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Health Care Management Science
  • 數據庫收錄SCIE/SSCI
  • 創刊年份1998年
  • 年發文量35

Health Care Management Science

期刊中文名:健康保健管理科學ISSN:1386-9620E-ISSN:1572-9389

該雜志國際簡稱:HEALTH CARE MANAG SC,是由出版商Springer Nature出版的一本致力于發布醫學研究新成果的的專業學術期刊。該雜志以HEALTH POLICY & SERVICES研究為重點,主要發表刊登有創見的學術論文文章、行業最新科研成果,扼要報道階段性研究成果和重要研究工作的最新進展,選載對學科發展起指導作用的綜述與專論,促進學術發展,為廣大讀者服務。該刊是一本國際優秀雜志,在國際上有很高的學術影響力。

基本信息:
期刊簡稱:HEALTH CARE MANAG SC
是否OA:未開放
是否預警:
Gold OA文章占比:31.75%
出版信息:
出版地區:UNITED STATES
出版周期:4 issues per year
出版語言:English
出版商:Springer Nature
評價信息:
中科院分區:3區
JCR分區:Q2
影響因子:2.3
CiteScore:7.2
雜志介紹 中科院JCR分區 JCR分區 CiteScore 投稿經驗

雜志介紹

Health Care Management Science雜志介紹

《Health Care Management Science》是一本以English為主的未開放獲取國際優秀期刊,中文名稱健康保健管理科學,本刊主要出版、報道醫學-HEALTH POLICY & SERVICES領域的研究動態以及在該領域取得的各方面的經驗和科研成果,介紹該領域有關本專業的最新進展,探討行業發展的思路和方法,以促進學術信息交流,提高行業發展。該刊已被國際權威數據庫SCIE、SSCI收錄,為該領域相關學科的發展起到了良好的推動作用,也得到了本專業人員的廣泛認可。該刊最新影響因子為2.3,最新CiteScore 指數為7.2。

英文介紹

Health Care Management Science雜志英文介紹

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 Schrey?gg, 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.

中科院SCI分區

Health Care Management Science雜志中科院分區信息

2023年12月升級版
綜述:
TOP期刊:
大類:醫學 3區
小類:

HEALTH POLICY & SERVICES
衛生政策與服務 3區

2022年12月升級版
綜述:
TOP期刊:
大類:醫學 2區
小類:

HEALTH POLICY & SERVICES
衛生政策與服務 1區

2021年12月舊的升級版
綜述:
TOP期刊:
大類:醫學 2區
小類:

HEALTH POLICY & SERVICES
衛生政策與服務 2區

2021年12月升級版
綜述:
TOP期刊:
大類:醫學 2區
小類:

HEALTH POLICY & SERVICES
衛生政策與服務 2區

2020年12月舊的升級版
綜述:
TOP期刊:
大類:醫學 3區
小類:

HEALTH POLICY & SERVICES
衛生政策與服務 2區

中科院SCI分區:是中國科學院文獻情報中心科學計量中心的科學研究成果。期刊分區表自2004年開始發布,延續至今;2019年推出升級版,實現基礎版、升級版并存過渡,2022年只發布升級版,期刊分區表數據每年底發布。 中科院分區為4個區。中科院分區采用刊物前3年影響因子平均值進行分區,即前5%為該類1區,6%~20%為2區、21%~50%為3區,其余的為4區。1區和2區雜志很少,雜志質量相對也高,基本都是本領域的頂級期刊。

JCR分區(2023-2024年最新版)

Health Care Management Science雜志 JCR分區信息

按JIF指標學科分區
學科:HEALTH POLICY & SERVICES
收錄子集:SSCI
分區:Q2
排名:52 / 118
百分位:

56.4%

按JCI指標學科分區
學科:HEALTH POLICY & SERVICES
收錄子集:SSCI
分區:Q1
排名:25 / 119
百分位:

79.41%

JCR分區:JCR分區來自科睿唯安公司,JCR是一個獨特的多學科期刊評價工具,為唯一提供基于引文數據的統計信息的期刊評價資源。每年發布的JCR分區,設置了254個具體學科。JCR分區根據每個學科分類按照期刊當年的影響因子高低將期刊平均分為4個區,分別為Q1、Q2、Q3和Q4,各占25%。JCR分區中期刊的數量是均勻分為四個部分的。

CiteScore 評價數據(2024年最新版)

Health Care Management Science雜志CiteScore 評價數據

  • CiteScore 值:7.2
  • SJR:0.958
  • SNIP:1.293
學科類別 分區 排名 百分位
大類:Health Professions 小類:General Health Professions Q1 3 / 21

88%

大類:Health Professions 小類:Medicine (miscellaneous) Q1 61 / 398

84%

歷年影響因子和期刊自引率

投稿經驗

Health Care Management Science雜志投稿經驗

該雜志是一本國際優秀雜志,在國際上有較高的學術影響力,行業關注度很高,已被國際權威數據庫SCIE、SSCI收錄,該雜志在HEALTH POLICY & SERVICES綜合專業領域專業度認可很高,對稿件內容的創新性和學術性要求很高,作為一本國際優秀雜志,一般投稿過審時間都較長,投稿過審時間平均 ,如果想投稿該刊要做好時間安排。版面費不祥。該雜志近兩年未被列入預警名單,建議您投稿。如您想了解更多投稿政策及投稿方案,請咨詢客服。

免責聲明

若用戶需要出版服務,請聯系出版商:Health Care Manag. Sci.。

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