Ising Amy

NC DETECT receives data on at least a daily basis from five data sources: emergency departments (ED), the statewide poison center (CPC), the statewide EMS data collection system, a regional wildlife center and laboratories from the NC State College of Veterinary Medicine.  A Web portal is available to users at state, regional and local levels and provides syndromic surveillance reports as well as reports for broader public health surveillance such as injury, occupational health, and post-disaster.  The current portal is built on access controls initially designed in 2002 for hospital-based users only.  The role-based access was modified slightly in 2004 to accommodate public health epidemiologists (PHEs) at the local, regional and state levels who wanted county-based report access.  The design used, however, was shortsighted and limited.  For example, the controls cannot accommodate certain users’ access to non-ED data sources as well as the ability to retrieve protected health information (PHI) via the portal when needed for investigation.  These evolving user needs have led to a full system redesign with a much more robust security model.

Objective

This paper describes the role-based access used in the North Carolina Disease Event Tracking and Epidemiologic Collection Tool (NC DETECT) Web portal for early event detection and timely public health surveillance.

The UNC Department of Emergency Medicine (UNC DEM) conducted an online survey to better understand the surveillance needs of Infection Control Practitioners (ICPs) in North Carolina and solicit feedback on the utility of the North Carolina Disease Event Tracking and Epidemiologic Collection Tool (NC DETECT).

The North Carolina Bioterrorism and Emerging Infection Prevention System (NC BEIPS) serves public health users across North Carolina at the local, regional and state levels, providing syndromic surveillance capabilities.  At the state level, our primary users are in the General Communicable Disease Control Branch of the NC Division of Public Health.  NC BEIPS currently receives daily data from the North Carolina Emergency Department Database (NCEDD), Carolina Poison Control Center (CPC), Prehospital Medical Information System (PreMIS) and the Piedmont Wildlife Center (PWC). Future data sources will include the North Carolina State University College of Veterinary Medicine Laboratories.  The PWC is a non-profit organization dedicated to wildlife rehabilitation, education, and scientific study of health and disease in wildlife populations.  PWC admits approximately 3,000 animals annually, including mammals, birds, and reptiles, the majority of which are from 21 counties in central North Carolina.  

Objective

This poster will illustrate how a novel data source, wildlife health center data, is being incorporated and used in a syndromic surveillance system.

Text-based syndrome case definitions published by the Center for Disease Control (CDC)1 form the basis for the syndrome queries used by the North Carolina Disease Event Tracking and Epidemiologic Collection Tool (NC DETECT). Keywords within these case definitions were identified by public health epidemiologists for use as search terms with the goal of capturing symptom complexes from free-text chief complaint and triage note data for the purpose of early event detection and situational awareness. Initial attempts at developing SQL queries incorporating these search terms resulted in the return of many unwanted records due to the inability to control for certain terms imbedded within unrelated free text strings. For example, a query containing the search term “h/a”, a common abbreviation for headache, also returns false positives such as “cough/asthma”, “skin rash/allergic reaction” or “psych/anxiety”.  Simple abbreviations without punctuation, such as “ha”, were even more problematic.  Global wildcards ('%') indicate that zero or more characters of any type may substitute for the wildcard.2 The term “ha” as a synonym for "headache" appears frequently in the data, but searching this term bracketed by global wildcards returns any instance where the two letters appear together (e.g. pharyngitis, hand, hallucinations, toothache). Using global wild cards to search for common symptoms such as headache using simple abbreviations, with or without specialized punctuation, results in the return of many unwanted false positive records. We describe here the advanced application of SQL character set wildcards to address this problem.

Objective

This paper describes a novel approach to the construction of syndrome queries written in Structured Query Language (SQL). Through the advanced application of character set wildcards, we are able to increase the number of valid records identified by our queries while simultaneously decreasing the number of false positives.

Emergency Department (ED) triage notes are clinical notes that expand upon the chief complaint, and are included in the AHIC minimum dataset for biosurveillance.1  Clinical notes can improve the accuracy of keyword-based syndromes but require processing that addresses negated terms.2,3  The North Carolina Disease Event Tracking and Epidemiologic Collection Tool (NC DETECT) syndrome classifier searches for keywords in free-text chief complaint and triage note data for the purpose of early event detection. Initial attempts to handle negation were included in the syndrome queries beginning in August 2005.  Query statements were written to identify and ignore select symptoms immediately following negated terms, such as denies fvr or no h/a.  Many  negated terms, however, were not addressed and continue to create false positive syndrome hits.  The purpose of this pilot was to address negation with NegEx (a negation tool)4, supplemented by selected modules from the Emergency Medical Text Processor (EMTP), a chief complaint pre-processor. 

Objective

The objective of this pilot study was to explore methods for addressing negation in triage notes.

The North Carolina Disease Event Tracking and Epidemiologic Collection Tool (NC DETECT) receives a designated set of data elements electronically available from 110 emergency departments (EDs) (98%) on at least a daily basis via a third party data aggregator. While automated processes monitor for data quality problems such as improper file formats or missing required elements, data corruption can occur at several stages before receipt, and if undetected, data can appear reliable. Hospitals might map to standard codes incorrectly, data aggregators might manipulate text improperly, or updates might be confused with original records. These inaccuracies cause delays and oversights in identifying events of public health importance.

Objective

This study evaluates the validity of a subset of ED data collected in NC DETECT, as well as measures the effectiveness of the data quality processes in place for this surveillance system.

Data quality for syndromic surveillance extends beyond validating and evaluating syndrome results. Data aggregators and data providers can take additional steps to monitor and ensure the accuracy of the data. In North Carolina, hospitals are mandated to transmit electronic emergency department data to the North Carolina Disease Event Tracking and Epidemiologic Tool (NC DETECT) system at least every 24 hours. Protocols have been established to ensure the highest level of data quality possible. These protocols involve multiple levels of data validity and reliability checks by NC DETECT staff as well as feedback from end-users concerning data quality. Hospitals also participate in the data quality processes by providing metadata including historical trends at each facility.

Objective

The purpose of this project is to describe the initiatives used by the NC DETECT to ensure the quality of ED data for surveillance.

To determine if ticks on dogs can provide early warning for tick-borne diseases in people.