Patent Application: Predicting ICU Patient Length of Stay

ChangeBridge: Patent Apps - Health Informatics (G16H)

Published September 14th, 2023

Detected March 26th, 2026

Summary

The USPTO has published a new patent application (US20260088160A1) detailing methods and systems for predicting a patient's length of stay in an intensive care unit (ICU). The application describes improved predictive models that analyze patient data to generate LOS predictions.

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What changed

This document is a published patent application from the USPTO (Application No. 19109855, Publication No. US20260088160A1) filed on September 14, 2023. It describes methods and systems for predicting a patient's length of stay (LOS) in an intensive care unit (ICU), aiming for improved performance over existing models. The described system involves obtaining patient records, extracting prediction features, analyzing them with a trained model, generating an LOS prediction, and presenting it via a user interface.

As this is a patent application, it does not impose direct regulatory obligations or compliance deadlines on healthcare providers. However, the underlying technology described could influence future healthcare operations, data analytics practices, and potentially inform the development of new clinical decision support tools. Compliance officers in healthcare technology or providers developing such systems should be aware of this patent filing as it pertains to intellectual property in health informatics.

Source document (simplified)

← USPTO Patent Applications

METHODS AND SYSTEMS FOR PREDICTING INTENSIVE CARE UNIT PATIENT LENGTH OF STAY

Application US20260088160A1 Kind: A1 Mar 26, 2026

Inventors

LOUIS NICOLAS ATALLAH, OMAR BADAWI, XINGGANG LIU, ROBIN FRENCH, PAMELA JAYNE AMELUNG JAYNE AMELUNG

Abstract

The present disclosure relates to methods and systems for predicting a length of stay (LOS) for a patient in an intensive care unit (ICU). As described herein, the methods and systems for generating a prediction of ICU LOS present improved performance over existing available predictive models. In certain embodiments, the methods described herein include: providing an ICU LOS prediction system; obtaining a plurality of records for a patient in an ICU covering at least a first time period; extracting a plurality of different defined ICU LOS prediction features for the patient; analyzing the extracted plurality of different defined ICU LOS prediction features using a trained developed ICU LOS prediction model; generating a prediction of ICU LOS for the patient based on the analysis; and presenting the generated prediction of ICU LOS for the patient via a user interface.