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Veteran Affairs

Description

Emerging infections, both natural and intentional, have provided an impetus for improved disease surveillance and response. The recognition of the interdependence of health care systems and public health infrastructure provides an opportunity to expand beyond traditional disease-based surveillance to a more comprehensive, integrated approach that leverages existing electronic information. The Veterans Affairs (VA) hospital system is uniquely positioned to perform multi-institutional enhanced electronic surveillance. A wealth of electronic information and technology resources are available in all VA hospitals and their associated clinics, as each facility uses the same standardized Computer Patient Record System. Influenza-like illness (ILI) is a common clinical syndrome of diverse etiology that presents with respiratory and systemic symptoms. The NC health department mandates the reporting of ILI from emergency departments to facilitate monitoring of seasonal ILI and serve as an important component of pandemic preparedness. Existing surveillance systems utilize an ICD-9 respiratory code screen and subsequent manual chart review which is timeconsuming and insensitive. Automated medical record review using more comprehensive electronic data may improve the system’s timeliness and efficiency.

 

Objective

To use data collected by NC-VET to create an automated ILI surveillance program and compare its accuracy and efficiency to the existing program.

Submitted by elamb on
Description

BioSense data includes Department of Defense and Veterans Affairs ambulatory care diagnoses and procedures, as well as Laboratory Corporation of America lab test orders. Data are mapped to eleven syndrome categories. SaTScan is a spatio-temporal technique that has previously been applied to surveillance at the metropolitan area level. Visualization of national results involves unique issues, including displaying cluster information that crosses jurisdictions, zip codes with highly variant data volume, and evaluating large multiple state clusters. SaTScan was first implemented in June 2005 in the BioSense application for daily monitoring at CDC’s BioIntelligence Center.

 

Objective

The objective is to describe the visualization and monitoring of the national spatio-temporal SaTScan results in the BioSense application. This is the first application of this algorithm to a national early event detection and situational awareness system.

Submitted by elamb on
Description

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.

Submitted by elamb on
Description

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.

Submitted by elamb on
Description

The BioSense system receives patient level clinical data from > 370 hospitals and 1100 ambulatory care Departments of Defense and Veterans Affairs medical facilities. Visits are assigned as appropriate to 78 sub-syndromes, including respiratory syncytial virus (RSV). Among infants and children < 1 year of age, RSV is the most common cause of bronchiolitis and pneumonia; 0.5% to 2% require hospitalization. Increasingly, RSV is also recognized as a major cause of pneumonia in elderly adults.

 

Objective

To analyze final diagnosis data available to BioSense and determine its potential utility for surveillance of RSV illness.

Submitted by elamb on
Description

The BioSense system currently receives real-time data from more than 370 hospitals, as well as national daily batched data from over 1100 Department of Defense and Veterans Affairs medical facilities. BioSense maps chief complaint and diagnosis data to 11 syndromes and 78 sub-syndromes (indicators). One of the 11 syndromes is gastrointestinal (GI) illness and 6 of the subsyndromes (abdominal pain; anorexia, diarrhea, food poisoning, intestinal infections, ill-defined; and nausea and vomiting) represent gastrointestinal concepts.

 

Objective

To describe the potential use of BioSense chief complaint and final diagnosis data for GI illness surveillance.

Submitted by elamb on
Description

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.

Submitted by elamb on
Description

Veterans accessing Veterans Affairs (VA) health care have higher suicide rates and more characteristics associated with suicide risk, including being male, having multiple medical and psychiatric comorbidities, and being an older age, compared with the general U.S. population. The Veterans Crisis Line is a telephone hotline available to Veterans with urgent mental health concerns; however, not all Veterans are aware of this resource. By contrast, telephone triage is a national telephone-based triage system used by the VA to assess and triage all Veterans with acute medical or mental health complaints.

Objective

To characterize Veterans who call telephone triage because of suicidal ideation (SI) or depression and to identify opportunities for suicide prevention efforts among these telephone triage users using a biosurveillance application.

 

Submitted by uysz on
Description

Telephone triage is a relatively new data source available to biosurveillance systems.1-2Because early detection and warning is a high priority, many biosurveillance systems have begun to collect and analyze data from non-traditional sources [absenteeism records, overthe-counter drug sales, electronic laboratory reporting, internet searches (e.g. Google Flu Trends) and TT]. These sources may provide disease activity alerts earlier than conventional sources. Little is known about whether VA telephone program influenza data correlates with established influenza biosurveillance.

Objective:

To evaluate the utility and timeliness of telephone triage (TT) for influenza surveillance in the Department of Veterans Affairs (VA).

Submitted by Magou on
Description

Antimicrobial prescriptions are a new data source available to the Veterans Health Administration (VHA) biosurveillance program. Little is known about whether antiviral or antibacterial prescription data correlates with influenza ICD-9-CM coded encounters. We therefore evaluated the utility and timeliness of antiviral and antibacterial utilization for influenza surveillance.

Submitted by teresa.hamby@d… on