Emergency Department (ED)

On August 20th and 21st, 2007, Ohio sustained heavy rains which resulted in severe flooding over a nine-county area in the north-central part of the state. Increased hospital emergency department (ED) visits were expected for gastrointestinal illnesses, but this was not observed. After a media report on September 4, 2007 suggested swarms of mosquitoes were plaguing residents, ED character-specific data were analyzed to see if these data could confirm the report.

Objective

This retrospective analysis of text fragments in emergency department chief complaints illustrates the usefulness of syndromic surveillance in providing timely situational awareness of insect prevalence in post-flood situations.

One limitation of syndromic surveillance systems based on emergency department (ED) data is the time and expense to investigate peak signals, especially when that involves phone calls or visits to the hospital. Many EDs use electronic medical records (EMRs) which are available remotely in real time. This may facilitate the investigation of peak signals.

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.

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.

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.

An N-gram is a sub-sequence of n items from a given sequence where n can be 1, 2,…, n and the items can be letters or words. N-gram models are widely used in statistical natural language processing [3]. In the syndromic surveillance context, N-grams can be used to cluster or classify natural language data.  They can also help in the design of kernels for machine learning algorithms such as support vector machines to learn from text data.  This work calculates the similarity percentages of ED or TH reasons to syndromic fingerprints using Ngrams. We define “reasons similarity” as the percentage of matched N-grams derived from the reasons field of an ED or TH record with the fingerprint of a syndrome. The fingerprint of a syndrome is a list of frequent N-grams related to this syndrome.  This fingerprint is constructed by collecting a large sample of classified reasons data for a particular syndrome, calculating all of the N-grams for this set and then selecting the most frequent N-grams to form a profile or fingerprint. N-gram generation may require extensive processing time especially for large files but this issue has been addressed by using parallel computation.

Objective

The objective of this work is to identify syndromic fingerprints in reasons for entering an emergency department (ED) or calling telehealth (TH). It also demonstrates that these fingerprints are valuable for classification.

Case detection from chief complaints suffers from low to moderate sensitivity. Emergency Department (ED) reports contain detailed clinical information that could improve case detection ability and enhance outbreak characterization. We developed a text processing system called Topaz that could be used to answer questions from ED reports, such as: How many new patients have come to the ED with acute lower respiratory symptoms? Of the respiratory patients, how many had a productive cough or wheezing? How many of the respiratory patients have a past history of asthma?

Objective

To evaluate how well a text processing system called Topaz can identify acute episodes of 55 clinical conditions described in ED notes.

The inception of syndromic surveillance has spawned a great deal of research into emergency department chief complaint data. In addition to its use as an early warning system of a bioterror or outbreak event, many health departments are attempting to maximize the utility of the information to augment chronic and communicable disease surveillance. Hence, it can be used to enhance the traditional methods of surveillance. Using syndromic data to describe what could be the normal for a geographic area may be useful in monitoring a population for disease trends. Prevention efforts could be concentrated during a particular time of year. In addition, geospatial shifts in directional trends may indicate an unusual occurrence related to the utilization of emergency department services.

Objective

To describe the geographical mean as well as the directional trends of syndromes for the District of Columbia using temporal and geospatial analyses.