How well do health professionals interpret diagnostic information? A systematic review
Research output: Contribution to journal › Article › peer-review
Colleges, School and Institutes
- School of Social and Community Medicine, University of Bristol, Bristol, UK The National Institute for Health Research Collaboration for Leadership in Applied Health Research and Care West at University Hospitals Bristol NHS Foundation Trust.
OBJECTIVE: To evaluate whether clinicians differ in how they evaluate and interpret diagnostic test information.
DESIGN: Systematic review.
DATA SOURCES: MEDLINE, EMBASE and PsycINFO from inception to September 2013; bibliographies of retrieved studies, experts and citation search of key included studies.
ELIGIBILITY CRITERIA FOR SELECTING STUDIES: Primary studies that provided information on the accuracy of any diagnostic test (eg, sensitivity, specificity, likelihood ratios) to health professionals and that reported outcomes relating to their understanding of information on or implications of test accuracy.
RESULTS: We included 24 studies. 6 assessed ability to define accuracy metrics: health professionals were less likely to identify the correct definition of likelihood ratios than of sensitivity and specificity. -25 studies assessed Bayesian reasoning. Most assessed the influence of a positive test result on the probability of disease: they generally found health professionals' estimation of post-test probability to be poor, with a tendency to overestimation. 3 studies found that approaches based on likelihood ratios resulted in more accurate estimates of post-test probability than approaches based on estimates of sensitivity and specificity alone, while 3 found less accurate estimates. 5 studies found that presenting natural frequencies rather than probabilities improved post-test probability estimation and speed of calculations.
CONCLUSIONS: Commonly used measures of test accuracy are poorly understood by health professionals. Reporting test accuracy using natural frequencies and visual aids may facilitate improved understanding and better estimation of the post-test probability of disease.
|Publication status||Published - 28 Jul 2015|
- Bayes Theorem, Clinical Competence, Data Interpretation, Statistical, Diagnostic Techniques and Procedures, Health Personnel, Humans, Likelihood Functions, Sensitivity and Specificity, Journal Article, Research Support, Non-U.S. Gov't, Review