Can low accuracy disease risk predictor models improve health care using decision support systems?

Douglas Benn, D. D. Dankel, S. H. Kostewicz

Research output: Contribution to journalArticle

7 Scopus citations

Abstract

A prototype decision support system has been designed for managing dental caries using a risk assessment model. Caries is a multifactorial disease with risk prediction models having low sensitivity (65%) and moderate specificity (80%) for 2 or more new lesions. These models are inaccurate for targeting resources at high risk people. However, low risk individuals can be more accurately identified. If the activity of early tooth decay lesions, in low risk people, are monitored over time and only lesions beyond 1/3 of the dentin depth are filled, the number of annual fillings may be reduced by 50%. Currently, most US dental schools do not teach risk assessment for caries and encourage early treatment of lesions leading to a repair destruction cycle. The combination of a decision support system with a moderate accuracy specificity risk model for predicting low risk individuals may produce a significant improvement in caries management.

Original languageEnglish
Pages (from-to)577-581
Number of pages5
JournalProceedings / AMIA ... Annual Symposium. AMIA Symposium
Publication statusPublished - 1998
Externally publishedYes

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