Facilitating Clinical Data Analytics for Comparative Effectiveness Research

Comparative effectiveness research (CER) is an important type of clinical research that compares the benefits and harms of alternative methods for a particular set of patients, to prevent, diagnose, treat and monitor a clinical condition, or to improve the delivery of care. Compared with the classic Randomized Controlled Trial (RCT), which measures efficacy under ideal, well-controlled clinical conditions, CER evaluates the effectiveness of an intervention in a “real world” environment.

Therefore, CER is critical for providing evidence and support for informed medical care and health policy decision making. With the rapid development of clinical information systems (e.g., EMR), more and more patient/disease data are accumulated in daily clinical databases. Clinical data analytics over such databases for CER has many advantages, such as speeding up the research process, reducing the research cost and providing clinical evidence for real-life decision making.

To facilitate data analytics for CER, we propose a system called OCERA: ‘On-demand Comparative Effectiveness Research Accelerator’. OCERA can assist physicians to efficiently undertake high quality CER by addressing various types of complexities in the clinical research process. In this abstract, we will also report a case study that shows the effectiveness of our system

By: Xingzhi Sun, Linhao Xu, Yiqin Yu, Matthias Reumann

Published in: RC25357 in 2013


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