Cross-Disciplinary Consultancy to Bridge Public Health Technical Needs and Analytic Developers: Asyndromic Surveillance Use Case

Zachary Faigen, Lana Deynaka, Amy Ising, Daniel Neill, Mike Conway, Geoffrey Fairchild, Julia Gunn, David Swenson, Ian Painter, Lauren Johnson, Chris Kiley, Laura Streichert, Howard Burkom

Abstract


Introduction:  We document a funded effort to bridge the gap between constrained scientific challenges of public health surveillance and methodologies from academia and industry. Component tasks are the collection of epidemiologists’ use case problems, multidisciplinary consultancies to refine them, and dissemination of problem requirements and shareable datasets. We describe an initial use case and consultancy as a concrete example and challenge to developers. 

Materials and Methods: Supported by the Defense Threat Reduction Agency Biosurveillance Ecosystem project, the International Society for Disease Surveillance formed an advisory group to select tractable use case problems and convene inter-disciplinary consultancies to translate analytic needs into well-defined problems and to promote development of applicable solution methods.  The initial consultancy’s focus was a problem originated by the North Carolina Department of Health and its NC DETECT surveillance system: Derive a method for detection of patient record clusters worthy of follow-up based on free-text chief complaints and without syndromic classification. 

Results:  Direct communication between public health problem owners and analytic developers was informative to both groups and constructive for the solution development process.  The consultancy achieved refinement of the asyndromic detection challenge and of solution requirements.  Participants summarized and evaluated solution approaches and discussed dissemination and collaboration strategies.

Conclusions: A solution meeting the specification of the use case described above could improve human monitoring efficiency with expedited warning of events requiring follow-up, including otherwise overlooked events with no syndromic indicators.  This approach can remove obstacles to collaboration with efficient, minimal data-sharing and without costly overhead.


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DOI: https://doi.org/10.5210/ojphi.v7i3.6354



Online Journal of Public Health Informatics * ISSN 1947-2579 * http://ojphi.org