Discussion in relation to other findings
anomaly
Our finding that clinicians integrate advice from a prognostic algorithm more than advice from other clinicians is in line with evidence suggesting that prognostic algorithms can be helpful in clinical practice. Evidence has shown that algorithms are sometimes used as confirmatory tools, validating clinicians' predictions, correcting prognostic impressions, or overcoming tendencies to ignore or overestimate prognoses.It is interesting to note that clinicians integrated advice perceived to be coming from an algorithm more than advice perceived to be coming from a clinical colleague, even though they were informed that such advice was as accurate as a doctor's or a nurse's prediction. As noted previously, prognostic algorithms have not consistently shown superiority to clinical prediction of survival;In our study, we found that participant experience did not impact how clinicians integrated advice. Despite the fact that doctors and nurses might be more involved and experienced in making survival predictions, there is conflicting evidence regarding whether experience improves prognostic accuracy, and there is no clear evidence about whether some types of HCP are better prognosticators than others.The only factor that had a significant impact on participants' advice integration was the strength of the advice. When participants received the advice that a patient had a 75% probability of surviving 2 weeks, they were less likely to integrate the advice into their estimates compared to advice given with lower or higher strength. The reasons for this finding are unclear and may have been a statistical anomaly. Future research should aim at exploring this and provide plausible explanations, for example if there are potential biases towards certain (even) estimates or whether participants' averaging strategies could explain this.Our results do not shed light on the optimal strategy for combining initial prognostic judgements with advice. Our study did not allow for study participants to share their thoughts or reasoning behind their decisions. Future studies should aim at exploring this in greater detail. There is a need for more research on understanding human‐algorithm interaction