Emergency Department (ED)

The threat of pandemic and seasonal influenza has drawn attention to syndromic surveillance systems for early detection of influenza-like illness. Since 2005, the Miami-Dade County Health Department has implemented ESSENCE (Electronic Surveillance System for the Early Notification of Community-based Epidemics) to monitor emergency department data for influenza-like Illness (ILI) using chief complaint information. This study evaluates the ability of the ESSENCE ILI chief complaint grouping for identifying true ICD-9 diagnosed influenza.

Objective

Previous studies have examined the utility of different methods of syndromic grouping. This study evaluates the utility of ESSENCE for ILI surveillance.

On 12/14/06, a windstorm in western Washington caused 4 million residents to lose power; within 24 hours, a surge in patients presented to emergency departments (EDs) with carbon monoxide (CO) poisoning. As previously described, records of all patients presenting to King County EDs with CO poisoning between 12/15/06 to 12/24/06 (n=279) were abstracted, of which 249 met the case definition and eligibility requirements. We attempted to identify each of the 249 confirmed cases of CO poisoning in our syndromic ED data set by comparing the hospital name, date, time, age, sex, zip code, chief complaint, and diagnoses across the two data sets. We designated each record as an exact match, likely match, possible match, or unmatched on the basis of the available fields.

Objective

We evaluated ED and emergency medical services data for describing an outbreak of CO poisoning following a windstorm, and determined whether loss of power was followed by an increase in other health conditions.

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.

Abbreviation, misspellings, and site specific terminology may misclassify chief complaints syndromes. The Emergency Medical Text Processor (EMT-P) is system that cleans emergency department chief complaints and returns standard terms. However, little information is available on the implementation of EMT-P in a syndromic surveillance system.

Objective

To describe the implementation and baseline evaluation of EMT-P developed by the University of North Carolina.

Syndromic surveillance can be a useful tool for the early recognition of outbreaks and trends in emergency department (ED) data. In addition, as a more timely data source than traditional disease reporting, syndromic data may also be leveraged to identify individual disease cases, increasing the utility for first time or redundant case recognition.

San Diego County (COSD) performs daily ED syndromic surveillance. In order to assess the utility for early identification of specific conditions of public health interest (e.g., salmonellosis, meningitis, hazardous exposures, heat-related illness), a novel process entitled Priority Infectious Conditions Capture, was developed.

Objective

This paper describes an assessment of an enhanced surveillance process used to identify reportable diseases and conditions of public health importance from ED chief complaint data in COSD.

In February of 2007, the Bureau of Epidemiology (BOE) received a request from Houston Department of Public Works to investigate a possible rise in gastrointestinal (GI) illness associated with complaints about poor water quality in a Northeastern Houston neighborhood. To investigate this complaint, BOE combined case report data with syndromic data from our Real-Time Outbreak Disease Surveillance (RODS). The Houston RODS collects and synthesizes real-time chief complaint data from 34 area hospitals and health facilities, representing approximately 70% coverage of licensed ER beds in Harris County. The system uses a Naïve Bayes Classifier to categorize ER chief complaints into 7 different syndromes, including GI illness.

Objective

To investigate public concern over a possible increase in GI illness associated with water quality complaints in Northeast Houston.

Outbreak detection algorithms for syndromic surveillance data are becoming increasingly complex. Initial algorithms focused on temporal data but newer methods incorporate geospatial dimensions. As methods evolve, it is important to understand the effects on detection of both algorithm parameters and population characteristics. Intensive, iterative data analyses are required to accomplish this. Even with leading-edge computer hardware, it can take weeks or months to complete analyses using advanced signal detection techniques such as the space-time scan statistic in the SaTScan program.

Given the strategic significance and national security implications of timely and accurate detection, proper tools for studying and thus improving increasingly complex surveillance algorithms are warranted.

Objective

We describe a method to perform computationally intensive analyses on large volumes of syndromic surveillance data using open-source grid computing technology.

Although Electronic Surveillance System for the Early Notification of Community Based Epidemics (ESSENCE) provides tools to detect a significant alert regarding an unusual public health event, combining that information with other surveillance data, such as 911 calls, school absenteeism and poison control records, has proved to be more sensitive in detecting an outbreak. On Monday, June 16, Florida Poison Information Network, which takes after-hours and weekend calls for Miami-Dade County Health Department (MDCHD), contacted the Office of Epidemiology and Disease Control about five homeless persons that visited the same hospital simultaneously with gastrointestinal symptoms on Saturday, June 14. Poison control staff asked MDCHD to investigate further to determine whether it was an outbreak.

Objective

To illustrate how MDCHD utilized ESSENCE in order to track a gastrointestinal outbreak in a homeless shelter.

Emergency Department (ED) syndromic surveillance data for influenza-like illness (ILI) have been found to provide timely and representative information about current influenza activity in NYC. DOHMH monitors visits daily from 50 of 61 EDs, capturing about 94% of all ED visits in NYC. Since January 1, 2007, DOHMH has been receiving disposition data (e.g., hospitalized, discharged) from a subset of EDs. Currently, disposition data is received from 37 EDs (approximately 1/3 of all visits by the next day and >60% of all visits within 1 week).

More detailed hospitalization data, including date, demographics, and diagnosis on all NYC hospitalizations are routinely collected by the New York State Department of Health Statewide Planning and Research Cooperative System (SPARCS). SPARCS is subject to a 2-3 year reporting lag, thus limiting its timeliness and prospective use. However, SPARCS data from prior to January 1, 2007 can supplement the ED syndromic data to develop a model for ILI hospitalizations and calculate excess hospitalizations attributable to influenza that can be used in near realtime, particularly in the event of a pandemic.

Objective

To use ED syndromic surveillance data to monitor hospitalizations for ILI and calculate excess hospitalizations attributable to influenza.

The North Carolina Disease Event Tracking and Epidemiologic Collection Tool (NC DETECT) serves public health users across NC at the local, regional and state levels, providing early event detection and situational awareness capabilities. At the state level, our primary users are in the General Communicable Disease Control Branch of the NC Division of Public Health. NC DETECT receives 10 different data feeds daily including emergency department visits, emergency medical service runs, poison center calls, veterinary laboratory test results, and wildlife treatment.

In order to fulfill our users’ needs with NC DETECT’s limited staff, business intelligence tools are utilized for the acquisition and processing of our multiple, disparate data sources as well as reporting our findings to our numerous end users. Business intelligence can be described as a broad category of application programs and technologies for gathering, storing, analyzing, and providing access to data to help enterprise users make better business decisions.

Objective

We report here on how NC DETECT uses business intelligence tools to automate both data capture and reporting in order to run a comprehensive surveillance system with limited resources.