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Abstract: Recent advances in data collection during routine health care in the form of Electronic Health Records (EHR), medical device data (e.g., infusion pump informatics, physiological monitoring data, and insurance claims data, among others, as well as biological and experimental data, have created tremendous opportunities for biological discoveries for clinical application. However, even with all the advancement in technologies and their promises for discoveries, very few research findings have been translated to clinical knowledge, or more importantly, to clinical practice. In this paper, we identify and present the initial work addressing the relevant challenges in three broad categories: data, accessibility, and translation. These issues are discussed in the context of a widely used detailed database from an intensive care unit, Medical Information Mart for Intensive Care (MIMIC III) database.

Learning Objective 1: 1. Identify the challenges in doing clinical research with current big data systems of observational data.

Learning Objective 2: 2. Develop understanding for possible paths to overcome barriers for translational clinical research with big data.

Authors:

Mohammad Adibuzzaman (Presenter)
Purdue University

Poching DeLaurentis, Purdue University
Jennifer Hill, Purdue University
Brian Benneyworth, Indiana University School of Medicine

Presentation Materials:

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