Monday, November 25, 2024

MHS MTEC RFP: interoperable Medical Automated Systems (iMAS) Data Analytics and Artificial Intelligence (AI) Algorithm Development for interoperable Algorithms for Care and Treatment (iACT)

Notice ID: MTEC-21-07-iACT

The MTEC mission is to assist the U.S. Army Medical Research and Development Command (USAMRDC) by providing cutting-edge technologies and supporting effective material life cycle management to transition medical solutions to industry that protect, treat, and optimize Warfighters’ health and performance across the full spectrum of military operations. MTEC is a biomedical technology consortium collaborating with multiple government agencies under a 10-year renewable Other Transaction Agreement (OTA), Agreement No. W81XWH-15-9-0001, with the U.S. Army Medical Research Acquisition Activity (USAMRAA).

The iACT prototype is a software system utilizing AI and Machine Learning based Clinical Decision Support algorithms to be used to support military medical personnel in their duties. The system will be developed with a Graphical User Interface (GUI) and Application Program Interface (API) to enable military personnel to input data and provide military personnel with AI-based alerts for their patients. The prototype shall include the development and integration of the medical database and AI based algorithms that are free of errors and minimizes the risks of data fitting and dimensionality through scalable data subsets. AI capabilities include, but are not limited to, predicting point of patient decompensation, predicting injury patterns, assessing patient status, identifying treatments and medications relating to the initial assessments, and providing medical alerts for personnel based on patient medical data. The system will receive patient data both manually entered and automatically collected from vital signs monitors, analyze the data received, and provide recommendations for medical treatment. The prototype must enable the development of specific AI-based algorithms that are free of errors and minimize the risks of data fitting and dimensionality through scalable data subsets.

Read more here.

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Jackie Gilbert
Jackie Gilbert
Jackie Gilbert is a Content Analyst for FedHealthIT and Author of 'Anything but COVID-19' on the Daily Take Newsletter for G2Xchange Health and FedCiv.

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