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. 2021 Nov 15;12:6585. doi: 10.1038/s41467-021-26905-5

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© The Author(s) 2021

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 2 — Panel a shows clinicians’ propensity to revise their diagnostic assessments in the network conditions according to the initial error in their diagnostic assessments. Clinicians’ accuracy is represented as the absolute number of percentage points of a given assessment from the most accurate assessment of 16% (represented by 0 along the x-axis, indicating a distance of 0 percentage points from the most accurate response). Magnitude of revision is measured as the absolute difference (percentage points) between a clinician’s initial diagnostic assessment and their final diagnostic assessment. Clinicians’ accuracy in their initial assessment significantly predicts the magnitude of their revisions between the initial to final response. Grey error band displays 95% confidence intervals for the fit of an OLS model regressing initial error of diagnostic assessment on magnitude of revision. Panel b shows the significant positive relationship between the improvement in clinicians’ diagnostic accuracy (from the initial to final assessment), and their likelihood of improving in their treatment recommendation (i.e. the probability of switching from recommending Option A, B, or D to Option C) for clinicians in the network conditions. The trend line shows the estimated probability of clinicians improving their treatment recommendations according to a logistic regression, controlling for an interaction between experimental condition (control or network) and patient-actor demographic (Black female or white male) (Supplementary Table 9). Error bars show standard errors clustered at the trial level.