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

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. Practice Implications: 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.

[1]  Daniel B. Neill,et al.  Identifying Emerging Novel Outbreaks In Textual Emergency Department Data , 2015, Online Journal of Public Health Informatics.

[2]  Howard Burkom,et al.  A Term-based Approach to Asyndromic Determination of Significant Case Clusters , 2015, Online Journal of Public Health Informatics.

[3]  S. Greene,et al.  Refining Historical Limits Method to Improve Disease Cluster Detection, New York City, New York, USA , 2015, Emerging infectious diseases.

[4]  Theresa M. Hamby,et al.  Identifying Clusters of Rare and Novel Words in Emergency Department Chief Complaints , 2014, Online Journal of Public Health Informatics.

[5]  Ramona Lall,et al.  Detecting Unanticipated Increases in Emergency Department Chief Complaint Keywords , 2014, Online Journal of Public Health Informatics.

[6]  Aaron Kite-Powell,et al.  A Dictionary-based Method for Detecting Anomalous Chief Complaint Text in Individual Records , 2014, Online Journal of Public Health Informatics.

[7]  H. Burkom,et al.  Time of Arrival Analysis in NC DETECT to Find Clusters of Interest from Unclassified Patient Visit Records , 2013, Online Journal of Public Health Informatics.

[8]  Andre Charlett,et al.  An Improved Algorithm for Outbreak Detection in Multiple Surveillance Systems , 2013, Statistics in medicine.

[9]  W. Chapman,et al.  An ISDS-Based Initiative for Conventions for Biosurveillance Data Analysis Methods , 2013, Online Journal of Public Health Informatics.

[10]  Paul H. Garthwaite,et al.  Statistical methods for the prospective detection of infectious disease outbreaks: a review , 2012 .

[11]  Daniel B. Neill,et al.  Detecting previously unseen outbreaks with novel symptom patterns , 2011 .

[12]  Stephanie Seneff,et al.  Automatic Drug Side Effect Discovery from Online Patient-Submitted Reviews: Focus on Statin Drugs , 2011 .

[13]  Michael D. Moskal,et al.  A survey of usage protocols of syndromic surveillance systems by state public health departments in the United States. , 2009, Journal of public health management and practice : JPHMP.

[14]  J. Meynard,et al.  Proposal of a framework for evaluating military surveillance systems for early detection of outbreaks on duty areas , 2008, BMC public health.

[15]  L. Hutwagner,et al.  The bioterrorism preparedness and response Early Aberration Reporting System (EARS) , 2003, Journal of Urban Health.

[16]  Vlado Keselj,et al.  Document clustering using character N-grams: a comparative evaluation with term-based and word-based clustering , 2005, CIKM '05.

[17]  J. Overhage,et al.  Framework for evaluating public health surveillance systems for early detection of outbreaks: recommendations from the CDC Working Group. , 2004, MMWR. Recommendations and reports : Morbidity and mortality weekly report. Recommendations and reports.

[18]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[19]  L. Lee,et al.  Updated guidelines for evaluating public health surveillance systems: recommendations from the Guidelines Working Group. , 2001, MMWR. Recommendations and reports : Morbidity and mortality weekly report. Recommendations and reports.

[20]  Ted Dunning,et al.  Accurate Methods for the Statistics of Surprise and Coincidence , 1993, CL.