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信息系统领域的智能健康:文献综述与研究议程。

Intelligent health in the IS area: A literature review and research agenda.

作者信息

Guo Xitong, Li Yan

机构信息

School of Management, Harbin Institute of Technology, Harbin 150006, China.

School of Information, Central University of Finance and Economics, Beijing 100098, China.

出版信息

Fundam Res. 2023 May 11;4(4):961-971. doi: 10.1016/j.fmre.2023.04.008. eCollection 2024 Jul.

DOI:10.1016/j.fmre.2023.04.008
PMID:39156567
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11330141/
Abstract

As the global demand for healthcare services continues to grow, improving the efficiency and effectiveness of the healthcare ecosystem has become a pressing concern. Information systems are transforming the healthcare delivery process, shifting the focus of healthcare services from passive disease treatment to proactive health prevention and the healthcare management model from hospital-centric to patient-centric. This study focuses on reviewing research in IS journals on the topic of e-health and is dedicated to constructing a theoretical model of intelligent health to provide a research basis for future discussions in this field. In addition, as the innovation of intelligent healthcare services has led to changes in its elements (e.g., an increase in the number of stakeholders), there is an urgent need to sort out and analyze the existing research.

摘要

随着全球对医疗服务的需求持续增长,提高医疗生态系统的效率和效果已成为紧迫问题。信息系统正在改变医疗服务提供过程,将医疗服务的重点从被动的疾病治疗转向主动的健康预防,并且将医疗管理模式从以医院为中心转变为以患者为中心。本研究着重回顾信息系统(IS)期刊中关于电子健康主题的研究,并致力于构建智能健康的理论模型,为该领域未来的讨论提供研究基础。此外,由于智能医疗服务的创新导致其要素发生变化(例如,利益相关者数量增加),迫切需要对现有研究进行梳理和分析。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6611/11330141/94c2680dc4e5/gr6.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6611/11330141/94c2680dc4e5/gr6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6611/11330141/3e20f4abf8dd/ga1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6611/11330141/19e36f296a93/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6611/11330141/f24bd957317f/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6611/11330141/4b3b65063a0b/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6611/11330141/72f603767b5a/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6611/11330141/70ab90444f3f/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6611/11330141/94c2680dc4e5/gr6.jpg

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Electron Mark. 2021;31(4):901-921. doi: 10.1007/s12525-021-00484-1. Epub 2021 Jul 17.
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Identifying who has long COVID in the USA: a machine learning approach using N3C data.在美国识别长新冠患者:使用 N3C 数据的机器学习方法。
Lancet Digit Health. 2022 Jul;4(7):e532-e541. doi: 10.1016/S2589-7500(22)00048-6. Epub 2022 May 16.
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Early detection of COVID-19 in the UK using self-reported symptoms: a large-scale, prospective, epidemiological surveillance study.
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Lancet Digit Health. 2021 Sep;3(9):e587-e598. doi: 10.1016/S2589-7500(21)00131-X. Epub 2021 Jul 29.
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A comparison of general and disease-specific machine learning models for the prediction of unplanned hospital readmissions.一般机器学习模型和疾病特异性机器学习模型在预测非计划性住院再入院方面的比较。
J Am Med Inform Assoc. 2021 Mar 18;28(4):868-873. doi: 10.1093/jamia/ocaa299.
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