The Value of Applying Big Data Analytics in Health Supply Chain Management.

Q2 Pharmacology, Toxicology and Pharmaceutics
F1000Research Pub Date : 2025-02-19 eCollection Date: 2024-01-01 DOI:10.12688/f1000research.156525.4
Dina Al Nuaimi, Niyi Awofeso
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引用次数: 0

Abstract

This study aims to evaluate the impact of big data analytics (BDA) on the performance of healthcare supply chain management (HCSCMP) by examining both overall efficiency improvements and identifying critical success factors for effective implementation. Through a systematic literature review, the research investigates how BDA enhances real-time decision-making within healthcare supply chains (HCSCs) and identifies the key enablers required for successful BDA adoption. A comprehensive search strategy was employed to analyze 65 papers, resulting in the inclusion of 39 studies published between 2016 and 2023. The review revealed a preference for literature reviews and questionnaires as the primary research methods. The findings indicate that BDA significantly improves HCSCs' efficiency, particularly in real-time decision-making and operational management. However, successful BDA implementation depends on addressing critical enablers and overcoming associated challenges.

大数据分析在医疗供应链管理中的应用价值
本研究旨在通过检查整体效率改进和确定有效实施的关键成功因素,评估大数据分析(BDA)对医疗保健供应链管理(HCSCMP)绩效的影响。通过系统的文献回顾,本研究调查了BDA如何增强医疗保健供应链(HCSCs)中的实时决策,并确定了成功采用BDA所需的关键促成因素。采用综合检索策略对65篇论文进行分析,结果纳入了2016年至2023年间发表的39项研究。回顾显示首选文献综述和问卷调查作为主要的研究方法。研究结果表明,BDA显著提高了HCSCs的效率,特别是在实时决策和运营管理方面。然而,成功的BDA实现取决于解决关键的促成因素和克服相关的挑战。
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
F1000Research
F1000Research Pharmacology, Toxicology and Pharmaceutics-Pharmacology, Toxicology and Pharmaceutics (all)
CiteScore
5.00
自引率
0.00%
发文量
1646
审稿时长
1 weeks
期刊介绍: F1000Research publishes articles and other research outputs reporting basic scientific, scholarly, translational and clinical research across the physical and life sciences, engineering, medicine, social sciences and humanities. F1000Research is a scholarly publication platform set up for the scientific, scholarly and medical research community; each article has at least one author who is a qualified researcher, scholar or clinician actively working in their speciality and who has made a key contribution to the article. Articles must be original (not duplications). All research is suitable irrespective of the perceived level of interest or novelty; we welcome confirmatory and negative results, as well as null studies. F1000Research publishes different type of research, including clinical trials, systematic reviews, software tools, method articles, and many others. Reviews and Opinion articles providing a balanced and comprehensive overview of the latest discoveries in a particular field, or presenting a personal perspective on recent developments, are also welcome. See the full list of article types we accept for more information.
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