Machine Learning Discharge Prediction System
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Solution Overview
Problem
Care providers face challenges in identifying complex patients and forecasting patient outcomes to plan post-discharge care effectively, as existing methods fail to accurately predict discharge destinations and timing, leading to inefficiencies and increased costs due to unnecessary delays and resource mismanagement.
Innovation Solution
A computer-implemented system using machine learning models to cluster patients based on clinical and non-clinical data, predicting discharge destinations, timing, and resource needs by employing ensemble-based approaches that integrate various predictive models and optimization techniques to provide accurate forecasts and optimize care planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional discharge planning methods are used, then care providers can manage patient discharge processes, but prediction accuracy of discharge timing and destinations remains insufficient leading to delays and resource mismanagement
Solution Approach 1:
The patent replaces traditional manual discharge planning methods with an automated machine learning-based prediction system. The system uses trained models to automatically analyze patient data, predict discharge timing and destinations, and generate recommendations, substituting human judgment and manual processes with computational algorithms that provide more accurate and timely predictions.
Solution Approach 2:
The system performs discharge planning predictions in advance by analyzing patient data early in the hospital stay and continuously updating predictions as new data becomes available. This preliminary action allows care providers to prepare discharge arrangements, coordinate with post-acute facilities, and plan resource allocation before the actual discharge date, eliminating last-minute delays.
2Productivity
If manual discharge planning processes are used, then care providers can coordinate patient care, but resource management efficiency decreases due to lack of accurate forecasting
Solution Approach 1:
The system implements feedback mechanisms where prediction outcomes are continuously monitored and used to retrain and improve the machine learning models. The system analyzes actual discharge outcomes, compares them with predictions, and uses this feedback to refine prediction accuracy over time, creating a self-improving system that enhances resource management efficiency through increasingly accurate forecasting information.
Solution Approach 2:
The machine learning prediction system acts as an intermediary between patient data and care planning decisions. It processes raw clinical data, transforms it into actionable forecasting information about discharge timing and destinations, and provides this processed information to care providers, enabling more efficient resource management without losing critical forecasting details.
3Measurement precision
If complex machine learning models are deployed, then prediction accuracy improves, but system complexity and computational requirements increase
Solution Approach 1:
The patent segments the machine learning system into distinct functional components: data collection modules, data preprocessing modules, multiple specialized prediction models for different discharge outcomes, and result integration modules. This segmentation allows each component to be optimized independently, manages system complexity through modular architecture, and enables parallel processing of different prediction tasks simultaneously.
Solution Approach 2:
The system employs universal data preprocessing pipelines and feature engineering techniques that can be applied across multiple different prediction models and clinical scenarios. The core architecture is designed to handle various types of patient data and predict multiple discharge outcomes using a unified framework, reducing overall system complexity through reusable components while maintaining high prediction accuracy.
Data Source
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AI summary
Techniques are described for predicting information regarding expected time of occurrence of clinical events based on longitudinal patient data. According to an embodiment, a method can include clustering, by a system comprising a processor, training data samples corresponding to different patients that experienced a clinical event into different patient groups as a function of different defined timeframes within which the clinical event occurred. The method further comprises employing, by the system, a first machine learning process to train a classification model using the training data samples to predict the different patient groups to which the training data samples respectively belong, and employing, by the system, a second machine learning process to train a clinical time to event model using the training data samples to predict an expected duration of time until occurrence of the clinical event as a function of the different patient groups.