Emergency Department (ED)

A number of different methods are currently used to classify patients into syndromic groups based on the patient’s chief complaint (CC). We previously reported results using an “Ngram” text processing program for building classifiers (adapted from business research technology at AT&T Labs). The method applies the ICD9 classifier to a training set of ED visits for which both the CC and ICD9 code are known. A computerized method is used to automatically generate a collection of CC substrings (or Ngrams), with associated probabilities, from the training data. We then generate a CC classifier from the collection of Ngrams and use it to find a classification probability for each patient. Previously, we presented data showing good correlation between daily volumes as measured by the Ngram and ICD9 classifiers.

Objective

Our objective was to determine the optimized values for the sensitivity and specificity of the Ngram CC classifier for individual visits using a ROC curve analysis. Points on the ROC curve correspond to different classification probability cutoffs.

In 2004, the Indiana State Department of Health (ISDH) partnered with the Regenstrief Institute to begin collecting syndromic data from 14 ED’s to monitor bioterrorism-related events and other public health emergencies. Today, Indiana’s public health emergency surveillance system (PHESS) receives approximately 5,000 daily ED visits as real-time HL7 formatted surveillance data from 55 hospitals. The ISDH analyzes these data using ESSENCE and initiates field investigations when human review deems necessary.1 The Marion County Health Department, located in the state’s capitol and most populous county, is the first local health department in Indiana using ESSENCE.

Objective

This paper describes how local and state stakeholders interact with Indiana’s operational PHESS, including resources allocated to syndromic surveillance activities and methods for managing surveillance data flow. We also describe early successes of the system.

Real-time Outbreak and Disease Surveillance (RODS), a syndromic surveillance system created by the University of Pittsburgh has been used in Ohio by the state and local health departments since late 2003. There are currently 133 health care facilities providing 88% coverage of emergency department visits statewide to the RODS system managed by Health Monitoring Systems Inc. (HMS). The system automatically alerts health department jurisdictions when various syndromic thresholds are exceeded.

As part of response protocols, investigators export a case listing in a comma-separated values file which typically includes thousands of lines with each row containing: date admitted, age, gender, zip code, hospital name, visit number, chief complaint, and syndrome. The HMS-RODS web site provides basic graphs and maps, yet lacks the flexibility afforded by ad hoc queries, cross tabulation, and portability enabling off-line analysis.

Objective

This paper describes the integration of open source applications as portable, customizable tools for epidemiologists to provide rapid analysis, visualization, and reporting during surveillance investigations.

Free-text emergency department triage chief complaints (CCs) are a popular data source used by many syndromic surveillance systems because of their timeliness, availability, and relevance. The lack of standardization of CC vocabulary poses a major technical challenge to any automatic CC classification approach. This challenge can be partially addressed by several methods, for example, medical thesaurus, spelling check, manually-created synonym list, and supervised machine learning techniques that directly operate on free text. Current approaches, however, ignore the fact that medical terms appearing in CCs are often semantically related. Our research exploits such semantic relations through a medical ontology in the context of automatic CC classification for syndromic surveillance.

Objective

This paper presents a novel approach of using a medical ontology to classify free-text CCs into syndrome categories.

The Texas Department of State Health Services (DSHS) Health Service Region 8 (HSR 8) encompasses 28 counties in South Central Texas. Of these, 5 counties are covered by a local health department syndromic surveillance system while the remaining counties fall under HSR 8 syndromic surveillance coverage. Of the 23 counties covered by HSR 8, 15 have hospitals with emergency departments. HSR 8 began receiving emergency department data from 3 hospitals for RedBat® syndromic surveillance monitoring in May of 2006. Four syndromes are monitored daily; Influenza-like Illness, Gastrointestinal Illness (GI), Rash-Illness, and Neurologic-Toxicologic Illness. Aberrations are detected by the Gustav algorithm using RedBat’s ‘Automatic Threshold Alert’ feature. The Gustav algorithm [patent pending], developed by ICPA, Inc., is an advanced variation of the cumulative sum method commonly used for aberration detection. The Gustav algorithm does not require an extended baseline level of illness and is very sensitive to small outbreaks; the algorithm also adjusts for weekly periodicity of medical visits.

Objective

This abstract describes the use of syndromic surveillance at a regional health department to detect an outbreak of norovirus in a nursing home facility.

