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Determinants of Physicians’ Referrals for Suspected Cancer Given a Risk-Prediction Algorithm: Linking Signal Detection and Fuzzy Trace Theory

Author

Listed:
  • Olga Kostopoulou

    (Imperial College London, UK)

  • Bence Pálfi

    (Goldsmiths University of London, UK)

  • Kavleen Arora

    (Imperial College London, UK)

  • Valerie Reyna

    (Cornell University, Cornell, NY, USA)

Abstract

Background Previous research suggests that physicians’ inclination to refer patients for suspected cancer is a relatively stable characteristic of their decision making. We aimed to identify its psychological determinants in the presence of a risk-prediction algorithm. Methods We presented 200 UK general practitioners with online vignettes describing patients with possible colorectal cancer. Per the vignette, GPs indicated the likelihood of referral (from highly unlikely to highly likely) and level of cancer risk (negligible/low/medium/high), received an algorithmic risk estimate, and could then revise their responses. After completing the vignettes, GPs responded to questions about their values with regard to harms and benefits of cancer referral for different stakeholders, perceived severity of errors, acceptance of false alarms, and attitudes to uncertainty. We tested whether these values and attitudes predicted their earlier referral decisions. Results The algorithm significantly reduced both referral likelihood ( b = −0.06 [−0.10, −0.007], P = 0.025) and risk level ( b = −0.14 [−0.17, −0.11], P

Suggested Citation

  • Olga Kostopoulou & Bence Pálfi & Kavleen Arora & Valerie Reyna, 2026. "Determinants of Physicians’ Referrals for Suspected Cancer Given a Risk-Prediction Algorithm: Linking Signal Detection and Fuzzy Trace Theory," Medical Decision Making, , vol. 46(1), pages 88-101, January.
    • Handle: RePEc:sae:medema:v:46:y:2026:i:1:p:88-101
    DOI: 10.1177/0272989X251376024
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    References listed on IDEAS

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