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        MedStar has announced that its maternal initiative, D.C. Safe Babies Safe Moms (SBSM), will begin using AI and machine learning to build a maternal and infant health safety surveillance system for hospitals to proactively identify health risks for both patients and their infants. The project is called AI/ML Learning to Reduce Maternal and Infant Health Disparities.
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        The surveillance system will use natural language processing and clinical informatics technology to monitor patient health records and analyze clinical patterns of care. It will also analyze the sentiment and tone of provider notes for additional context and insights.
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        The objective of this offering is to help care teams understand patient risks before they become emergencies and to address structural disparities in maternal care. The offering was launched as a part of the Clinton Global Initiative (CGI) Commitment to Action project and aims to complete a beta model of the surveillance system within the three-year commitment period.
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    <a href="https://www.prnewswire.com/news-releases/dc-safe-babies-safe-moms-launches-project-using-ai-to-improve-maternal-and-infant-health-outcomes-at-clinton-global-initiative-2023-meeting-301929212.html">
      Press release
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    <a href="https://vator.tv/news/2023-09-18-medstar-initative-launches-ai-solution-for-maternal-health">
      Vator News
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Telehealth

MedStar's D.C. Safe Babies Safe Moms initiative, known as SBSM, is implementing AI and machine learning to develop a surveillance system for maternal and infant health. The AI/ML Learning to Reduce Maternal and Infant Health Disparities project will utilize natural language processing and clinical informatics technology to monitor patient health records and analyze patterns of care. Additionally, sentiment analysis will be used to gain further insights from provider notes. The objective is to identify health risks before they become emergencies and address disparities in maternal care. This offering is part of the Clinton Global Initiative Commitment to Action project and aims to complete a beta model of the surveillance system within three years.

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