BioSurveillance

COMBS is facilitating analyst workflows and collaboration, greatly accelerating the management of a bio-event, effectively implementing new capabilities and technologies, and providing opportunities for a wide variety of organizations to contribute data and tools that support their own goals while supporting and governing the ecosystem collaboratively.

Objective

The goal of DTRA's Biosurveillance Ecosystem (BSVE) program is to significantly reduce the time required to identify threats to human health and respond appropriately. The Draper Team is developing the Collaborative Overarching Multi-feed Biosurveillance System (COMBS) for BSVE to revolutionize biosurveillance (BSV) capabilities. Analysts will benefit from rapid and thorough information access, as will local public health authorities and individual citizens.

Multiple data sources are used in a variety of biosurveillance systems. With the advent of new technologies, globalization, high performance computing, and "big data" opportunities, there are seemingly unlimited potential data streams that could be useful in biosurveillance. Data streams have not been universally defined in either the literature or by specific biosurveillance systems. The definitions and framework that we have developed enable a characterization methodology that facilitates understanding of data streams and can be universally applicable for use in evaluating and understanding a wide range of biosurveillance activities- filling a gap recognized in both the public health and biosurveillance communities.

Objective

To develop a data stream-centric framework that can be used to systematically categorize data streams useful for biosurveillance systems, supporting comparative analysis

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.

For more than a decade, biosurveillance systems (and more recently BioSense) have been employed in the United States. Efforts to drastically expand these surveillance capacities have been a national priority given concerns about national security. However, there has been little emphasis on value or increasing value to communities or agencies contributing and analyzing data. This qualitative analysis focused on all biosurveillance stakeholders and the opportunity to enhance interoperability and reuse of data and systems.

Objective

To understand the perspective of biosurveillance stakeholders and how their participation creates value for them as well as public health departments.

The Internet has created an information revolution that spans across all knowledge domains and removes temporal and geographic barriers. Various disparate tools allow individuals to communicate with each other across these barriers. We also have an abundance of electronic resources containing health information locked inside free text components. The lack of integration of these tools and electronic resources has prevented harnessing of information for use in integrated and novel ways. We developed an application for 'Semantic Processing and Integration of Distributed Electronic Resources for Epidemiology' or EpiSPIDER (http://www.epispider.org), an integrative web-based information processing system that uses these tools and electronic resources to create an information environment for enhancing the surveillance of emerging infectious disease threats to global health.

Objective

To demonstrate how natural language processing (NLP) of clinical records can contribute to case detection and characterization in biosurveillance.

In the aftermath of September 11th, 2001, the potential for subsequent bioterrorism attacks and more recently, the increased awareness of the threat of Avian flu and other communicable diseases, has compelled the Montana healthcare community to mobilize its diagnostic resources for detecting the presence of toxins or infectious biologic agents at the earliest possible moment. This state-wide, pilot initiative integrates disparate Emergency Room data, making patients’ symptoms and diagnoses available for biosurveillance and achieves interoperability among Montana’s emergency facilities.

Objective

This oral presentation describes a multi-agency and multi-center medical data integration system for syndromic surveillance in the State of Montana. This is a significant public health benefit given the recent threats of bio-terrorism and potential viral epidemics, including Bird-Flu.

Objective: Emerging and re-emerging infectious diseases (EID/REID) involve large populations at risk and thus they might lead to rapidly increasing cases or case fatality rates. Living in this global village, cross-country or cross-continent spread has occurred more frequently in recent decades, implying that epidemics of any infectious disease can expand from local to national to international if control efforts are not effective.

Infectious disease outbreaks require rapid access to information to support a coordinated response from healthcare providers and public health officials. They need to know the size, spread, and location of the outbreak, and they also need access to models that will help them to determine the best strategy to contain the outbreak. 

There are numerous software tools for outbreak detection, and there are also surveillance systems that depend on communication between health care professionals. Most of those systems use a single type of surveillance data (e.g., syndromic, mandatory reporting, or laboratory) and focus on human surveillance.

However, there are fewer options for planning responses to outbreaks. Modeling and simulation are complex and resource-intensive. For example, EpiSims and EpiCast, developed by the National Institute of Health Models of Infectious Disease Agent Study involve large, diverse datasets and require access to high-performance computing.

Cyberenvironments are an integrated set of tools and services tailored to a specific discipline that allows the community to leverage the national cyberinfrastructure in their research and teaching. They provide data stores, computational capabilities, analysis and visualization services, and interfaces to shared instruments and sensor networks.

The National Center for Supercomputing Applications is applying the concept of cyberenvironments to infectious disease surveillance to produce INDICATOR.

Objective

This paper describes INDICATOR, a biosurveillance cyberenvironment used to analyze hospital data and generate alerts for unusual values.

The goal of this project is to create a set of clinical condition categories based on explicit criteria for use in biosurveillance programs. The categories will be defined and keywords and ICD-9-CM diagnosis codes for implementation will be proposed.