Imperial College London
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DrOlgaKostopoulou

Faculty of Medicine, Department of Surgery & Cancer

Reader in Medical Decision Making
 
 
 
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Contact

 

o.kostopoulou Website

 
 
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Location

 

5.07Medical SchoolSt Mary's Campus

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Summary

 

Publications

Citation

BibTex format

@article{Palfi:2022:10.1186/s41235-022-00421-6,
author = {Palfi, B and Arora, K and Kostopoulou, O},
doi = {10.1186/s41235-022-00421-6},
journal = {Cognitive Research: Principles and Implications},
title = {Algorithm-based advice taking and clinical judgement: impact of advice distance and algorithm information},
url = {http://dx.doi.org/10.1186/s41235-022-00421-6},
volume = {7},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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 their risk estimates after receiving algorithmic advice. Half of them received information about the algorithm’s derivation, validation, and accuracy. We measured Weight of Advice and found that, on average, GPs weighed their estimates and the algorithm equally – but not always: they retained their initial estimates 29% of the time, and fully updated them 27% of the time. Updating did not depend on whether GPs were informed about the algorithm. We found a weak negative quadratic relationship between estimate updating and advice distance: although GPs integrate algorithmic advice on average, they may somewhat discount it, if it is very different from their own estimate. These results present a more complex picture than simple egocentric discounting of advice. They cast a more optimistic view of advice taking, where experts weigh algorithmic advice and their own judgement equally and move towards the advice even when it contradicts their own initial estimates.
AU  - Palfi,B
AU  - Arora,K
AU  - Kostopoulou,O
DO  - 10.1186/s41235-022-00421-6
PY  - 2022///
SN  - 2365-7464
TI  - Algorithm-based advice taking and clinical judgement: impact of advice distance and algorithm information
T2  - Cognitive Research: Principles and Implications
UR  - http://dx.doi.org/10.1186/s41235-022-00421-6
UR  - https://rdcu.be/cTkmR
UR  - http://hdl.handle.net/10044/1/98379
VL  - 7
ER  -
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