Algorithm-based advice taking and clinical judgement: impact of advice distance and algorithm information.

ABSTRACT: Evidence-based algorithms can improve both lay and professional judgements and decisions, yet they remain underutilised. Research on advice taking established that humans tend to discount advice-especially when it contradicts their own judgement ("egocentric advice discounting")-but this can be mitigated by knowledge about the advisor's past performance. Advice discounting has typically been investigated using tasks with outcomes of low importance (e.g. general knowledge questions) and students as participants. Using the judge-advisor framework, we tested whether the principles of advice discounting apply in the clinical domain. We used realistic patient scenarios, algorithmic advice from a validated cancer risk calculator, and general practitioners (GPs) as participants. GPs could update

SUBMITTER: Palfi B 

PROVIDER: S-EPMC9329504 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature