Cancer risk algorithms in primary care: can they improve risk estimates and referral decisions?

Background: Cancer risk calculators were introduced to clinical practice in the last decade, but they remain underused. We aimed to test their potential to improve risk assessment and 2-week-wait referral decisions. Methods: 157 GPs were presented with 23 vignettes describing patients with possible colorectal cancer symptoms. GPs gave their intuitive risk estimate and inclination to refer. They then saw the risk score of an algorithm (QCancer was not named) and could update their responses. Half of the sample was given information about the algorithm9s derivation, validation, and accuracy. At the end, we measured their algorithm disposition. Results: GPs changed their inclination to refer 26% of the time and switched decisions entirely 3% of the time. Post-algorithm decisions improved significantly vis-a-vis the 3% NICE threshold (OR 1.45 [1.27, 1.65], p<.001). The algorithm9s impact was greater where GPs had underestimated risk. GPs who received information about the algorithm had more positive disposition towards it. A learning effect was observed: GPs9 intuitive risk estimates became better calibrated over time, i.e., moved closer to QCancer. Conclusions: Cancer risk calculators have the potential to improve 2-week-wait referral decisions. Their use as learning tools to improve intuitive risk estimates is promising and should be further investigated.