Chief Complaint

There exists no standard set of syndromes for syndromic surveillance, and available syndromic case definitions demonstrate substantial heterogeneity of findings constituting the definition. Many syndromic case definitions require the presence of a syndromic finding (e.g., cough or diarrhea) and a fever.

 

Objective

Automated syndromic surveillance systems often use chief complaints as input. Our objective was to determine whether chief complaints accurately represent whether a patient has any of the following febrile syndromes: Febrile respiratory, febrile gastrointestinal, febrile rash, febrile neurological, or febrile hemorrhagic.

The objective of this project was to classify and extract mental health emergency department (ED) visits from the Houston Real-time Outbreak and Disease Surveillance (RODS) system. In addition, this project will offer a

This paper describes a novel approach to the statistical classification of free-text chief complaints for the purpose of syndromic surveillance.

In the spring of 2005, the ISDH began using Electronic Surveillance System for the Early Notification of Community-based Epidemics  (ESSENCE) application to analyze emergency department (ED) chief complaint data for syndromic surveillance purposes.  While granting hospitals and local health departments access to their data through ESSENCE has been desirable since the start of the PHESS project, an aggressive timeline made it necessary to direct all resource capacity toward first establishing hospital ED data connections.  The Marion County Health Department (Indianapolis) was the only LHD in the state with access to its 14 hospitals through ESSENCE.

However, because hospitals and local health departments (except Marion County) did not have access to their data through ESSENCE, any syndromic alert follow-up conducted by the ISDH was accomplished primarily by telephone.   This method, while feasible, was inefficient.  The ISDH felt that alert data follow-up could be greatly facilitated if hospitals and LHDs could view these data through ESSENCE just as the ISDH was doing.

Objective

This paper describes how the Indiana State Department of Health (ISDH) improved response capability by increasing local health department (LHD) and hospital access to syndromic surveillance data as part of the stateís evolving Public Health Emergency Surveillance System (PHESS).

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.

Syndromic surveillance of emergency department(ED) visit data is often based on computerized classifiers which assign patient chief complaints (CC) tosyndromes. These classifiers may need to be updatedperiodically to account for changes over time in the way the CC is recorded or because of the addition of new data sources. Little information is available as to whether more frequent updates would actually improve classifier performance significantly. It can be burdensome to update classifiers which are developed and maintained manually. We had available to us an automated method for creating classifiers thatallowed us to address this question more easily. The “Ngram” method, described previously, creates a CC classifier automatically based on a training set of patient visits for which both the CC and ICD9 are available. This method measures the associations of text fragments within the CC (e.g. 3 characters for a “3-gram”) with a syndromic group of ICD9 codes. It then automatically creates a new CC classifier based on these associations. The CC classifier thus created can then be deployed for daily syndromic surveillance.

Objective

Our objective was to determine if performance of the Ngram classifier for the GI syndrome was improved significantly by updating the classifier more frequently.

The existing New York State Department of Health emergency department syndromic surveillance system has used patient’s chief complaint (CC) for assigning to six syndrome categories (Respiratory, Fever, Gastrointestinal, Neurological, Rash, Asthma). The sensitivity and specificity of the CC computer algorithms that assign CC to syndrome categories are determined by using chart review as the criterion standard. These analyses are used to refine the algorithm and to evaluate the effect of changes in the syndrome definitions. However, the chart review (CR) method is labor intensive and expensive. Using an automated ICD9 code-based assignment as a surrogate for chart review could offer a significant cost reduction in this process and allow us to survey a much larger sample of visits.

Objective To examine sub-syndrome distributions among BioSense emergency department (ED) chief complaint and final diagnosis based data and to observe patterns by hospital system, age, and gender.

Syndromic surveillance aims to decrease the time to detection of an outbreak compared to traditional surveillance methods. Emergency department (ED) syndromic surveillance systems vary in their methodology and complexity and are usually based on presenting chief complaints. Prior work in ED-based syndromic surveillance has shown conflicting results on agreement between chief complaint and discharge diagnosis, which may be syndrome-dependent. The use of ED discharge diagnosis may improve surveillance validity if it can be done in a timely fashion.

Objective 

The purpose of this study is to characterize the relationship of emergency department chief complaint and final primary ICD-9 diagnosis assigned at the time of emergency department disposition for patients with symptoms and/or ICD-9 codes associated with influenza like illness (ILI) using an electronic medical record.