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Patients Derogate Physicians Who Use a Computer-Assisted Diagnostic Aid

Author

Listed:
  • Hal R. Arkes

    (Department of Psychology, The Ohio State University, Columbus, Ohio, arkes.1@osu.edu)

  • Victoria A. Shaffer

    (Department of Psychology, Wichita State University, Wichita, Kansas)

  • Mitchell A. Medow

    (Division of General Internal Medicine, College of Medicine, The Ohio State University, Columbus, Ohio)

Abstract

Objective . To ascertain whether a physician who uses a computer-assisted diagnostic support system (DSS) would be rated less capable than a physician who does not. Method . Students assumed the role of a patient with a possible ankle fracture (experiment 1) or a possible deep vein thrombosis (experiment 2). They read a scenario that described an interaction with a physician who used no DSS, one who used an unspecified DSS, or one who used a DSS developed at a prestigious medical center. Participants were then asked to rate the interaction on 5 criteria, the most important of which was the diagnostic ability of the physician. In experiment 3, 74 patients in the waiting room of a clinic were randomly assigned to the same 3 types of groups as used in experiment 1. In experiment 4, 131 3rd- and 4th-year medical students read a scenario of a physician-patient interaction and were randomly assigned to 1 of 4 groups: the physician used no DSS, heeded the recommendation of a DSS, defied a recommendation of a DSS by treating in a less aggressive manner, or defied a recommendation of a DSS by treating in a more aggressive manner . Results . The participants always deemed the physician who used no decision aid to have the highest diagnostic ability. Conclusion . Patients may surmise that a physician who uses a DSS is not as capable as a physician who makes the diagnosis with no assistance from a DSS. Key words: decision support techniques; diagnosis computer assisted; patient satisfaction. (Med Decis Making 2007; 27: 189—202)

Suggested Citation

  • Hal R. Arkes & Victoria A. Shaffer & Mitchell A. Medow, 2007. "Patients Derogate Physicians Who Use a Computer-Assisted Diagnostic Aid," Medical Decision Making, , vol. 27(2), pages 189-202, March.
    • Handle: RePEc:sae:medema:v:27:y:2007:i:2:p:189-202
    DOI: 10.1177/0272989X06297391
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    References listed on IDEAS

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    1. Arkes, Hal R. & Dawes, Robyn M. & Christensen, Caryn, 1986. "Factors influencing the use of a decision rule in a probabilistic task," Organizational Behavior and Human Decision Processes, Elsevier, vol. 37(1), pages 93-110, February.
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    Cited by:

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    2. Alexander Erlei & Lukas Meub, 2024. "Reputation-Driven Adoption and Avoidance of Algorithmic Decision Aids in Credence Goods Markets," Papers 2401.17929, arXiv.org, revised Apr 2025.
    3. Palmeira, Mauricio, 2020. "Advice in the presence of external cues: The impact of conflicting judgments on perceptions of expertise," Organizational Behavior and Human Decision Processes, Elsevier, vol. 156(C), pages 82-96.
    4. Hanzhe Li & Jin Li & Ye Luo & Xiaowei Zhang, 2024. "AI Persuasion, Bayesian Attribution, and Career Concerns of Decision-Makers," Papers 2410.01114, arXiv.org, revised Feb 2026.
    5. Tinglong Dai & Sridhar Tayur, 2022. "Designing AI‐augmented healthcare delivery systems for physician buy‐in and patient acceptance," Production and Operations Management, Production and Operations Management Society, vol. 31(12), pages 4443-4451, December.
    6. Tinglong Dai & Shubhranshu Singh, 2020. "Conspicuous by Its Absence: Diagnostic Expert Testing Under Uncertainty," Marketing Science, INFORMS, vol. 39(3), pages 540-563, May.
    7. 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.
    8. Maximilian Germann & Lukas Mertes & Martin Weber & Benjamin Loos, 2025. "Trust and delegated investing: a Money Doctors experiment," Review of Finance, European Finance Association, vol. 29(1), pages 75-102.
    9. 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).
    10. Mahmud, Hasan & Islam, A.K.M. Najmul & Ahmed, Syed Ishtiaque & Smolander, Kari, 2022. "What influences algorithmic decision-making? A systematic literature review on algorithm aversion," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    11. Mahmud, Hasan & Islam, A.K.M. Najmul & Mitra, Ranjan Kumar, 2023. "What drives managers towards algorithm aversion and how to overcome it? Mitigating the impact of innovation resistance through technology readiness," Technological Forecasting and Social Change, Elsevier, vol. 193(C).

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