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 Time of Arrival Analysis in NC DETECT to Find Clusters of Interest from Unclassified Patient Visit Records Meichun Li*1, Wayne Loschen2, Lana Deyneka3, Howard Burkom2, Amy Ising1 and Anna Waller1 1Emergency Medicine, UNC Chapel Hill, Chapel Hill, NC, USA; 2Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA; 3North Carolina Division of Public Health, Raleigh, NC, USA Objective To describe a collaboration with the Johns Hopkins Applied Physics Laboratory (JHU APL), the North Carolina Division of Pub- lic Health (NC DPH), and the UNC Department of Emergency Med- icine Carolina Center for Health Informatics (CCHI) to implement time-of-arrival analysis (TOA) for hospital emergency department (ED) data in NC DETECT to identify clusters of ED visits for which there is no pre-defined syndrome or sub-syndrome. Introduction TOA identifies clusters of patients arriving to a hospital ED within a short temporal interval. Past implementations have been restricted to records of patients with a specific type of complaint. The Florida Department of Health uses TOA at the county level for multiple sub- syndromes (1). In 2011, NC DPH, CCHI and CDC collaborated to enhance and evaluate this capability for NC DETECT, using NC DE- TECT data in BioSense 1.0 (2). After this successful evaluation based on exposure complaints, discussions were held to determine the best approach to implement this new algorithm into the production envi- ronment for NC DETECT. NC DPH was particularly interested in determining if TOA could be used for identifying clusters of ED vis- its not filtered by any syndrome or sub-syndrome. In other words, can TOA detect a cluster of ED visits relating to a public health event, even if symptoms from that event are not characterized by a prede- fined syndrome grouping? Syndromes are continuously added to NC DETECT but a syndrome cannot be created for every potential event of public health concern. This TOA approach is the first attempt to ad- dress this issue in NC DETECT. The initial goal is to identify clus- ters of related ED visits whose keywords, signs and/or symptoms are NOT all expressed by a traditional syndrome, e.g. rash, gastroin- testinal, and flu-like illnesses. The goal instead is to identify clusters resulting from specific events or exposures regardless of how patients present – event concepts that are too numerous to pre-classify. Methods In late 2011, NC DPH and JHU APL signed a Software License Agreement and soon thereafter CCHI received the TOA software package. In May 2012, the TOA controller was adapted and set up to run against ED visit data for all NC DETECT hospitals. The TOA looks for clusters in all ED visits by hospital based solely on arrival time in both 30-minute and 60-minute intervals. There is no pre-clas- sification of the chief complaints or triage notes into syndromes. TOA alerts are viewable on the NC DETECT Web application and, as of August 2012, users are able to document any actions taken on these alerts. Results From April 15, 2012 to July 31, 2012, TOA generated 173 alerts across all 115 hospitals reporting to NC DETECT. The TOA identi- fied a group of scabies-related ED visits that was not captured in an- other syndrome. The TOA also identified clusters identified by hospitals as disaster-related which included misspellings that had not been previously identified, e.g. “diaster” and “disater,” as well as events involving out-of-town groups that will not be identified spa- tially (Table 1). This preliminary review of TOA alerts did not eval- uate TOA for false negatives. Conclusions Our preliminary review of TOA shows that this algorithm approach can be helpful for identifying clusters of ED visits that are not cap- tured by existing syndromes and can be used to identify hospital cod- ing schemes for disaster events. The TOA will continue to be monitored in our production environment and evaluated for additional effectiveness. We will also explore tools that will display counts of terms within a TOA alert to assist in signal investigation. Table 1: Sample clusters detected with TOA analysis Keywords Cluster detection; Time-of-arrival analysis; Syndrome classification References 1. Burkom H, Loschen W, Kite-Powell A et al. A Collaboration to En- hance Detection of Disease Outbreaks Clustered by Time of Patient Arrival, presented at the International Society for Disease Surveil- lance, 2010 Annual Conference, Park City, Utah, Dec 2, 2010 2. Deyneka L, Xu Z, Burkom H, Hicks P, Benoit S, Vaughan-Batten H, Ising A. Finding time-of-arrival clusters of exposure-related visits to emergency departments in contiguous hospital groups. Emerging Health Threats Journal 2011, 4: 11702 - DOI: 10.3402/ehtj.v4i0.11702 *Meichun Li E-mail: mcli@email.unc.edu Online Journal of Public Health Informatics * ISSN 1947-2579 * http://ojphi.org * 5(1):e13, 2013