IDEAS home Printed from https://ideas.repec.org/a/ibf/gjbres/v15y2021i1p77-88.html
   My bibliography  Save this article

Factors Affecting The Adoption Of Artificial Intelligence In Healthcare

Author

Listed:
  • Magda Hercheui
  • Gianluca Mech

Abstract

This paper investigates how clinicians perceive the usefulness and the ease of use of Artificial Intelligence (AI) in healthcare. The paper aims to understand whether AI solutions are perceived to have a positive impact on patient care and the clinician’ work, and which factors affects the adoption of AI in healthcare. The paper draws upon key concepts of TAM (Technology Acceptance Model), adopting an exploratory approach. Semi-structured interviews with 22 clinicians from the NHS (the National Health System, in the United Kingdom) reveal that they perceive the usefulness of AI for healthcare (better efficiency, healthcare quality, and diagnostic accuracy). However, respondents point out factors which affect the way they perceive the ease of use of AI, such as the difficulty to integrate the technology within healthcare systems (low compatibility) and to understand the technology (high complexity), concerns with ethical issues, and the need to have intensive training on digital skills.

Suggested Citation

  • Magda Hercheui & Gianluca Mech, 2021. "Factors Affecting The Adoption Of Artificial Intelligence In Healthcare," Global Journal of Business Research, The Institute for Business and Finance Research, vol. 15(1), pages 77-88.
    • Handle: RePEc:ibf:gjbres:v:15:y:2021:i:1:p:77-88
    as

    Download full text from publisher

    File URL: http://www.theibfr2.com/RePEc/ibf/gjbres/gjbr-v15n1-2021/GJBR-V15N1-2021-4.pdf
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    Artificial Intelligence; Healthcare Systems; UK NHS; Technology Acceptance Model;
    All these keywords.

    JEL classification:

    • M10 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - General

    Statistics

    Access and download statistics

    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:ibf:gjbres:v:15:y:2021:i:1:p:77-88. 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: Mercedes Jalbert (email available below). General contact details of provider: .

    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.