Case Study: University of Mississippi Medical Center identifies high‑risk 12‑month AMI patients with near‑double accuracy using Jvion's Cognitive Clinical Success Machine

Applying Eigen-enabled Cognitive Machine Technology to Stop Heart Attacks

The University of Mississippi Medical Center, a prominent academic medical center serving a high‑risk Mississippi population, faced an urgent problem: roughly 1 in 10 patients suffered an acute myocardial infarction (AMI) within 12 months of discharge. Seeking better predictive capability, the system’s CHIO engaged Jvion and deployed Jvion’s Acute Myocardial Infarction (AMI) clinical vectors powered by the Cognitive Clinical Success Machine to identify 12‑month AMI risk using the provider’s existing (and sometimes incomplete) data.

Jvion tuned the machine, validated outputs with the hospital team, and integrated predictions into the organization’s Epic EHR within months. The solution produced measurable gains: it performed almost two times better than stress tests and about 20% better than CT coronary angiograms at identifying AMI events in a low‑risk population, focused a target cohort representing 1.5% of patients (in which 75 out of 100 patients were at risk within 12 months), and has generated more than 50,000 daily predictions for over 10,000 patients—enabling the hospital to design targeted interventions based on Jvion’s insights.

University of Mississippi Medical Center

John Showalter

Chief Health Information Officer