Perl Trish

Despite the number of infections, hospitalizations, and deaths from influenza each year, developing the ability to predict the timing of these outbreaks has remained elusive. Public health practitioners have lacked a reliable, easy-to-implement method for predicting the onset of a period of elevated influenza incidence in a community. We (a team of statisticians, epidemiologists, and clinicians) have developed a model to help public health practitioners develop simple, adaptable, data-driven rules to define a period of increased disease incidence in a given location. We call this method the Above Local Elevated Respiratory illness Threshold (ALERT) algorithm. The ALERT algorithm is a simple method that defines a period of elevated disease incidence in a community or hospital that systematically collects surveillance data on a particular disease.

Objective

Our objective was to develop a simple, easy-to-use algorithm to predict the onset of a period of elevated influenza incidence in a community using surveillance data.

Emergency Departments (ED) supply critical infrastructure to provide medical care in the event of a disaster or disease outbreak, including seasonal and pandemic influenza [1]. Already over-crowded and stretched to near-capacity, influenza activity augments patient volumes and increases ED crowding [2,3]; high ED patient volumes expected during a true influenza pandemic represents a significant threat to the nation's healthcare infrastructure [4]. EDs ability to manage both seasonal and pandemic influenza surges is dependent on coupling early detection with graded rapid response. Although many EDs have devised influenza response measures, the potential utility of coupling early warning systems with various response strategies for managing influenza outbreaks in the ED setting has not been rigorously studied. While practical use of traditional surveillance systems has been limited due to the several week lag associated with reporting, new internet-based surveillance tools, such as GFT, report surveillance data in near-real time, thus allowing rapid integration into healthcare response planning [5].

Objective

Google Flu Trends (GFT) is a novel internet-based influenza surveillance system that uses search engine query data to estimate influenza activity. This study assesses the temporal correlation of city GFT data to both confirmed cases of influenza, as well as standard crowding indices from one inner-city emergency department (ED).

Measures aimed at controlling epidemics of infectious diseases critically benefit from early outbreak recognition. Through a manual electronic medical record (EMR) review of 5,127 outpatient encounters at the Veterans Administration health system (VA), we previously developed single-case detection algorithms (CDAs) aimed at uncovering individuals with influenza-like illness (ILI). In this work, we evaluate the impact of using CDAs of varying statistical performance on the time and workload required to find a community-wide influenza outbreak through a VA-based syndromic surveillance system (SSS). The CDAs utilize various logical arrangements of EMR data, including ICD-9 codes, structured clinical parameters, and/or an automated analysis of the free-text of the full clinical note. The 18 ILI CDAs used here are limited to the most successful representatives of ICD-9-only and EMR-based case detectors.

Objective

This work uses a mathematical model of a plausible influenza epidemic to begin to test the influence of CDAs on the performance of a SSS.

A major goal of biosurveillance is the timely detection of an infectious disease outbreak. Once a disease has been identified, another very important goal is to find all known cases of the disease to assist public health investigators. Natural language processing (NLP) systems may be able to assist in identifying epidemiological variables and decrease time-consuming manual review of records.

Objective

To identify epidemiologically important factors such as infectious disease exposure history, travel or specific variables from unstructured data using NLP methods.

OBJECTIVE

A “whole-system facsimile” recreates a complex automated biosurveillance system running prospectively on real historical datasets. We systematized this approach to compare the performance of otherwise identical surveillance systems that used alternative statistical outbreak detection approaches, those used by CDC’s BioSense syndromic system or a popular scan statistics.

Objective

There were two objectives of this analysis. First, apply text-processing methods to free-text clinician notes extracted from the VA electronic medical record for automated detection of Influenza-Like-Illness. Secondly, determine if use of data from free-text clinical documents can be used to enhance the predictive ability of case detection models based on coded data.

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.

Measures aimed at controlling epidemics of infectious diseases critically benefit from early outbreak recognition [1]. SSS seek early detection by focusing on pre-diagnostic symptoms that by themselves may not alarm clinicians. We have previously determined the performance of various Case Detector (CD) algorithms at finding cases of influenza-like illness (ILI) recorded in the electronic medical record of the Veterans Administration (VA) health system. In this work, we measure the impact of using CDs of increasing sensitivity but decreasing specificity on the time it takes a VA-based SSS to identify a modeled community-wide influenza outbreak. Objective This work uses a mathematical model of a plausible influenza epidemic to test the influence of different case-detection algorithms on the performance of a real-world syndromic surveillance system (SSS).

To understand the types of false positive cases identified by an Influenza-like illness (ILI) text classifier by measuring the prevalence of ILI-related concepts that are negated, hypothetical, include explicit mention of temporality, experienced by someone other than the patient, or described in templated text that is difficult to process.

Objective

We asked to what extent computerized processing of the full free-text clinical documentation could enhance syndrome detection compared to the sole use of structured data elements from a comprehensive electronic medical record.