Emergency Department (ED)

The Real-time Outbreak and Disease Surveillance system collects chief complaints as free text and uses a naïve Bayesian classifier called CoCo to classify the complaints into syndromic categories. CoCo 3.0 has been trained on 28,990 manually clas-sified chief complaints. The free text chief com-plaints are challenging to work with, due to problems caused by linguistic variations such as synonyms, abbreviations, acronyms, truncations, concatenations, misspellings and typographic errors. Failure to correct these word variations may result in missed cases, thereby decreasing sensitivity of detection.

Objective

To determine whether preprocessing chief complaints before automatically classifying them into syndromic categories improves classification performance.

In 2004, the Indiana State Department of Health (ISDH) contracted with the Regenstrief Institute to build an information exchange infrastructure to support the collection of surveillance data. This pilot program involves implementation of electronic reporting in 46 of the state’s 114 emergency departments. Chief complaint data are collected and analyzed to identify clusters of disease earlier than a diagnosis can be confirmed or the disease reported to the ISDH. The system utilized the chief complaint coder CoCo to map the chief complaints into one of eight syndromes. This evaluation was completed after one-third of the pilot facilities were operational.

Objective

This evaluation was conducted to determine if any pilot hospitals have operational practices that may affect the ability of the Public Health Emergency Surveillance System to accurately and efficiently identify clusters of infectious disease in Indiana.

The North Carolina Disease Event Tracking and Epidemiologic Collection Tool (NC DETECT) receives a designated set of data elements electronically available from 110 emergency departments (EDs) (98%) on at least a daily basis via a third party data aggregator. While automated processes monitor for data quality problems such as improper file formats or missing required elements, data corruption can occur at several stages before receipt, and if undetected, data can appear reliable. Hospitals might map to standard codes incorrectly, data aggregators might manipulate text improperly, or updates might be confused with original records. These inaccuracies cause delays and oversights in identifying events of public health importance.

Objective

This study evaluates the validity of a subset of ED data collected in NC DETECT, as well as measures the effectiveness of the data quality processes in place for this surveillance system.

In November of 2001 a syndromic surveillance system was established in Los Angeles (LA) County to analyze emergency department (ED) chief complaints in select hospitals. Chief complaints were analyzed and categorized into a syndrome (rash, respiratory, neurological, gastrointestinal), and an algorithm was developed to create a daily threshold for each category. Questions remain as to what events can be detected by the system in a timely manner. On the community level, of interest is whether an outbreak with a wide epidemiological curve would have the intensity of case visits needed to trigger a signal. On the individual level, of interest is the length of time it takes for a person with a given disease characteristic to seek medical attention, whether medical care is sought in the ED first, and how the syndromic system classifies them upon visiting the ED. To address these questions the 2004 LA County West Nile community-wide outbreak was selected for review, with a focus on the more severe neuro-invasive cases.

Objective

To evaluate the effectiveness of monitoring emergency room chief complaints as an indicator for a neuro-invasive disease outbreak.

An important goal of influenza surveillance is to provide public health decisionmakers with timely estimates of the severity of community-wide influenza. One potential indicator is the number of influenza hospitalizations. In New York City methods for estimating influenza hospitalizations include asking hospitals to self-report, sending field staff to review medical records, and analyzing electronic hospital discharge data available months after influenza season is over. Given the limitations of each of these approaches, we evaluated whether electronic ED data, received daily for syndromic surveillance, could be used to monitor hospitalizations during influenza  epidemics.

Objective

To evaluate whether trends in influenza hospitalizations can be monitored using ED syndromic surveillance data.

Facing public health threats of bioterrorism and emerging infectious diseases (EID), the traditional passive surveillance system is not efficient and outmoded. Evidences reveal that several newly developed syndromic surveillance system (SSS) in different countries can provide an active, powerful, timely, and effective epidemiological investigation. Using this SSS, we can find non-specific symptoms, and set up baseline clinical data and epidemic threshold. Due to English barriers and standardized language problem in the past, we initiated to develop an emergency department-based syndromic surveillance system (ED-SSS) using clinical data involving both check-list format chief complaints (CoCo) and International Classification of Diseases, Ninth Revision (ICD-9) that best fit the situations in Taiwan.

