Automated collection of pathogen-specific diagnostic data for real-time syndromic epidemiological studies

Lindsay Meyers; Christine C. Ginocchio; Aimie N. Faucett; Frederick S. Nolte; Per H. Gesteland; Amy Leber; Diane Janowiak; Virginia Donovan; Jennifer Dien Bard; Silvia Spitzer; Kathleen A. Stellrecht; Hossein Salimnia; Rangaraj Selvarangan; Stefan Juretschko; Judy A. Daly; Jeremy C. Wallentine; Kristy Lindsey; Franklin Moore; Sharon L. Reed; Maria Aguero-Rosenfeld; Paul D. Fey; Gregory A. Storch; Steve J. Melnick; Christine C. Robinson; Jennifer F. Meredith; Camille V. Cook; Robert K. Nelson; Jay D. Jones; Samuel V. Scarpino; Benjamin M. Althouse; Kirk M. Ririe; Bradley A. Malin; Mark A. Poritz

The copyright holder for this preprint (which was not peer-reviewed) is the author/funder. year of the test. The initial dataset provided low-resolution information but was a useful platform to evaluate the proposed system. Further development to enable export of higher resolution data required the design of routines that would adhere to an alternative HIPAA de-identification 155 strategy, namely, the Expert Determination approach, which requires a risk assessment demonstrating that the chance of re-identifying an individual is sufficiently small [60, 61] . The Expert Determination process identified and made recommendations for fields that could facilitate disclosure of PHI. A summary of the Expert Determination results detailing the risk of Trend data in regard to replicability, availability and distinguishability is shown in Methods, 160 Table 2 .