Predictive Model for Heart Failure Population Management
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Solution Overview
Problem
Current machine learning models for heart failure management are limited by their small, systematically collected datasets and narrow clinical settings, making them non-generalizable to broad and heterogeneous populations, and lack clinically relevant, actionable results.
Innovation Solution
A system using a large retrospective dataset from electronic health records to develop a predictive model that integrates clinical variables and evidence-based care gaps, employing machine learning to provide clinically relevant treatment recommendations by estimating risk reduction and prioritizing patients for resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If machine learning models use small, systematically collected datasets from narrow clinical settings, then model training is feasible and validation is straightforward, but the models lack generalizability to broad and heterogeneous heart failure populations
Solution Approach 1:
The patent applies universality by developing a machine learning model that functions across diverse heart failure populations and clinical settings. The model is trained on large, heterogeneous real-world data from multiple sources including electronic health records, clinical trials, and registry data, enabling it to generalize across different patient demographics, disease severities, and treatment protocols rather than being limited to narrow clinical scenarios
2Measurement precision
If machine learning models focus on prediction accuracy within narrow clinical settings, then model performance is high for specific outcomes, but clinically relevant actionable results are not achieved
Solution Approach 1:
The patent implements feedback by creating a closed-loop system where the machine learning model not only predicts outcomes but also provides actionable recommendations that are fed back to clinicians. The model identifies specific care gaps, suggests evidence-based interventions, and tracks whether these recommendations are implemented and effective, continuously improving both prediction accuracy and clinical actionability through iterative feedback from real-world outcomes
Solution Approach 2:
The patent applies preliminary action by proactively identifying patients at risk and recommending interventions before adverse outcomes occur. The model analyzes current patient status, predicts future risks, and suggests preventive actions that clinicians can take in advance, such as closing care gaps or adjusting treatments, rather than merely describing past or current states
3Reliability
If healthcare resources are allocated without data-driven patient stratification, then resource distribution is simple and quick, but patient outcomes are not optimized and costs increase
Solution Approach 1:
The patent applies segmentation by dividing the heart failure population into distinct risk strata using the machine learning model. Patients are segmented into high-risk, medium-risk, and low-risk groups based on predicted outcomes and care gap profiles, enabling differentiated resource allocation where intensive interventions are targeted to high-risk patients while low-risk patients receive standard care, optimizing outcomes and reducing unnecessary costs
Data Source
AI summary
A method for providing treatment recommendations for a patient to a physician is disclosed. The method includes receiving health information associated with the patient, determining a first risk score for the patient based on the health information using a trained predictor model, determining a second risk score for the patient based on the health information and at least one artificially closed care gap included in the health information using the predictor model, determining a predicted risk reduction score based on the first risk score and the second risk score, determining a patient classification based on the predicted risk reduction score, and outputting a report based on at least one of the first risk score, the second risk score, or the predicted risk reduction score.