Objective

The aims of this study are to set up a SSS for detecting newly EID outbreaks early using more standardized information of triage CoCo of hospital emergency department in metropolitan Taipei City to (1) break through Chinese language barrier; (2) investigate its feasibility to detect influenza like illness (ILI) outbreaks using integrated clinical and epidemiological information installed within information technology system; and (3) compare the sensitivity, specificity, and kappa value of ILI between ICD-9 and CoCo.

In the fall of 2006, the Ohio Department of Health (ODH) and the Indiana State Department of Health (ISDH) proactively began general discussions regarding surveillance issues of mutual interest. Both states, having operational syndromic surveillance systems, thought value could be added to one another’s program by sharing data across their common border. Ohio receives emergency department chief complaint data from 130 of its hospitals; Indiana from 76 hospitals. The ODH uses the EpiCenter System managed by Health Monitoring Systems, while the ISDH Public Health Emergency Service System uses Electronic Surveillance System for the Early Notification of Communitybased Epidemics. Each state desired to view the new shared data through its own system. A formal memorandum of understanding was developed and signed by both states to support syndromic data sharing. Data began flowing between the two states in April, 2008.

Objective

The ODH and the ISDH enhanced their individual syndromic surveillance efforts through cross-border sharing of emergency department chief complaint data.

Capital Health is a regional health care organization, which provides services for over one million inhabitants in the Edmonton area of Alberta, Canada. Traditionally, disease surveillance under its jurisdiction has been paper-based and records maintained by different departments in several locations. Before the Alberta Real Time Syndromic Surveillance Net (ARTSSN), there was no centralized database or unified approach to surveillance and automated reporting despite rich electronic health data in the region. The existing labor-intensive manual surveillance process is inefficient and inherently susceptible to human error. Its effectiveness is sub-optimal in detecting outbreaks of emerging infectious diseases, and clusters of injuries or toxic exposures. The ultimate objective of ARTSSN is to enhance public health surveillance through earlier and more sensitive detection of clusters and trends, with subsequent tracking and response through an integrated, automated surveillance and reporting system.

Objective

ARTSSN is a pilot public health surveillance project developed for the Capital Health region of Alberta, Canada and funded by Alberta Health and Wellness. This paper describes the advantages of using ARTSSN and comparing information derived from multiple electronic data sources simultaneously for real time syndromic surveillance.

The Maryland Department of Health and Mental Hygiene conducts enhanced surveillance using the Electronic Surveillance System for Early Notification of Community-Based Epidemics (ESSENCE). The current version of ESSENCE for the National Capital Region consists of information from multiple data sources for syndromic surveillance in Maryland, Washington DC, and Virginia. Chief complaint data from emergency department (ED) visits and over-the-counter (OTC) medications are categorized into syndromes and alerts are generated when observed counts are outside the expected range. ESSENCE alerts users to unusual counts of a particular syndrome based on both temporal and spatial distribution for enhanced surveillance of disease activity. While several studies have examined the usefulness of ED data to detect the start of the influenza season, a lack of information exists on the usability of OTC sales to detect influenza. OTC data may provide an earlier alert to illness than other sources, if people self-treat with OTC medications.

Objective

This study examines the ability of syndromic surveillance data to detect seasonal influenza. ED visits for influenza-like illness and OTC flu medication sales are evaluated to determine whether these data sources are useful in the detection of the influenza season. Data sources that can detect seasonal influenza may also be used to help detect the start of pandemic influenza.

The Bioterrorism Surveillance Unit of the Los Angeles County (LAC) Department of Public Health, Acute Communicable Disease Control (ACDC) program analyzes Emergency Department (ED) data daily. Currently capturing over 40% of the ED visits in LAC, the system categorizes visits into syndrome groups and analyzes the data for aberrations in count and spatial distribution. Typical usage of the system may be extended for various enhanced surveillance activities by creating additional syndrome categories tailored to specific illnesses or conditions. This report describes how ED data was utilized for enhanced surveillance regarding: (1) a sustained heat wave in California that broke temperature and duration records, (2) a 30,000 gallon raw sewage spill that prompted the closure of two miles of beach, and (3) an alert to ACDC of a high school student who attended school while symptomatic for meningitis.

Objective

To describe enhanced surveillance provided by the LAC Department of Public Health’s syndromic surveillance system for monitoring health events in 2006.