Zongqi Xia, Elizabeth Secor, Lori B. Chibnik, Riley M. Bove, Suchun Cheng, Tanuja Chitnis, Andrew Cagan, Vivian S. Gainer, Pei J. Chen, Katherine P. Liao, Stanley Y. Shaw, Ashwin N. Ananthakrishnan, Peter Szolovits, Howard L. Weiner, Elizabeth W. Karlson, Shawn N. Murphy, Guergana K. Savova, Tianxi Cai, Susanne E. Churchill, Robert M. Plenge, Isaac S. Kohane, Philip L. De Jager

To optimally leverage the scalability and unique features of the electronic health records (EHR) for research that would ultimately improve patient care, we need to accurately identify patients and extract clinically meaningful measures. Using multiple sclerosis (MS) as a proof of principle, we showcased how to leverage routinely collected EHR data to identify patients with a complex neurological disorder and derive an important surrogate measure of disease severity heretofore only available in research settings.