Harvard’s research focuses onuses of personally controlled electronic health records in the prevention, control, and reporting of chronic disease. Klompas et al. describe the utility of an electronic medical record system to augment the Behavioral Risk Factor Surveillance System (BRFSS) and other traditional methods for diabetes surveillance. Weitzman et al. present a novel approach to monitoring preventive and self-care practices for diabetes using an online social network.

Utah’s research activities include implementing a visual analytic and decision support system to enhance community health assessment and public health surveillance and deploying new methods and models from computer science, social and behavioral sciences and other disciplines to represent knowledge and exchange information relevant to public health practice. Staes et al. present an evaluation and analysis of Reportable Condition Mapping Tables relative to nationally defined reporting logic and recommend “using knowledge management tools to author, verify, improve, and authenticate logic, and continually incorporate improved logic that has been validated in clinical systems” (5). Xu et al. describe how their framework for collaboration leads to successful translation of their research into public health practice.

Indiana’s research includes developing Adaptive Turnaround Document systems (computer-interpreted paper forms) to support newborn screening and immunization tracking and enhancing basic infrastructure capabilities to support public health. Downs et al. review technical challenges in exchanging data between clinicians in the Indiana Health information Exchange and the Indiana State Department of Health. Dixon et al. describe a process for developing a framework to continuously analyze public health data to improve data quality.

Pittsburgh’s research focuses ondeveloping Bayesian disease surveillance methods including case detection and outbreak detection and characterization and translating them into technologies for public health practice. Wagner et al. present a probabilistic, decision-theoretic system for disease surveillance and control and use the example of influenza surveillance to describe how the software components transform data collected by the healthcare system into useful data for public health practice.Tsuiet al. demonstrate how a probabilistic case detection system uses emergency department dictated notes and laboratory results to compute the posterior probability of influenza and influenza-like illness.