Medical Simulation Training for First Response to Chemical, Biological, Radiological, Nuclear Events

Award Information
Agency:
Department of Defense
Branch
Army
Amount:
$730,000.00
Award Year:
2006
Program:
SBIR
Phase:
Phase II
Contract:
W81XWH-05-C-0042
Award Id:
73703
Agency Tracking Number:
A043-182-0545
Solicitation Year:
n/a
Solicitation Topic Code:
n/a
Solicitation Number:
n/a
Small Business Information
9881 Broken Land Parkway Suite 400, Columbia, MD, 21046
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
128822736
Principal Investigator:
Dale Olsen
President, CEO
(443) 283-2504
dale.olsen@simmersion.com
Business Contact:
Dale Olsen
President, CEO
(443) 283-2504
dale.olsen@simmersion.com
Research Institution:
n/a
Abstract
Increasing demands for military medical bioterrorism preparedness training make it critical to develop programs that teach healthcare professionals how to distinguish between symptoms of common problems and similar symptoms that indicate a terrorist attack. SIMmersionT LLC (www.simmersion.com), in partnership with the National Capital Area Medical Simulation Center, (simcen.usuhs.mil), and the Henry M. Jackson Foundation (www.hjf.org), proposes to provide a computer-based training system using computer-simulated patients. The resulting simulation system will provide the necessary training, practice, and assessments required to improve a trainee's capability to identify indicators, signs, and symptoms of chemical, biological, radiological, nuclear, and high-explosive (CBRNE) attacks and to take appropriate action. Users will learn to distinguish smallpox and Marburg hemorrhagic fever from chicken pox and Rocky Mountain spotted fever, which have similar symptoms but do not indicate a bioterrorist threat. The User will engage in a simulated conversation throughout training, perform a medical exam, and implement a post-diagnosis patient-management plan. Skills required to take a detailed medical history and implement an effective plan apply to other CBRNE events as well. The simulation features distributive learning and performance metrics capabilities to help users learn from their experience.

* information listed above is at the time of submission.

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