The intent of the National Syndromic Surveillance Program Community of Practice (NSSP CoP) is to support open and dynamic participation of any individual or organization working on or interested in increasing the quality, utility, and availability of syndromic surveillance data. The goal of this assessment was to collect feedback from members regarding the barriers and challenges to participating in the NSSP CoP activities and utilizing its related tools.
Presented September 27, 2018.
This presentation offers practical tips on how to create a successful and sustainable community of practice.
Presenter
Deborah W. Gould, PhD., Division of Health Informatics and Surveillance, Centers for Disease Control and Prevention
The Automated Hospital Emergency Department Data (AHEDD) System was designed to detect early indicators of bioterrorism and naturally occurring health risks. Initial development includes real-time data collection from four pilot hospitals, an automated syndromic surveillance application, and the capability of raw data analysis for further investigation and follow-up. This automated system frees hospital and State staff from manual reporting and analysis; and has a broad application for Public Health, collecting both chief complaint and diagnosis codes. As the project expands we plan to add the remaining 22 acute care hospitals; include poisoning, asthma, and injury surveillance; and assess electronic disease reporting from diagnosis codes and data linkage with other public health data stores, such as Environmental Health Tracking, and pre-hospital data.
Objective
This paper describes the use of technology to create an automated, real-time surveillance system with the capacity for early detection and alerting of potential health threats, and the capability to facilitate prompt investigation and increased efficiency for both New Hampshire hospital and the Division of Public Health Service resources.
BioSense currently receives demographic and chief complaint data from more than 360 hospitals and text radiology reports from 36 hospitals. Detection of pneumonia is an important as several Category A bioterrorism diseases as well as avian influenza can manifest as pneumonia. Radiology text reports are often received within 1-2 days and may provide a faster way to identify pneumonia than coded diagnoses. Objective To study the performance of a simple keyword search of radiology reports for identifying pneumonia.
Yearly epidemics of respiratory diseases occur in children. Early recognition of these and of unexpected epidemics due to new agents or as acts of biological/chemical terrorism is desirable. In this study, we evaluate the ordering of chest radiographs as a proxy for early identification of epidemics of lower respiratory tract disease. This has the potential to act as a sensitive real-time surveillance tool during such outbreaks.
Objective:
Create a tool for monitoring respiratory epidemics based on chest radiograph ordering patterns.
Infectious disease surveillance is important for disease control as well as to inform prevention and treatment [1]. While influenza surveillance data coverage and quality has improved significantly in recent years due to resource investments and advances in information technology, the need remains for improvements in data dissemination to the wider community.
Objective
This paper describes a review of modes and styles of the online dissemination of national influenza surveillance data.
This article describes the methodology and results of Team #134Ãs submission to the 2007 ISDS Technical Contest.
This paper describes a research effort to map the literature of bioterrorism agents research worldwide using bibliographic analysis, content map analysis, and co-authorship analysis based on Medline data. The objectives of our research are to (a) identify researchers who have expertise in the research domain of bioterrorism agents from the world, (b) identify major institutions and countries where these researchers reside, and (c) identify emerging topics and trends in bioterrorism agents research.
Emergency Department (ED) triage notes are clinical notes that expand upon the chief complaint, and are included in the AHIC minimum dataset for biosurveillance.1 Clinical notes can improve the accuracy of keyword-based syndromes but require processing that addresses negated terms.2,3 The North Carolina Disease Event Tracking and Epidemiologic Collection Tool (NC DETECT) syndrome classifier searches for keywords in free-text chief complaint and triage note data for the purpose of early event detection. Initial attempts to handle negation were included in the syndrome queries beginning in August 2005. Query statements were written to identify and ignore select symptoms immediately following negated terms, such as denies fvr or no h/a. Many negated terms, however, were not addressed and continue to create false positive syndrome hits. The purpose of this pilot was to address negation with NegEx (a negation tool)4, supplemented by selected modules from the Emergency Medical Text Processor (EMTP), a chief complaint pre-processor.
Objective
The objective of this pilot study was to explore methods for addressing negation in triage notes.
The purposes of this study are to validate a keyword-based text parsing algorithm for identifying fractures and compare radiology results with chief complaint and ICD-9 final diagnoses.