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Evidence

Predictive Analytics: A Case Study in Machine-Learning and Claims Databases

Date: 07/16/2019

Journal: The American Journal of Pharmacy Benefits

Date published: November/December 2016

Authors: David A. Kvancz, MS, RPh, FASHP; Marcus N. Sredzinski, PharmD; and Celynda G. Tadlock, PharmD, MBA

Objectives:

This study focused the power of modern analytics on hereditary angioedema (HAE), a single rare disease, because it exhibits features of diseases associated with high costs: rare, hard to diagnose, progressive, and takes a long time from diagnosis to appropriate treatment. Despite the availability of effective therapies, misdiagnoses and underdiagnosis of HAE result in significant burden to the healthcare system.

Conclusions:

This study successfully demonstrated the ability of this state-of-the-art predictive analysis to find rare-disease patients in a large and complex database. This information could be valuable to claims managers and employers who may realize savings by helping physicians bring these patients to appropriate treatment sooner.

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