Cloud

Cost-effective, flexible and innovative tools that integrate disparate data sets and allow sharing of information between geographically dispersed collaborators are needed to improve public health surveillance practice. Gossamer Health (Good Open Standards System for Aggregating, Monitoring and Electronic Reporting of Health), http://gossamerhealth.org, is an open source system, suitable for server or "cloud" deployment, that is designed for the collection, analysis, interpretation and visualization of syndromic surveillance data and other indicators to monitor population health. The Gossamer Health system combines applied public health informatics research conducted at the University of Washington Center for Public Health Informatics and Washington State Department of Health, in collaboration with other state and local health jurisdictions, the International Society for Disease Surveillance and the Centers for Disease Control and Prevention.

Objective

The goal of this work is to make available to the public health community an open source system that makes available in a standards-based, modular fashion the basic tools required to conduct automated indicator-based population health surveillance. These tools may be deployed in a flexible fashion on health department servers, in the Amazon EC2 cloud, or in any combination, and are coupled through well-defined standards-based interfaces.

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.

Two significant barriers to greater use of syndromic surveillance techniques are computational time and software complexity. Computational time refers to the time for many methods (for example, scan statistics and AMOEBA statistics) to create reliable results. Software complexity refers to the difficulty of setting up and configuring suites of software to collect data, analyze it, and visualize the results. Both of these barriers can be partially surmounted by the use of cloud computing resources.

Objective

To describe how use of cloud computing resources can improve the timely provision of disease surveillance analyses.

Major challenges in syndromic surveillance today include lack of standardization in syndrome definitions and limited ability to detect outbreaks of specific and rare diseases. To generate situational awareness surveillance results across various regions must be comparable and epidemiologically well defined. In addition, the high cost of obtaining and maintaining powerful computing resources (e.g., parallel computers) needed for data processing and analysis, and absence of a protocol for data sharing, highlight some of the obstacles to achieving situational awareness.

Cloud computing is an enabling technology that can overcome these challenges and facilitate new and novel approaches to surveillance.

Objective

We present a Cloud Computing based approach to disease surveillance that facilitates efficient data collection, processing and storage, as well as new concepts for data sharing and data fusion, disease search and situational awareness.