In September 2004, Kingston, Frontenac and Lennox and Addington Public Health began a 2-year pilot project to develop and evaluate an Emergency Department Chief Complaint Syndromic Surveillance System in collaboration with the Ontario Ministry of Health and Long Term Care – Public Health Branch, Queen’s University, Public Health Agency of Canada, Kingston General Hospital and Hotel Dieu Hospital. At this time, the University of Pittsburgh’s Real-time Outbreak and Disease Surveillance (RODS, Version 3.0) was chosen as the surveillance tool best suited for the project and modifications were made to meet Canadian syndromic surveillance requirements. To evaluate the design and implementation of the system, a multi-sectored approach to evaluation was taken. Individual evaluations of the process, technical aspects and of cost/benefit were conducted to demonstrate proof of concept and the associated costs. An overall outcome or effectiveness evaluation will take place in spring 2006.

Objective

This paper outlines the approach used to evaluate an emergency department syndromic surveillance system on the following areas: process and outcome, cost/benefit and technical.

In the aftermath of September 11th, 2001, the potential for subsequent bioterrorism attacks and more recently, the increased awareness of the threat of Avian flu and other communicable diseases, has compelled the Montana healthcare community to mobilize its diagnostic resources for detecting the presence of toxins or infectious biologic agents at the earliest possible moment. This state-wide, pilot initiative integrates disparate Emergency Room data, making patients’ symptoms and diagnoses available for biosurveillance and achieves interoperability among Montana’s emergency facilities.

Objective

This oral presentation describes a multi-agency and multi-center medical data integration system for syndromic surveillance in the State of Montana. This is a significant public health benefit given the recent threats of bio-terrorism and potential viral epidemics, including Bird-Flu.

Syndromic surveillance systems have long been an important part of the public health arena. The long standing goal of early detection of disease outbreak has gained new urgency and requires a broader spectrum in the era of potential bioterrorism. A number of programs have used syndromic surveillance to broadly monitor community health. Outpatient chief complaints as well as positive laboratory tests have been used to monitor the occurrence of natural diseases. 

Limitations of the systems currently attempted include overbroad syndromic categories, labor intensive syndrome recognition training and time intensive manual data entry. Optimal use of laboratory data has been impeded by some of the same issues as well as a too often narrow focus and significant limitations on real time reporting. Given the likelihood of blunt and/or penetrating trauma being a manifestation of terrorist activity, the continuous inclusion of common traumatic and medical emergency conditions is a valuable tool for surveillance.

Objective

This paper describes the use of a multiple collective community health care database to monitor the occurrence of natural and manmade illness and injuries.

Methicillin resistant staphylococcus aureus (MRSA) is a leading cause of skin and soft tissue infections (SSTI). Until recently, S. aureus pneumonia has been considered primarily a nosocomial infection, and was reported infrequently as a cause of severe community-acquired pneumonia. In recent years, there have been several reports of MRSA community-acquired pneumonia cases associated with influenza among healthy individuals resulting in hospitalization or death. During the 2007-08 influenza season, the WA DOH received reports of necrotizing staphylococcus pneumonia associated with flu-like illness and confirmed flu; these included severe cases of pneumonia caused by MRSA. We examined data from our biosurveillance system to describe trends in staphylococcus infection among ED patients and patients hospitalized with pneumonia or influenza in King County, WA.

Objective

We used our biosurveillance system to describe trends in emergency department visits for SSTI as well as staphylococcus pneumonia hospitalization trends.

Drug-related deaths have increased over the past decade throughout the United States. In New York City (NYC), every year there are approximately 900 psychoactive drug-related fatalities with the majority involving opioids. Unintentional drug overdose is the fourth leading cause of early adult death in NYC, and high rates of drug-related morbidity among drug users are evidenced by over 30,000 drug mentions in NYC emergency departments each year. Moreover, nonfatal overdose may be common among chronic drug users. Despite the relationship between fatal and non-fatal overdose clusters and continued increases in drug-related morbidity and mortality, no regular surveillance system currently exists. The implementation of a drug-related early warning system can inform and target a comprehensive public health response addressing the significant health problem of overdose morbidity and mortality.

Objective

This presentation describes how multiple syndromic data sources from emergency medical services ambulance dispatches and emergency department visits can be combined to routinely monitor citywide spatial patterns of adverse drug events and drug morbidity. This information can be used to target information, treatment and prevention services to drug “hotspots,” to provide early warning for drug-related morbidity, and to detect potential increased risk for overdose death.