IDEAS home Printed from https://ideas.repec.org/a/spr/infsem/v19y2021i2d10.1007_s10257-021-00520-9.html
   My bibliography  Save this article

Study of E-business applications based on big data analysis in modern hospital health management

Author

Listed:
  • Xiaohong Li

    (Affiliated Hospital of Southwest Medical University)

  • Yanling Zhang

    (Affiliated Hospital of Southwest Medical University)

  • Yujuan Li

    (Affiliated Hospital of Southwest Medical University)

  • Ke Yu

    (Affiliated Hospital of Southwest Medical University)

  • Yihua Du

    (Affiliated Hospital of Southwest Medical University)

Abstract

In the era of big data, aiming at the problem of health data acquisition and processing in modern hospital information management, this paper analyzes and realizes the auxiliary role of big data and e-business in modern hospital health management. Based on machine learning, this paper analyzes hospital health service systems and proposes a detailed management plan that changes the systems from fragmented hospital health management to comprehensive and omnidirectional hospital health management systems and from disordered market competition to comprehensive regionalization to help provide technical support for strengthening hospital health data management.

Suggested Citation

  • Xiaohong Li & Yanling Zhang & Yujuan Li & Ke Yu & Yihua Du, 2021. "Study of E-business applications based on big data analysis in modern hospital health management," Information Systems and e-Business Management, Springer, vol. 19(2), pages 621-640, June.
    • Handle: RePEc:spr:infsem:v:19:y:2021:i:2:d:10.1007_s10257-021-00520-9
    DOI: 10.1007/s10257-021-00520-9
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10257-021-00520-9
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10257-021-00520-9?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Rajesh Chidananda Reddy & Biplab Bhattacharjee & Debasisha Mishra & Anandadeep Mandal, 2022. "A systematic literature review towards a conceptual framework for enablers and barriers of an enterprise data science strategy," Information Systems and e-Business Management, Springer, vol. 20(1), pages 223-255, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:infsem:v:19:y:2021:i:2:d:10.1007_s10257-021-00520-9. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.