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Emergency Department (ED)

The Florida Department of Health (FDOH) electronically receives both urgent care center (UCC) data and hospital emergency department (ED) data from health care facilities in 43 of its 67 counties through its Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE-FL). Each submitted record is assigned to one of eleven ESSENCE Syndrome categories based on parsing of chief complaint data. The UCC data come from 22 urgent care centers located in Central Florida, and the ED data come from 161 hospitals located across the state. Traditionally, the data from these two sources are grouped and viewed together. To date, limited investigation has been carried out on the validity of grouping data from UCCS and EDs in ESSENCE-FL. This project will investigate and describe the differences between the data received from these two sources and provide best practices for grouping and analyzing these data sources.

Objective

To identify best practices for grouping emergency department and urgent care data in a syndromic surveillance system.

Submitted by knowledge_repo… on

The State of Ohio, as well as the country, has experienced an increasing incidence of drug ODs over the last three decades [3]. Of the increased number of unintended drug OD deaths in 2008, 9 out of 10 were caused by medications or illicit drugs [1]. In Ohio, drug ODs surpassed MVCs as the leading cause of injury death in 2007. This trend has continued through the most current available data [3]. Using chief complaint data to quickly track changes in the geographical distribution, demographics, and volume of drug ODs may aid public health efforts to decrease the number of associated deaths.

Objective:

Preliminary analysis was completed to define, identify, and track the trends of drug overdoses (OD), both intentional and unintentional, from emergency department (ED) and urgent care (UC) chief complaint data.

 



 

Submitted by Magou on

TOA identifies clusters of patients arriving to a hospital ED within a short temporal interval. Past implementations have been restricted to records of patients with a specific type of complaint. The Florida Department of Health uses TOA at the county level for multiple subsyndromes (1). In 2011, NC DPH, CCHI and CDC collaborated to enhance and evaluate this capability for NC DETECT, using NC DETECT data in BioSense 1.0 (2). After this successful evaluation based on exposure complaints, discussions were held to determine the best approach to implement this new algorithm into the production environment for NC DETECT. NC DPH was particularly interested in determining if TOA could be used for identifying clusters of ED visits not filtered by any syndrome or sub-syndrome. In other words, can TOA detect a cluster of ED visits relating to a public health event, even if symptoms from that event are not characterized by a predefined syndrome grouping? Syndromes are continuously added to NC DETECT but a syndrome cannot be created for every potential event of public health concern. This TOA approach is the first attempt to address this issue in NC DETECT. The initial goal is to identify clusters of related ED visits whose keywords, signs and/or symptoms are NOT all expressed by a traditional syndrome, e.g. rash, gastrointestinal, and flu-like illnesses. The goal instead is to identify clusters resulting from specific events or exposures regardless of how patients present – event concepts that are too numerous to pre-classify.

Objective:

To describe a collaboration with the Johns Hopkins Applied Physics Laboratory (JHU APL), the North Carolina Division of Public Health (NC DPH), and the UNC Department of Emergency Medicine Carolina Center for Health Informatics (CCHI) to implement time-of-arrival analysis (TOA) for hospital emergency department (ED) data in NC DETECT to identify clusters of ED visits for which there is no pre-defined syndrome or sub-syndrome.

 

Submitted by Magou on

One criterion for evaluating the effectiveness of a surveillance system is the system’s positive predictive value. To our knowledge few studies have described the positive predictive value of syndromic surveillance signals for naturally occurring conditions of public health importance.

 

Objective

We evaluated the positive predictive value of signals detected by our syndromic surveillance system.

Referenced File
Submitted by elamb on

In the Northern part of Norway, all General Practitioners (GPs) and hospitals use electronic health records (EHR). They are connected via an independent secure IP-network called the Norwegian Health Network. The newly developed “Snow Agent System” can utilize this environment by distributing processes to, and extracting epidemiological data directly from, the EHR system in a geographic area. This system may enable the GPs to discover local disease outbreaks that may have affected the current patient by providing epidemiological data from the local population. Currently, work is being done to add more functionality to the system. The overall goal for this project is to contribute to a system that will share epidemiological information between GPs and provide them with information about contagious diseases that may be useful in a clinical setting.

To achieve this, we need the GPs to accept and use the system. Nearly one half of information systems fail due to user resistance and staff interference despite the fact that they are technologically sound. One of the reasons for user resistance is lack of user involvement and bad design. The more specialized the system, the more you need user research to unsure success. With this in mind we have decided to take a User-Centred-Design approach to the project.

