Mitigating cognitive bias with clinical decision support systems: an experimental study

ABSTRACT

Bias in clinical reasoning has been identified as one of the main sources of diagnostic errors. Clinical Decision Support Systems that suggest possible diagnoses and provide information to mitigate cognitive bias could support physicians in finding a less biased diagnosis. We examine the influence of confidence and experience on the probability to adjust the decision after receiving decision aid and whether forming a first opinion beforehand or immediately receiving decision support makes a difference. 103 physicians and medical students participated in an online experiment built on decision tasks formulated to trigger availability and representativeness bias. The analysis showed that the presentation of prevalence data to mitigate availability bias changed the final probability estimate of the diagnosis significantly. Prototypical data to counteract representativeness bias showed no significant change. Medical experience, confidence in the decision, and timing of support had no significant influence on the probability to change the estimate.

KEYWORDS:

• Cognitive bias
• clinical decision support system
• availability bias
• representativeness bias
• mitigating bias
• diagnostic error

Acknowledgments

This work was supported by a PhD grant from the DFG Research Training Group 2535 Knowledge- and data-based personalization of medicine at the point of care (WisPerMed), University of Duisburg-Essen, Germany.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data of this study can be accessed via the following OSF-Link: https://osf.io/nyrtm/?view_only=8d6155573f404f168e7958a9e5948216.

Additional information

Funding

The work was supported by the German Research Foundation (DFG) [RTG 2535 WisPerMed].

Notes on contributors

Alisa Küper

Alisa Küper is a research assistant at the “Chair of Social Psychology: Media and Communication” of the “Department of Computer Science and Applied Cognitive Science” at the University of Duisburg-Essen. There, she completed her bachelor’s and master’s studies in applied cognitive and media science from 2013 to 2019, in the master’s with a specialization in psychology. As a PhD student since March 2021 her general research interests are in human-computer interaction, technology acceptance, and “Explainable AI”.