IDEAS home Printed from https://ideas.repec.org/a/sae/medema/v33y2013i1p108-118.html
   My bibliography  Save this article

Why Do Patients Derogate Physicians Who Use a Computer-Based Diagnostic Support System?

Author

Listed:
  • Victoria A. Shaffer
  • C. Adam Probst
  • Edgar C. Merkle
  • Hal R. Arkes
  • Mitchell A. Medow

Abstract

Objective . To better understand 1) why patients have a negative perception of the use of computerized clinical decision support systems (CDSSs) and 2) what contributes to the documented heterogeneity in the evaluations of physicians who use a CDSS. Methods . Three vignette-based studies examined whether negative perceptions stemmed directly from the use of a computerized decision aid or the need to seek external advice more broadly (experiment 1) and investigated the contributing role of 2 individual difference measures, attitudes toward statistics (ATS; experiment 2) and the Multidimensional Health Locus of Control Scale (MHLC; experiment 3), to these findings. Results . A physician described as making an unaided diagnosis was rated significantly more positively on a number of attributes than a physician using a computerized decision aid but not a physician who sought the advice of an expert colleague (experiment 1). ATS were unrelated to perceptions of decision aid use (experiment 2); however, greater internal locus of control was associated with more positive feelings about unaided care and more negative feelings about care when a decision aid was used (experiment 3). Conclusion . Negative perceptions of computerized decision aid use may not be a product of the need to seek external advice more generally but may instead be specific to the use of a nonhuman tool and may be associated with individual differences in locus of control. Together, these 3 studies may be used to guide education efforts for patients.

Suggested Citation

  • Victoria A. Shaffer & C. Adam Probst & Edgar C. Merkle & Hal R. Arkes & Mitchell A. Medow, 2013. "Why Do Patients Derogate Physicians Who Use a Computer-Based Diagnostic Support System?," Medical Decision Making, , vol. 33(1), pages 108-118, January.
  • Handle: RePEc:sae:medema:v:33:y:2013:i:1:p:108-118
    DOI: 10.1177/0272989X12453501
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/0272989X12453501
    Download Restriction: no

    File URL: https://libkey.io/10.1177/0272989X12453501?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
    ---><---

    Citations

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


    Cited by:

    1. Gregory Weitzner, 2024. "Reputational Algorithm Aversion," Papers 2402.15418, arXiv.org.
    2. Chugunova, Marina & Sele, Daniela, 2022. "We and It: An interdisciplinary review of the experimental evidence on how humans interact with machines," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 99(C).
    3. Chiara Longoni & Andrea Bonezzi & Carey K Morewedge, 2019. "Resistance to Medical Artificial Intelligence," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 46(4), pages 629-650.
    4. Bauer, Kevin & Nofer, Michael & Abdel-Karim, Benjamin M. & Hinz, Oliver, 2022. "The effects of discontinuing machine learning decision support," SAFE Working Paper Series 370, Leibniz Institute for Financial Research SAFE.
    5. Ekaterina Jussupow & Kai Spohrer & Armin Heinzl, 2022. "Radiologists’ Usage of Diagnostic AI Systems," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 64(3), pages 293-309, June.
    6. Huang, Xiaozhi & Wu, Xitong & Cao, Xin & Wu, Jifei, 2023. "The effect of medical artificial intelligence innovation locus on consumer adoption of new products," Technological Forecasting and Social Change, Elsevier, vol. 197(C).
    7. Sanne Lubberding & Cornelia F van Uden‐Kraan & Elisabeth A Te Velde & Pim Cuijpers & C René Leemans & Irma M Verdonck‐de Leeuw, 2015. "Improving access to supportive cancer care through an eHealth application: a qualitative needs assessment among cancer survivors," Journal of Clinical Nursing, John Wiley & Sons, vol. 24(9-10), pages 1367-1379, May.
    8. Rebitschek, Felix G. & Gigerenzer, Gerd & Wagner, Gert G., 2021. "People underestimate the errors made by algorithms for credit scoring and recidivism prediction but accept even fewer errors," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 11, pages 1-11.

    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:sae:medema:v:33:y:2013:i:1:p:108-118. 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: SAGE Publications (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.