Determinants of physicians’ referrals for suspected cancer given a risk-prediction algorithm: Linking signal detection and fuzzy-trace theory

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Kostopoulou, Olga; Palfi, Bence; Arora, Kavleen and Reyna, Valerie. 2025. Determinants of physicians’ referrals for suspected cancer given a risk-prediction algorithm: Linking signal detection and fuzzy-trace theory. Medical Decision Making, ISSN 0272-989X [Article] (In Press)

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Abstract or Description

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 vignette, GPs indicated 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 regards 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<0.001). The strongest predictor of referral was the value GPs attached to patient benefits (b=0.30 [0.23, 0.36] p<0.001), followed by benefits (b=0.18 [0.11, 0.24] p<0.001) and harms (b=-0.14 [-0.21, -0.08] p<0.001) to the health system/society. Perceived severity of missing a cancer vis-à-vis over-referring also predicted referral (b=0.004 [0.001, 0.007] p=0.009). The algorithm did not significantly reduce the impact of these variables on referral decisions.

Conclusions. The decision to refer patients who might have cancer can be influenced by how physicians perceive and value the potential benefits and harms of referral primarily for patients, and the moral seriousness of missing a cancer vis-à-vis over-referring. These values contribute to an internal threshold for action and are important even when an algorithm informs risk judgements.

Item Type:

Article

Additional Information:

Funding: The study was funded by a Cancer Research UK grant awarded to Olga Kostopoulou. Funding Scheme: Population Research Committee - Project Award, Reference A28634. Valerie Reyna’s contribution was supported by the National Institute of Standards and Technology (Grant 60NANB22D052) and the Institute for Trustworthy AI in Law and Society (supported by both National Science Foundation and National Institute of Standards and Technology Grant IIS-2229885).

Data Access Statement:

The data are publicly available at https://osf.io/nydh2/.

Departments, Centres and Research Units:

Psychology

Dates: