Winston Carla

Monitoring laboratory test reports could aid disease surveillance by adding diagnostic specificity to early warning signals and thus improving the efficiency of public health investigation of detected signals. Laboratory data could also be employed to direct and evaluate interventions and countermeasures, while monitoring outbreak trends and progress; this would ultimately result in better outbreak response and management, and enhanced situation awareness. Since Electronic Laboratory Reporting (ELR) has the potential to be more accurate, timely, and cost-effective than reporting by other means of communication (e.g., mail, fax, etc.), ELR adoption has been systematically promoted as a public health priority.  However, the continuing use of non-standard, local codes or text to represent laboratory test type and results complicates the use of ELR data in public health practice. Use of structured, unique, and widely available coding system(s) to support the concepts represented by locally assigned laboratory test order and result information improves the computational characteristics of ELR data. Out of several coding strategies available, the Office of the U.S. National Coordinator for Health Information Technology has recently suggested incorporating Logical Observation Identifiers Names and Codes (LOINC) for laboratory orders and Systemized Nomenclature of Medicine- Clinical Terms (SNOMED CT) codes for laboratory results to standardize ELR.

Objective:

To examine the use of LOINC and SNOMED CT codes for coding laboratory orders and results in laboratory reports sent from 63 non-federal hospitals to the BioSense Program in calendar year 2011.

 

The Joint VA/DoD BioSurveillance System for Emerging Biological Threats project seeks to improve situational awareness of the health of VA/DoD populations by combining their respective data. Each system uses a version of the Electronic Surveillance System for Early Notification of Community-Based Epidemics (ESSENCE); a combined version is being tested. The current effort investigated combining the datasets for disease cluster detection. We compared results of retrospective cluster detection studies using both separate and joined data. — Does combining datasets worsen the rate of background cluster determination?

— Does combining mask clusters detected on the separate datasets?

— Does combining find clusters that the separate datasets alone would miss?

Objective:

We examined the utility of combining surveillance data from the Departments of Defense (DoD) and Veterans Affairs (VA) for spatial cluster detection.

 

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).

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.

An objective of the Joint VA/DoD BioSurveillance System for Emerging Biological Threats project is to improve situational awareness of the health of combined VA and DoD populations. DoD and VA both use versions of the Electronic Surveillance System for the Early Notification of Community-Based Epidemics (ESSENCE). With a retrospective outpatient data collection available, we analyzed relative coverage and timeliness of the two systems to understand potential benefits of a joint system.

Objective

We determined the utility and effective methodology for combin- ing patient record information from the Departments of Veterans Af- fairs (VA) and Defense (DoD) health surveillance systems. 

Drug poisoning, or overdose, is an epidemic problem in the United States1,2. In keeping with national trends, a recent study combining U.S. Veterans Health Administration (VHA) data with the National Death Index showed increases in opioid overdose mortality from 2001 to 20093. One of the challenges in monitoring the overdose epidemic is that collecting cohort data to analyze overdose rates can be laborintensive. Moreover, analysts are often unable to collect real-time data on overdose events. To explore solutions to these challenges, we examined opioid overdose by using Veteran healthcare data already being collected for syndromic surveillance.

Objective

To examine inpatient admissions for opioid overdose among U.S. Veterans using national-level surveillance data.

 

The National Strategy for Biosurveillance promotes a national effort to improve early detection and enable ongoing situational awareness of all-hazards threats. Implicit in the Strategy’s implementation plan is the need to upgrade capabilities and integrate multiple disparate data sources, including more complete electronic health record (EHR) data into future biosurveillance capabilities. Thus, new biosurveillance applications are clearly needed. Praedico™ is a next generation biosurveillance application that incorporates cloud computing technology, a Big Data platform utilizing MongoDB as a data management system, machine-learning algorithms, geospatial and advanced graphical tools, multiple EHR domains, and customizable social media streaming from public health-related sources, all within a user friendly interface.

Objective

The purpose of our study was to conduct an initial assessment of the biosurveillance capabilities of a new software application called Praedico™ and compare results obtained from previous queries with the Electronic Surveillance System for the Early Notification of Community-Based Epidemics (ESSENCE).