 

Objective

The Norwegian Centre for Telemedicine plans to establish a peer-to-peer symptom based surveillance network between all GPs, laboratories, accident and emergency units, and other relevant health providers in Northern Norway. This paper describes some initial results from a study of GPs’ user requirements, regarding what they want in return from the system.

Referenced File
Submitted by elamb on

NC DETECT is the Web-based early event detection and timely public health surveillance system in the North Carolina Public Health Information Network. The reporting system also provides broader public health surveillance reports for emergency department visits related to hurricanes, injuries, asthma,  vaccine-preventable diseases, environmental health and others. NC DETECT receives data on at least a daily basis from four data sources: emergency departments, the statewide poison center, the statewide EMS data collection system, a regional wildlife center and laboratory data from the NC State College of Veterinary Medicine. Data from select urgent care centers are in pilot testing.

 

Objective

Managers of the NC DETECT surveillance system wanted to augment standard tabular Web-based access with a Web-based spatial-temporal interface to allow users to see spatial and temporal characteristics of the surveillance data. Users need to see spatial and temporal patterns in the data to help make decisions about events that require further investigation. The innovative solution using Adobe Flash and Web services to integrate the mapping component with the backend database will be described in this paper.

Referenced File
Submitted by elamb on

The CDC recently developed sub-syndromes for classifying disease to enhance syndromic surveillance of natural outbreaks and bioterrorism. They have developed ICD9 classifiers for six GI Illness subsyndromes: Abdominal Pain, Nausea and Vomiting, Diarrhea, Anorexia, Intestinal infections, and Food poisoning. If the number of visits for sub-syndromes varies significantly by age it may impact the design of outbreak detection methods.

 

Objective

We hypothesized that the percentage of visits for the GI sub-syndromes varied significantly with age.

Referenced File
Submitted by elamb on

In addition to utilizing syndromic surveillance data to respond to public health threats and prepare for major incidents, local health departments can utilize the data to examine patient volumes in the emergency departments (EDs) of local hospitals. The information obtained may be valuable to hospital and clinic administrators who are charged with allocating resources. 

Indianapolis represents 92% of Marion County’s population. The county’s public hospital and clinic network provide care for 1 in 3 county residents who are Medicaid enrollees or uninsured. To assess the need for extended hours at eight public primary care clinics in Marion County, Indiana, this study examined the hospital’s ED volume. We hypothesize that

changes in the ED volume trends that corresponded to the start or end of usual clinic hours (8am-5pm) would be evidence of clinic hours’ impact on ED use.

 

Objective

This paper highlights the use of syndromic surveillance data to examine daily trends in ED volume at an urban public hospital.

Referenced File
Submitted by elamb on

The use of syndromic surveillance in Tulsa County began as an attempt to identify symptoms associated with Category A agents, namely Anthrax. The underlying premise for adopting the system was the hope that an astute clinician, upon observing clusters of cases exhibiting certain symptoms, would rapidly notify the local health department so that an epidemiological investigation could be initiated. The system is also designed to send spatial and temporal alerts when cases of pre-defined syndromes are observed. Since 2002, when the system was first implemented, Tulsa Health Department has looked for other ways to integrate syndromic surveillance into its daily operations, and to expand its focus from an exclusive bioterrorism tool, to one that is broader in scope. One such way has been to  utilize the system to identify other syndromes and conditions. Collected emergency data has therefore, been used to identify occurrences of animal bites, mental conditions etc. This paper addresses the use of syndromic surveillance for the identification of heat-related illnesses during the hot Oklahoma summer months.

 

Objective

This paper describes the application of syndromic surveillance methodologies to identify nonbioterrorism syndromes particularly, the incidence of heat-related syndromes during the hot Oklahoma summer months.

Referenced File
Submitted by elamb on

Safe drinking water is essential for all communities. Intentional or unintentional contamination of drinking water requires water utilities and local public health to act quickly. The Water Security (WS) initiative of the U.S. Environmental Protection Agency is a multi-faceted approach involving water utilities and local public health officials (LPH) to identify, communicate, contain, and mitigate a drinking water contamination event. Components of WS include: online water quality monitoring, enhanced security monitoring, consumer complaint surveillance, and innovative uses of public health surveillance data streams. LPH already use multiple surveillance data systems to recognize disease events in a timely manner. However, few of these systems can be integrated or specifically designed for detection of drinking water contamination incidents.

 

Objective

This poster describes the integration of public health surveillance data as a component of an early warning system for detection of a drinking water contamination incident.

Referenced File
Submitted by elamb on
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