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Description

The largely unstructured data format of current EMRs creates significant barriers for repurposing of this data for use in research. Capture of structured data at the point-of-care would permit seamless and automated extraction of clinical data for secondary uses. Using structured data common to both the UCSF EMR and CRFs of the ongoing I-SPY2 trial, we demonstrate the feasibility of automated extraction and the potential research time and cost savings created by doing so.

Learning Objective 1: Understand the goal of using an eSource approach (automated extraction of data from the electronic health record to populate clinical research study systems), including regulatory and clinical stakeholder needs, and be able to outline characteristics of the EMR and the EDC (electronic data capture) systems that pose the greatest challenges to an eSource approach.

Learning Objective 2: Understand the role that standards play (in particular RFD, the CCD, and FHIR) in this particular solution and in the eSource approach in general.

Authors:

Adam Asare (Presenter)
Quantum Leap Healthcare Collaborative

Jessica Hong, Quantum Leap Healthcare Collaborative
Aheli Chattopadhyay, UCSF
Thomas Bechtold, Quantum Leap Healthcare Collaborative
Michael Ibara, Quantum Leap Healthcare Collaborative
Mitra Rocca, U.S. Food & Drug Association
Alexander Koorkoff, UCSF
Karen Kimura, Quantum Leap Healthcare Collaborative
Laura Esserman, UCSF
Irene Dubman, Quantum Leap Healthcare Collaborative

Presentation Materials:

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