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Description

Cancer patients are predisposed to chronic, co-morbid conditions, yet little is known about real-world treatment patterns. Using the Observational Health Data Sciences and Informatics (OHDSI) network, we identified and described treatment pathways for patients with cancer who initiate treatment for depression (n=1,145,510), hypertension (n=3,178,944), and type II diabetes (n=886,766) from 8 databases of over 295 million patients across 3 countries. We found wide variations in pathways similar to those in non-cancer patients.

Learning Objective 1: Understand the variations in and characteristics of real-world treatment pathways for depression, hypertension, and type II diabetes in patients with cancer

Learning Objective 2: Recognize the feasibility and utility of using a large-scale observational data network with a common data model to perform cancer research

Authors:

RuiJun Chen (Presenter)
Columbia University

Patrick Ryan, Columbia University
Karthik Natarajan, Columbia University
Thomas Falconer, Columbia University
Christian Reich, Observational Health Data Sciences and Informatics (OHDSI)
Rohit Vashisht, Observational Health Data Sciences and Informatics (OHDSI)
Nigam Shah, Observational Health Data Sciences and Informatics (OHDSI)
George Hripcsak, Columbia University

Presentation Materials:

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