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Kass-Hout Taha

Real-time emergency department (ED) data from the BioSense surveillance program for ILI visits and ILI admissions provide valuable insight into disease severity that bridges gaps in traditional influenza surveillance systems that monitor ILI in outpatient settings and laboratory-confirmed hospitalization, but do not quantify the relationship between ILI visits and hospital admissions.

Objective

The purpose of this analysis is to gain understanding of the burden of influenza in recent years through analysis of clinically rich hospital data. Patterns of visits and severity measures such as the ratio of admissions related to influenzalike illness (ILI) by age group from 2007 to 2010 are described.

Referenced File
Submitted by uysz on

The BioSense program’s mission is to support and improve public health surveillance infrastructure and human capacity required to monitor (with minimal lag) critical population health indicators of the scope and severity of acute health threats to the public health; and support national, state, and local responses to those threats. This mission is consistent with the 2006 Pandemic All Hazards Preparedness Act, and 2007 Homeland Security Presidential Directive (HSPD-21), both of which call for regional and nationwide public health situational awareness, through an interoperable network of systems, built on existing state and local situational awareness capability.

 

Objective

The objective of this study is that the Centers for Disease Control and Prevention will update the International Society for Disease Surveillance community on the latest activities for the BioSense program redesign (Centers for Disease Control and Prevention, USA).

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Submitted by hparton on

National Health IT Initiatives are helping to advance the state of automated disease surveillance through incentives to health care facilities to implement electronic medical records and provide data to health departments and use collaborative systems to enhance quality of care and patient safety. While the emergence of a standard for the transfer of surveillance data is urgently needed, migrating from the current practice to a future standard can be a source of frustration. This project represents collaboration among the CDC BioSense Program, Tarrant County Public Health and the ESSENCE Team at the Johns Hopkins University APL. The objectives of the project are to: develop reusable meaningful use messaging software for ingestion health information exchange data available in Tarrant County, demonstrate the use of this data for supporting surveillance, demonstrate the ability to share data for regional and national surveillance using the messaging guide model, and demonstrate how this model can be proliferated among health departments that use ESSENCE by investigating the potential use of cloud technology. The presentation will outline the steps for achieving this goal.

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Submitted by elamb on

This paper describes a hybrid (event-based and indicator-based) surveillance platform designed to streamline the collaboration between domain experts and machine learning algorithms for detection, prediction and response to health-related events (such as disease outbreaks).

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Submitted by elamb on

MUse will make EHR data increasingly available for public health surveillance. For Stage 2, the Centers for Medicare & Medicaid Services (CMS) regulations will require hospitals and offer an option for eligible professionals to provide electronic syndromic surveillance data to public health. Together, these data can strengthen public health surveillance capabilities and population health outcomes (Figure 1). To facilitate the adoption and effective use of these data to advance population health, public health priorities and system capabilities must shape standards for data exchange. Input from all stakeholders is critical to ensure the feasibility, practicality, and, hence, adoption of any recommendations and data use guidelines.

Objective

To develop national Stage 2 Meaningful Use (MUse) recommendations for syndromic surveillance using hospital inpatient and ambulatory clinical care electronic health record (EHR) data

Submitted by uysz on

Understanding the relationship between mental illness and medical comorbidity is an important aspect of public health surveillance. In 2004, an estimated one fourth of the US adults reported having a mental illness in the previous year (1). Studies showed that mental illness exacerbates multiple chronic diseases like cardiovascular diseases, diabetes and asthma (2). BioSense is a national electronic public health surveillance system developed by the Centers for Disease Control and Prevention (CDC) that receives, analyzes and visualizes electronic health data from civilian hospital emergency departments (EDs), outpatient and inpatient facilities, Veteran Administration (VA) and Department of Defense (DoD) healthcare facilities. Although the system is designed for early detection and rapid assessment of all-hazards health events, BioSense can also be used to examine patterns of healthcare utilization.



Objective:

The purpose of this paper was to analyze the associated burden of mental illness and medical comorbidity using BioSense data 20082011.

Submitted by Magou on

In November of 2011 BioSense 2.0 went live to provide tools for public health departments to process, store, and analyze meaningful use syndromic surveillance data. In February of 2012 ESSENCE was adapted to support meaningful use syndromic surveillance data and was installed on the Amazon GovCloud. Tarrant County Public Health Department agreed to pilot the ESSENCE system and evaluate its performance compared to a local version ESSENCE they currently used. The project determined the technical feasibility of utilizing the Internet cloud to perform detailed public health analysis, necessary changes needed to support meaningful use syndromic surveillance data, and any public health benefits that could be gained from the technology or data.

Objective:

This project represents collaboration among CDC’s BioSense Program, Tarrant County Public Health and the ESSENCE Team at the Johns Hopkins University APL. For over six months the Tarrant County Public Health Department has been sending data through the BioSense 2.0 application to a pilot version of ESSENCE on the Amazon GovCloud. This project has demonstrated the ability for local hospitals to send meaningful use syndromic surveillance data to the Internet cloud and provide public health officials tools to analyze the data both using BioSense 2.0 and ESSENCE. The presentation will describe the tools and techniques used to accomplish this, an evaluation of how the system has performed, and lessons learned for future health departments attempting similar projects.

 

Submitted by Magou on

Objective:

The objective of this project is to enable the ESSENCE system to read in, utilize, and export out meaningful use syndromic surveillance data using the Health Level 7 (HL7) v2.5 standard. This presentation will detail the technical hurdles with reading a meaningful use syndromic surveillance data feed containing multiple sources, deriving a common meaning from the varying uses of the standard and writing data out to a meaningful use HL7 2.5 format that can be exported to other tools, such as BioSense 2.0 (2). The presentation will also describe the technologies employed for facilitating this, such as Mirth, and will discuss how other systems could utilize these tools to also support processing meaningful use syndromic surveillance data.

Introduction:

In order to utilize the new meaningful use syndromic surveillance data sets that many public health departments are now receiving, modifications to their systems must be made. Typically this involves enabling the storage and processing of the extra fields the new standard contains. Open source software exists, such as Mirth Connect, to help with reading and interpreting the standard. However, issues with reliably reading data from one source to another arise when the standard itself is misunderstood. Systems that process this data must understand that while the data they receive is in the HL7 v2.5 standard format, the meaning of the data fields might be different from provider to provider. Additional work is necessary to sift thro

Submitted by jababrad@indiana.edu on
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