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

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.

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