Travers Debbie

Tracking emergency department (ED) asthma visits is an important part of asthma surveillance, as ED visits can be preventable and may represent a failure of asthma control efforts. When using limited clinical ED datasets for secondary purposes such as public health surveillance, it is important to employ a standard approach to operationally defining ED visits attributable to asthma. The prevailing approach uses only the primary ICD-9-CM diagnosis assigned to the ED visit; however, doing so may underestimate the public health impact of asthma. We conducted this pilot study to determine the value of including ED visits with asthma-related diagnoses in secondary or tertiary positions. For example, for an ED visit with a primary diagnosis of upper respiratory infection and secondary diagnosis of asthma, it is possible that the infection triggered the asthma exacerbation and the visit could be attributed to both infection and asthma.

Objective

Determine operational definition of ED visits attributable to asthma for public health surveillance purposes.

The variability of free text emergency department (ED) data is problematic for biosurveillance, and current methods of identifying search terms for symptoms of interest are inefficient as well as time- and labor-intensive. Our ad hoc approach to term identification for the North Carolina Disease and Epidemiologic Collection Tool (NC DETECT) begins with development of clinical case definitions from which we build automated syndrome queries in standard query language. The queries are used to search free text clinical data from EDs, with the goal of identifying free text terms to match the case definitions. The free text search terms were initially collected from epidemiologists and clinical and technical staff at NC DETECT through informal review of ED data. Over time, we reviewed individual cases missed by our queries and identified additional search terms. We also manually reviewed records to find misspellings, abbreviations and acronyms for known search terms (e.g., dypnea, diff. br. and SHOB for dyspnea), and developed a pre-processor to clean text prior to syndromic classification. The purpose of this project was to develop and test a more standardized approach to search term identification.

Objective

This paper describes and applies a new method for identifying biosurveillance search terms using the Semantic Network of the Unified Medical Language System.

The North Carolina Bioterrorism and Emerging Infection Prevention System (NC BEIPS) receives daily emergency department (ED) data from 33 (29%) of the 114 EDs in North Carolina. These data are available via a Web-based portal and the Early Aberration Reporting System to authorized NC public health users for the purpose of syndromic surveillance (SS). Users currently monitor several syndromes including: gastrointestinal severe, fever/rash illness and influenza-like illness. The syndrome definitions are based on the infection-related syndrome definitions of the CDC and search the chief complaint (CC) and, when available, triage note (TN) and initial temperature fields. Some EDs record a TN, which is a brief text passage that describes the CC in more detail. Most research on the utility of ED data for SS has focused on the use of CC. The goal of this study was to determine the sensitivity, specificity, and both positive and negative predictive value of including TN in the syndrome queries.

Objective

This study evaluates the addition of TN to syndrome queries used in the NC BEIPS.

We report here on the use of the North Carolina Bioterrorism and Emerging Infection Prevention System (NC BEIPS, www.ncbeips.org) to reverse engineer a syndrome definition of influenza for the purpose of influenza surveillance.

The lack of a standardized vocabulary for recording CC complicates the collection, aggregation, and analysis of CC for any purpose, but especially for real-time surveillance of patterns of illness and injury. The need for a controlled CC vocabulary has been articulated by national groups and a plan proposed for developing such a vocabulary. To date there has been no comparison of published CC lists.  This study lays the groundwork for a controlled ED CC vocabulary by comparing selected terms from several published ED CC lists.

Objective

The purpose of this study was to compare the most common chief complaints (CC) from a national emergency department (ED) survey, with four published CC lists in order to identify issues relevant to the creation of a controlled ED CC vocabulary.

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.

In North Carolina, select hospital emergency departments have been submitting data since 2003 for use in syndromic surveillance. These data are collected, stored, and parsed into syndrome categories by the North Carolina Emergency Department Database. The fever with rash illness syndrome is designed to capture smallpox cases. This syndrome was created as a combination of the separate fever and rash syndromes proposed by the consensus recommendations of the CDC’s Working Group on Syndrome Groups.

Objective 

This paper describes the construction of a syndromic surveillance case definition and a test for its ability to capture the appropriate syndromic cases.

The goal of this project is to compare automated syn-dromic surveillance queries using raw chief complaints to those pre-processed with the Emergency Medical Text Processor (EMT-P) 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.

The goal of this paper is to describe a methodology used to create a gold standard set of emergency department (ED) data that can subsequently be used to evaluate the sensitivity and specificity of syndrome definitions.