Patent Application: Predicting Care Coordination Data Using EHR Machine Learning

ChangeBridge: Patent Apps - Health Informatics (G16H)

Detected March 26th, 2026

Summary

The USPTO has published a patent application detailing a method for predicting care coordination data using EHR machine learning. The application, filed by Judith Payne and Jody Sheehan Garey, aims to improve scheduling and resource allocation by estimating patient acuity levels and care durations.

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What changed

This document is a published patent application (US20260088161A1) from the USPTO, detailing a novel method, apparatus, and computer program product for generating predicted care coordination data. The core innovation involves a machine learning-based predictive model trained on electronic health record (EHR) data to automatically estimate patient acuity levels, skilled care durations, and other care coordination parameters for scheduled appointments. This system is designed to dynamically update scheduling and resource allocation systems, thereby enhancing operational efficiency and reducing manual workload.

While this is a patent application and not a regulation, it signifies potential future technological advancements in healthcare operations. Compliance officers in healthcare technology and providers utilizing EHR systems should be aware of such innovations. The application's focus on data-driven decision-making and automated adjustments to appointment durations, staffing, and resource needs could influence future compliance requirements related to data privacy, system validation, and operational standards in healthcare settings. No immediate compliance actions are required, but awareness of this technological direction is prudent for long-term strategic planning.

Source document (simplified)

← USPTO Patent Applications

METHOD, APPARATUS, AND COMPUTER PROGRAM PRODUCT FOR GENERATING PREDICTED CARE COORDINATION DATA OBJECTS

Application US20260088161A1 Kind: A1 Mar 26, 2026

Inventors

Judith Payne, Jody Sheehan Garey

Abstract

A method, apparatus, and computer program product are provided for generating predicted care coordination data objects using a machine learning-based predictive model. The model is trained to analyze complex, high-dimensional electronic health record (EHR) data to automatically estimate patient acuity levels, skilled care durations, and other care coordination parameters for scheduled appointments. These predictions are used to dynamically and prospectively update scheduling and resource allocation systems, improving operational efficiency and reducing manual workload. Unlike conventional systems or human-based methods, the disclosed system continuously adapts to evolving clinical data and care patterns, enabling real-time, data-driven decision-making. The integration of the predictive model with scheduling and allocation systems allows for automated adjustments to appointment durations, staffing levels, and resource needs, thereby enhancing care delivery and staff productivity. The system provides a scalable approach to acuity estimation thereby improving scheduling and resource allocation systems.