
Toxicon | 2.6 | 4区 |
Journal Of Knee Surgery | 1.6 | 4区 |
International Journal Of Sports Medicine | 2 | 4区 |
Journal Of Ethnopharmacology | 4.8 | 2区 |
Stem Cells International | 3.8 | 3区 |
Journal Of Ethnobiology And Ethnomedicine | 2.9 | 2区 |
Transplant Immunology | 1.6 | 4区 |
Nature Reviews Endocrinology | 31 | 1区 |
Asian Journal Of Surgery | 3.5 | 3区 |
Medicine | 1.3 | 4区 |
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