<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Epic on AI and Society Course</title><link>https://msucerl.org/cmse101/tags/epic/</link><description>Recent content in Epic on AI and Society Course</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Thu, 21 May 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://msucerl.org/cmse101/tags/epic/index.xml" rel="self" type="application/rss+xml"/><item><title>2.4 Sepsis Prediction &amp; Hospital Triaging Algorithms</title><link>https://msucerl.org/cmse101/use-cases/2-4-sepsis-prediction/</link><pubDate>Thu, 21 May 2026 00:00:00 +0000</pubDate><guid>https://msucerl.org/cmse101/use-cases/2-4-sepsis-prediction/</guid><description>&lt;h1 id="sepsis-prediction--hospital-triaging-algorithms"&gt;Sepsis Prediction &amp;amp; Hospital Triaging Algorithms&lt;/h1&gt;
&lt;h2 id="context--systems-architecture"&gt;Context &amp;amp; Systems Architecture&lt;/h2&gt;
&lt;p&gt;Sepsis is a life-threatening medical emergency caused by the body&amp;rsquo;s extreme response to an infection, accounting for roughly one in three hospital deaths in the United States. To catch signs of deterioration before overt clinical collapse, hundreds of hospitals across the country integrated automated, proprietary predictive models directly into their Electronic Health Record (EHR) ecosystems—most notably the Epic Sepsis Model (ESM) developed by Epic Systems. These tools run continuously in the background of active medical wards, computing an automated probability score of an individual patient developing sepsis and throwing real-time pop-up alerts to bedside nurses and physicians.&lt;/p&gt;</description></item></channel></rss>