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AMIA Jt Summits Transl Sci Proc. 2016 Jul 20;2016:167-75. eCollection 2016.

A Decompositional Approach to Executing Quality Data Model Algorithms on the i2b2 Platform.

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Vanderbilt University, Nashville, TN;
Mayo Clinic, Rochester, MN.
Feinberg School of Medicine, Northwestern University, Chicago, IL.
Weill Cornell Medical College, Cornell University, New York, NY.
NorthShore University HealthSystem, Evanston, IL.


The Quality Data Model (QDM) is an established standard for representing electronic clinical quality measures on electronic health record (EHR) repositories. The Informatics for Integrated Biology and the Bedside (i2b2) is a widely used platform for implementing clinical data repositories. However, translation from QDM to i2b2 is challenging, since QDM allows for complex queries beyond the capability of single i2b2 messages. We have developed an approach to decompose complex QDM algorithms into workflows of single i2b2 messages, and execute them on the KNIME data analytics platform. Each workflow operation module is composed of parameter lists, a template for the i2b2 message, an mechanism to create parameter updates, and a web service call to i2b2. The communication between workflow modules relies on passing keys ofi2b2 result sets. As a demonstration of validity, we describe the implementation and execution of a type 2 diabetes mellitus phenotype algorithm against an i2b2 data repository.


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