Emergency Department (ED)

As part of public health protection activities conducted in support of the G8 Summit in Sea Island, GA, June 2004, DPH implemented SS in the state’s coastal region using information provided from ED visits, 911 calls, and pharmacy sales. Following this high-profile event, questions arose about whether to maintain the ED system and about whether and where to extend its use in GA.  Despite the emergence of practice-based guidance for conducting SS and the growing experience of public health agencies, little guidance is available regarding strategies for identifying sites where SS should be targeted.

 

Objective

This paper describes the strategy used by the Georgia Division of Public Health (DPH) in implementing syndromic surveillance (SS), including criteria for prioritizing localities and the early results of applying these criteria in initiating new emergency department (ED)-visit based systems.

With increased penetration of clinical information system products and increased interest in clinical data exchange, a variety of clinician’s notes are becoming available for surveillance. Chief complaints have been studied extensively, and emergency department notes have received attention, but narrative clinic visit notes have gotten little attention.

Objective

To assess the performance of an unmodified, general purpose natural language processing system to detect fever, and to assess the feasibility of parsing visit notes for syndromic surveillance.

Emergency Department surveillance methods currently rely on identification of acute illness by tracking chief complaint or ICD9 discharge codes. Newer generation electronic medical records are now capturing additional  information such as vital signs. These data have the potential for identifying disease syndromes earlier than the traditional methods.

Objective

This paper describes the temporal relationship between numbers of cases of fever, recorded as discrete vital sign data in an electronic medical record, and ICD9 Influenza Like Illnesses in the Emergency Department at the University of Wisconsin Hospital.

The spatial scan statistic [1] detects significant spatial clusters of disease by maximizing a likelihood ratio statistic over a large set of spatial regions. Typical spatial scan approaches either constrain the search regions to a given shape, reducing power to detect patterns that do not correspond to this shape, or perform a heuristic search over a larger set of irregular regions, in which case they may not find the most relevant clusters. In either case, computation time is a serious issue when searching over complex region shapeso r when analyzing a large amount of data. Analternative approach might be to search over all possible subsets of the data to find the  most relevant pat-terns, but since there are exponentially many subsets, an exhaustive search is computationally infeasible.

Objective

We present a new method of "linear-time subset scanning" and apply this technique to various spatial outbreak detection scenarios, making it computationally feasible (and very fast) to perform spatial scans over huge numbers of search regions.

Currently, Indiana monitors emergency department patient chief complaint data from 73 geographically dispersed hospitals. These data are analyzed using the Electronic Surveillance System for the Early Notification of Community-based Epidemics application. 

While researchers continue to improve syndromic detection methods, there is significant interest among public health practitioners regarding how to most effectively use the currently available tools. The Public Health Emergency Surveillance System (PHESS) staff have developed and refined a daily syndromic alert analysis and response process based on experiences gained since November 2004.

Objective

This paper describes how the Indiana State Department of Health PHESS staff responded to a syndromic surveillance alert related to a bioterrorism preparedness event.

In North Carolina, select hospital emergency departments have been submitting data since 2003 for use in syndromic surveillance. These data are collected, stored, and parsed into syndrome categories by the North Carolina Emergency Department Database. The fever with rash illness syndrome is designed to capture smallpox cases. This syndrome was created as a combination of the separate fever and rash syndromes proposed by the consensus recommendations of the CDC’s Working Group on Syndrome Groups.

Objective 

This paper describes the construction of a syndromic surveillance case definition and a test for its ability to capture the appropriate syndromic cases.

The City of Atlanta, volunteer organizations, and the faith community operate several homeless shelters throughout the city. Services available at these shelters vary, ranging from day services, such as meals, mail collection, and medical clinics, to overnight shelter accommodations. In addition to the medical clinics available at these facilities, the Atlanta homeless population also utilizes emergency departments in Fulton County for their health care needs.

Objective

This paper describes a cluster of Streptococcus pneumoniae infections identified through emergency department syndromic surveillance.

Rhode Island implemented the Real-time Outbreak and Disease Surveillance (RODS) system, developed in 1999 by the University of Pittsburgh’s Center for Biomedical Informatics. This system is based on real-time information from hospital emergency departments that is transmitted and analyzed electronically for the purpose of early detection of and situational awareness for public health emergencies. Through this system, chief complaint is reported in real-time. Diagnoses, coded in the International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM), are reported to the RI RODS system as they become available. Three hospitals are currently participating in a pilot implementation of the RI RODS system.

Preliminary work by a CDC Working Group (CDCWG) developed recommendations for syndrome definitions for use in syndromic surveillance programs. Ten syndromes, based on ICD-9-CM diagnosis codes, identified diseases associated with critical bioterrorism-associated agents or indicative of naturally occurring infectious disease outbreaks. As a component of the evaluation of the RI RODS system, we evaluated the RI RODS chief complaint classifier (CoCo) using ICD-9-CM codes and the CDCWG work as the gold standard.

Objective

This paper presents findings related to the evaluation of the CoCo used in the pilot implementation of a syndromic surveillance system in Rhode Island.

Efforts have been made to standardize and prioritize the description and evaluation of syndromic surveillance systems. Systematic information on the performance of existing systems can be used to assess and compare the value of these systems, and inform decisions regarding their use. 

The Michigan’s Emergency Department Syndromic Surveillance System (MSSS) is an implementation of an early version of the Real-time Outbreak and Disease Surveillance system developed by the University of Pittsburgh, which collects patient chief complaint data from emergent care facilities in real time. At the Michigan Department of Community Health the system has been in use since 2003. Alterations to the system and recruitment of data contributors have been ongoing. The primary stated purpose of the MSSS is earlier detection of outbreaks of severe illness, enabling a more rapid public health response and intervention to reduce the impact of public health threats.

Objective

This work describes key characteristics of MSSS and reports on its evaluation.

Immediately following September 11, 2001, the District of Columbia Department of Health began a syndromic surveillance program based on emergency room (ER) visits. ER logs are faxed on a daily basis to the health department, where health department staff code them on the basis of chief complaint, recording the number of patients in each of the following syndromic categories: death, sepsis, rash, respiratory complaints, gastrointestinal complaints, unspecified infection, neurological, or other complaints. These data are analyzed daily and when a syndromic category shows an unusually high occurrence, a patient chart review is initiated to determine if the irregularity is a real threat. 

A time series analysis of the data from this system has shown that with the application of a variety of detection algorithms, the syndromic surveillance data does well in identifying the onset of the flu season. In addition, simulation studies using the same data have shown that over a range of simulated outbreak types, the univariate and multivariate CUSUM algorithms performed more effectively than other algorithms. The multivariate CUSUM was preferred to the univariate CUSUM for some but not all outbreak types.

Objective

This paper evaluates an ER syndromic surveillance system based on simulation studies and comparisons with other surveillance systems.