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Visual assessment of the similarity between a patient and trial population

Journal: Applied Clinical Informatics
ISSN: 1869-0327
DOI: https://doi.org/10.4338/ACI-2015-12-RA-0178
Issue: Vol. 7: Issue 2 2016
Pages: 477-488

Visual assessment of the similarity between a patient and trial population

Is This Clinical Trial Applicable to My Patient?

Editors Choice

Research Article

A. Cahan (1, 2), J. J. Cimino (3, 4)

(1) IBM T.J. Watson Research Center, Yorktown Heights, NY; (2) National Library of Medicine, Bethesda, MD, Informatics Institute; (3) University of Alabama at Birmingham, Birmingham, AL; (4) National Institutes of Health Clinical Center, Bethesda, MD

Keywords

Clinical decision support systems, data representation, patient similarity, visual scale

Summary

Background: A critical consideration when applying the results of a clinical trial to a particular patient is the degree of similarity of the patient to the trial population. However, similarity assessment rarely is practical in the clinical setting. Here, we explore means to support similarity assessment by clinicians. Methods: A scale chart was developed to represent the distribution of reported clinical and demographic characteristics of clinical trial participant populations. Constructed for an individual patient, the scale chart shows the patient’s similarity to the study populations in a graphical manner. A pilot test case was conducted using case vignettes assessed by clinicians. Two pairs of clinical trials were used, each addressing a similar clinical question. Scale charts were manually constructed for each simulated patient. Clinicians were asked to estimate the degree of similarity of each patient to the populations of a pair of trials. Assessors relied on either the scale chart, a summary table (aligning characteristics of 2 trial populations), or original trial reports. Assessment time and between-assessor agreement were compared. Population characteristics considered important by assessors were recorded. Results: Six assessors evaluated 6 cases each. Using a visual scale chart, agreement between physicians was higher and the time required for similarity assessment was comparable Conclusion: We suggest that further research is warranted to explore visual tools facilitating the choice of the most applicable clinical trial to a specific patient. Automating patient and trial population characteristics extraction is key to support this effort.

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