Creating Patient Centric Information Commons for Autism Research

SIG Chairs: Isaac Kohane and Megan O’Boyle Presenters: Geraldine Bliss, Megan O’Boyle, Paul Avillach and Isaac Kohane. Phelan McDermid Foundation http://22q13.org Center of Biomedical Informatics at Harvard Medical School. https://cbmi.med.harvard.edu Analysis of large-scale systems of biomedical data provides a perspective on neuropsychiatric disease that may be otherwise elusive. An analysis of large-scale systems of data from autism spectrum disorder (ASD) and of ASD research as an exemplar of what might be achieved from study of such data. Those with experience in creating large data sets from multiple, heterogeneous sources with wide heterogeneity in both the ‘big’ness of the data in bandwidth/storage as well as complexity will recognize that finding the right data and matching it up correctly to the right individual across these sources is so fraught with difficulty that it has contributed to the substantive shortfall if not outright failure of both large scale national population-scale efforts and local single institution/single population studies. As a result there is no widely available implementation of a framework to support a clinical information commons. We have had some success at the national and international multi-institutional level. Without getting this right, the core of the Precision Medicine agenda—linking data to specific patients/subjects—is at risk. As an example, the emerging use of electronic health record systems and other large clinical databases that allow the data acquired during the course of care to be used to identify distinct subpopulations, clinical trajectories, and pathophysiological substructures of ASD.