MONDAY, April 15, 2019 — Standardizing demographic data can improve the accuracy of patient matching, according to a study published in the May issue of the Journal of the American Medical Informatics Association.
Shaun J. Grannis, Ph.D., from the Center for Biomedical Informatics in Indianapolis, and colleagues used four manually reviewed datasets to examine the degree to which recommendations for demographic data standardization improve patient matching accuracy. Sensitivity, specificity, positive predictive value, and accuracy were used to evaluate matching performance.
The researchers observed an independent association between address standardization and improved matching sensitivities for both the public health and health information exchange (HIE) datasets of about 0.6 and 4.5 percent, respectively. For both datasets, overall accuracy was unchanged due to reduced match specificity. No similar impact was seen for address standardization in the death master file dataset. Matching sensitivity was improved by standardizing last name for the HIE dataset, while overall accuracy remained the same due to a reduction in match specificity. No similar impact was noted for other datasets. The combined effect of address and last name standardization improved sensitivity from 81.3 to 91.6 percent for the HIE dataset.
“Standardizing certain demographic data on a broader scale can improve match rates, ensuring that patients and clinicians have better data on which to make decisions to enhance care quality and safety,” the authors write.
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Posted: April 2019