Determinants of physicians’ referrals for suspected cancer given a risk-prediction algorithm: linking signal detection and fuzzy-trace theory
Author(s)
Kostopoulou, Olga
Palfi, Bence
Arora, Kavleen
Reyna, Valerie
Type
Journal Article
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 < 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. The perceived severity of missing a cancer vis-à-vis overreferring 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 judgments.
Highlights
• Physicians’ inclination to refer patients for suspected cancer is determined by their assessment of cancer risk but also their core values; specifically, their values in relation to the perceived benefits and harms of referrals and the seriousness of missing a cancer compared with overreferring.
• We observed a moral prioritization of referral decision making, in which considerations about benefits to the patient were foremost, considerations about benefits but also harms to the health system or the society were second, while considerations about oneself carried little or no weight.
• Having an algorithm informing assessments of risk influences referral decisions but does not remove or significantly reduce the influence of physicians’ core values.
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 < 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. The perceived severity of missing a cancer vis-à-vis overreferring 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 judgments.
Highlights
• Physicians’ inclination to refer patients for suspected cancer is determined by their assessment of cancer risk but also their core values; specifically, their values in relation to the perceived benefits and harms of referrals and the seriousness of missing a cancer compared with overreferring.
• We observed a moral prioritization of referral decision making, in which considerations about benefits to the patient were foremost, considerations about benefits but also harms to the health system or the society were second, while considerations about oneself carried little or no weight.
• Having an algorithm informing assessments of risk influences referral decisions but does not remove or significantly reduce the influence of physicians’ core values.
Date Issued
2026-01-01
Date Acceptance
2025-07-14
Citation
Medical Decision Making, 2026, 46, pp.88-101
ISSN
0272-989X
Publisher
SAGE Publications
Start Page
88
End Page
101
Journal / Book Title
Medical Decision Making
Volume
46
Copyright Statement
Copyright This paper is embargoed until publication. Once published the Version of Record (VoR) will be available on immediate open access.
License URL
Publication Status
Published
Date Publish Online
2025-10-16