Layout 1 ISDS Annual Conference Proceedings 2012. This is an Open Access article distributed under the terms of the Creative Commons Attribution- Noncommercial 3.0 Unported License (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. ISDS 2012 Conference Abstracts Processing of Novel Electronic Health Data to Support Public Health Surveillance Peter Hicks*, Henry Rolka, Mark Wooster and Lynette Brammer CDC, Atlanta, GA, USA Objective To describe data management and analytic processes undertaken to rapidly acquire and use previously unavailable data during a public health emergency response. Introduction Accurately gauging the health status of a population during an event of public health significance (e.g. hurricanes, H1N1 2009 pan- demic) in support of emergency response and situation awareness ef- forts can be a challenge for established public health surveillance systems in terms of geographic and population coverage as well as the appropriateness of health indicators. The demand for timely, accu- rate, and event-specific data can require the rapid development of new data assets to “fill-in” existing information gaps to better char- acterize the scope, scale, magnitude, and population health impact of a given event within a very narrow time-window. Such new data as- sets may be concurrently under development and evaluation while being used to support response efforts. Recent examples include the “drop-in” surveillance processes deployed at evacuation centers fol- lowing Hurricane Katrina1 and the illness and injury surveillance sys- tems established for response workers during the Deepwater Horizon Oil spill response. During the 2009 H1N1 pandemic response, CDC acquired access to data from several national-level health informa- tion systems that previously had been un-vetted as public health in- formation sources. These sources provided data extracts from massive administrative or electronic medical records (EMR) based in hospi- tal and primary care settings. It was hoped that such data could sup- plement existing influenza surveillance systems and aid in the characterization of the pandemic. Few of these new data sources had formal documentation or concise information on the underlying pop- ulations and geographies represented. Methods Throughout CDC’s H1N1 response; epidemiologists, data man- agers, and IT specialists collaborated to develop standardized meth- ods to rapidly characterize, process, store, and provision these new data for analysis and reporting by subject matter experts.These new data were not part of a formally designed sample so each data source needed to undergo extensive empirical review to understand, repre- sentativeness, unique nuances, and facilitate the interpretation of an- alytic results and accurate reporting to public health decision makers. Results Such work requires a multi-disciplinary approach that cyclically reviews incoming data iteratively while concurrently documenting findings, modifying initial business rules (e.g. extraction, binning, or coding logic), and analytic techniques to produce the most inter- pretable and informative results. To elucidate the underlying com- plexity for these sequential and contingent activities occurring across information technology, informatics, and epidemiology domains, we retrospectively described the intersection of the discrete tangible tasks and workforce roles via a TaskFlow diagram (Figure 1). Vertical “swim lanes” represent discrete tasks: On-boarding/Documentation, Analysis/Visualization, and Visualization/Reporting. Workforce roles such as Data management, Epidemiological Analysis, and Commu- nications are broken into three horizontal “swim lanes” as each re- quires dramatically different skillsets and are accomplished by different individuals. Each of the steps (1-9) in the diagram were leveraged to produce supplemental artifacts (e.g. code books, ex- traction guides, defined analytic methods, etc.) to support ongoing analysis, interpretation, reporting, and over process improvement. The totality of all of these interrelated activities have an a priori pur- pose of characterizing population health during an event of public health significance to support disease prevention and control efforts in a timely fashion. Conclusions This presentation describes the underlying business processes, ac- tivities, and roles used in transforming novel data sources, during the H1N1 response, into informative assets to support public health sur- veillance. By formally articulating and describing each of these steps, in a structured manner, we hope to contribute to the dialogue of de- veloping useful practices for leveraging electronic health data to meet public health surveillance challenges. Keywords informatics; surveillance; emergency response; h1n1; data manage- ment *Peter Hicks E-mail: phicks@cdc.gov Online Journal of Public Health Informatics * ISSN 1947-2579 * http://ojphi.org * 5(1):e31, 2013