Ms. Araya recently joined Pangaea as an application developer. Ms. Araya has over 3 years of experience in developing software applications using web 2.0 technologies as well as the design and development of relational databases for large and complex datasets. Prior to joining Pangaea, Ms. Araya worked a software engineer for an IT consulting firm where she developed an online travel application as well as an inventory management system. Prior to that, Ms Araya worked as an application developer for the Ministry of Finance in Asmara, Eritrea where she developed the Ministry’s Personnel System to manage employee records. Ms Araya holds a B.S. in Computer Science from University of Asmara, Asmara, Eritrea and an M.S. in Computer Science from Maharishi University of Management, Fairfield, IA. We are pleased to have Helen as part of the Pangaea team.
CDC expands flu-tracking efforts
The Centers for Disease Control (CDC) has a number of initiatives to attempt to track Influenza Like Illness (ILI) and in particular outbreaks of H1N1. On Sept. 1, CDC began securely exchanging public health data daily via the Nationwide Health Information Network (NHIN). The pilot project is gathering flu symptom data from health care providers in Indiana, New York and Washington state.The full article can be found at:
http://fcw.com/Articles/2009/10/26/CDC-expands-flu-tracking-efforts.aspx
Pangaea Information Technologies, Ltd. has been working on a project in collaboration with Rush University Medical Center called GUARDIAN (Geographic Utilization of Artificial Intelligence in Real-Time for Disease Identification and Notification) which provides an AI based approach to detecting diseases including ILI and H1N1. GUARDIAN is currently generating daily and weekly reports on likely and confirmed H1N1 cases.
GUARDIAN uses a variety of techniques — from simple code look-up-tables to advanced natural-language processing algorithms — to transform and standardize these disparate data sources into a relational, hierarchical data structure that is optimized for infectious disease modeling. Additionally, GUARDIAN has a flexible and modular architecture in addition to a real-time web-based user interface. This allows GUARDIAN to be used for monitoring of any set of infectious diseases in real-time (in addition to the daily and weekly summary reports that it generates).
Automated Real-Time H1N1 Monitoring
On October 28th, GE Healthcare announced that they had received a grant from the CDC to perform “near real-time” monitoring of the spread of the H1N1 influenza virus. This project pulls daily reports from patient data already being collected in GE’s Centricity EMR system to send to the CDC. The advantage of this system is that GE is able to leverage the medical data that they are already collecting on an estimated 14 million patients. The obvious disadvantage is that their system is specifically limited to data from hospitals and clinics that are already using GE’s system for their EMR.
For the past few years, Pangaea Information Technologies has been working on a project in collaboration with Rush University Medical Center called GUARDIAN (Geographic Utilization of Artificial Intelligence in Real-Time for Disease Identification and Notification) which takes a different approach to solving this problem. Like GE’s system, GUARDIAN is currently generating daily reports on likely and confirmed H1N1 cases. However, the GUARDIAN system is not tied to a particular EMR. During its development, GUARDIAN has interfaced with EMRs from EPIC, Picis and Cerner using the industry-standard HL7 messaging protocol.
GUARDIAN uses a variety of techniques – from simple code lookup-tables to advanced natural-language processing algorithms – to transform and standardize these disparate data sources into a relational, hierarchical data structure that is optimized for infectious disease modeling. Additionally, GUARDIAN has a flexible and modular architecture in addition to a real-time web-based user interface. This allows GUARDIAN to be used for monitoring of any set of infectious diseases in real-time (in addition to the daily and weekly summary reports that it generates).
GE’s contribution to the CDC’s H1N1 surveillance is a major improvement over the analysis of 2-week-old data that they were previously limited to. However, we at Pangaea believe that the ability to interface with any medical record system and the flexibility to quickly add/update disease-modeling profiles will make the GUARDIAN system a more powerful and useful biosurveillance system for the next major outbreak.
RCMP-T
The Parsons and Pangaea team were awarded a contract to develop the next version of the Range Complex Master Planning Tool (RCMP-T) for the U.S. Army under the Parsons U.S. Army Sustainable Range Program (SRP) contract. The RCMP-T is a web based application that serves the SRP community by providing installations a capability of developing, staffing, and publishing a comprehensive Range Complex Master Plan (RCMP) for their respective installations and/or areas of responsibility. An RCMP is a requirement of AR 350-19, The Sustainable Range Program. The RCMP is a detailed master plan for an installation and/or area of responsibility covering current and projected live fire and non-live ranges, special training facilities, training areas, and the infrastructure required to support SRP’s planning efforts.
GUARDIAN
Pangaea was awarded a contract under a grant with Rush University Medical Center from the U.S. Army Medical Research and ACQ Activity, Telemedicine and Advanced Technology Research Center to continue the development for GUARDIAN (Geographic Utilization of Artificial Intelligence in Real-Time for Disease Identification and Notification). GUARDIAN is a real-time, scalable, extensible, automated, knowledge-based biological threat agent (BTA) detection and diagnosis system. The purpose of GUARDIAN is to conduct rapid analysis of multiple parameters contained in medical records collected by hospital emergency departments in real time in order to detect probable specific disease occurrence. The goal of this system is to assist clinicians in detecting potential BTAs as quickly and effectively as possible based on pre-diagnostic data in order to better respond to and mitigate the potential effects of a large-scale outbreak.