Medicine in words and numbers: A cross-sectional survey comparing probability assessment scales

C.L.M. Witteman, S. Renooij, P. Koele

Research output: Contribution to conferenceAbstractOther research output

Abstract

Background
In the complex domain of medical decision making, reasoning under uncertainty can benefit from supporting tools. Automated tools such as decision support systems often build upon mathematical models, such as Bayesian networks. These networks require probabilities which often have to be assessed by experts in the domain of application. Probability response scales can be used to support the assessment process. We compare assessments obtained with different types of response scale.
Methods
General practitioners (GPs) gave assessments on and preferences for three different probability response scales: a numerical scale, a scale with only verbal labels, and a combined verbal-numerical scale we had designed ourselves. Standard analyses of variance were performed.
Results
No differences in assessments over the three response scales were found. Preferences for type of scale differed: the young and less experienced GPs preferred the verbal scale, the senior and most experienced preferred the numerical scale, with the groups in between having a preference for the combined verbal-numerical scale.
Conclusions
We conclude that any of the three response scales is equally suitable for supporting probability assessment. We advise that the combined verbal-numerical scale is a good choice for aiding the process, since it offers numerical labels for those who prefer numbers, while also offering verbal labels to those who prefer words.

Conference

ConferenceSPUDM (Subjective Probability, Utility and Decision Making) 2007 Symposium on Assessing clinical thinking and decision processes: Overview and comparative assessment across disciplines
CityWarsaw
Period1/01/07 → …

Fingerprint

Dive into the research topics of 'Medicine in words and numbers: A cross-sectional survey comparing probability assessment scales'. Together they form a unique fingerprint.

Cite this