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Visual representation of statistical information improves diagnostic inferences in doctors and their patients

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  • Garcia-Retamero, Rocio
  • Hoffrage, Ulrich

Abstract

Doctors and patients have difficulty inferring the predictive value of a medical test from information about the prevalence of a disease and the sensitivity and false-positive rate of the test. Previous research has established that communicating such information in a format the human mind is adapted to—namely natural frequencies—as compared to probabilities, boosts accuracy of diagnostic inferences. In a study, we investigated to what extent these inferences can be improved—beyond the effect of natural frequencies—by providing visual aids. Participants were 81 doctors and 81 patients who made diagnostic inferences about three medical tests on the basis of information about prevalence of a disease, and the sensitivity and false-positive rate of the tests. Half of the participants received the information in natural frequencies, while the other half received the information in probabilities. Half of the participants only received numerical information, while the other half additionally received a visual aid representing the numerical information. In addition, participants completed a numeracy scale. Our study showed three important findings: (1) doctors and patients made more accurate inferences when information was communicated in natural frequencies as compared to probabilities; (2) visual aids boosted accuracy even when the information was provided in natural frequencies; and (3) doctors were more accurate in their diagnostic inferences than patients, though differences in accuracy disappeared when differences in numerical skills were controlled for. Our findings have important implications for medical practice as they suggest suitable ways to communicate quantitative medical data.

Suggested Citation

  • Garcia-Retamero, Rocio & Hoffrage, Ulrich, 2013. "Visual representation of statistical information improves diagnostic inferences in doctors and their patients," Social Science & Medicine, Elsevier, vol. 83(C), pages 27-33.
    • Handle: RePEc:eee:socmed:v:83:y:2013:i:c:p:27-33
    DOI: 10.1016/j.socscimed.2013.01.034
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    6. Michelle McDowell & Mirta Galesic & Gerd Gigerenzer, 2018. "Natural Frequencies Do Foster Public Understanding of Medical Tests: Comment on Pighin, Gonzalez, Savadori, and Girotto (2016)," Medical Decision Making, , vol. 38(3), pages 390-399, April.
    7. Zachary Breig & Paul Feldman, 2024. "Revealing risky mistakes through revisions," Journal of Risk and Uncertainty, Springer, vol. 68(3), pages 227-254, June.
    8. Aysegul Kanay & Denis Hilton & Laetitia Charalambides & Jean-Baptiste Corrégé & Eva Inaudi & Laurent Waroquier & Stéphane Cézéra, 2021. "Making the carbon basket count: Goal setting promotes sustainable consumption in a simulated online supermarket," Post-Print hal-03403040, HAL.
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