USPTO Patent Grant: Decision Support Tools for Reducing AMI Patient Readmissions

ChangeBridge: Patent Grants - Health Informatics (G16H)

Published March 24th, 2026

Detected March 24th, 2026

Summary

The USPTO has granted a patent (US12586684B2) to Cerner Innovation, Inc. for decision support tools designed to reduce readmissions for patients with acute myocardial infarction (AMI). The patent covers systems and methods utilizing machine learning models to predict readmission risk and initiate intervening actions.

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What changed

The United States Patent and Trademark Office (USPTO) has issued patent US12586684B2, titled "Decision support tools for reducing readmissions of individuals with acute myocardial infarction." This patent, assigned to Cerner Innovation, Inc., covers systems and methods that employ machine learning models to predict the risk of readmission for AMI patients prior to discharge. The technology aims to identify patients at high risk using diagnostic data and then initiate interventions to mitigate this risk.

This patent grant is primarily informational and does not impose new regulatory obligations on healthcare providers or technology companies. However, it signifies a technological advancement in predictive analytics for patient care, potentially influencing future product development and adoption in health informatics. Compliance officers should note this as a development in AI-driven healthcare solutions, particularly concerning patient readmission reduction strategies.

Source document (simplified)

← USPTO Patent Grants

Decision support tools for reducing readmissions of individuals with acute myocardial infarction

Grant US12586684B2 Kind: B2 Mar 24, 2026

Assignee

Cerner Innovation, Inc.

Inventors

Rupanjali Chaudhuri, Vadim Khotilovich, Monica Gaur, Chetan KV, Will Zimmerman

Abstract

System, methods and computer storage media are disclosed for providing a decision support tool for reducing readmissions of AMI patients through early prediction of readmission. An AMI patient may be identified using a working diagnosis and/or an elevated troponin level. One or more machine learning models may be utilized to predict readmission at a time prior to discharge. Based on the prediction, an intervening action may be automatically initiated. Further embodiments include training machine learning model(s) to predict readmission of an AMI patient. In some embodiments, a first model may be trained using reference patient data as it existed at a predetermined time following the patient's admission (e.g., 12 hours after admission), and a second model may be trained using reference patient data as it existed at a later time (e.g., discharge). Readmission risk scores from each model may be combined to determine an overall risk for an AMI patient.