Unsupervised Patient Risk Clustering for Targeted Health Interventions
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Solution Overview
Problem
Existing patient identification methods for higher health needs are biased, leading to underidentification of patients who would benefit from targeted healthcare interventions, resulting in poorer health outcomes and higher healthcare costs.
Innovation Solution
A computer-implemented method using unsupervised learning to categorize patients based on multiple risk scores, including healthcare utilization metrics, to accurately identify patients likely to benefit from targeted outreach and interventions, reducing bias and improving health outcomes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional biased methods are used to identify patients with higher health needs, then the identification process is simpler and faster, but the accuracy and fairness of patient identification deteriorates, leading to underidentification of patients who would benefit from targeted interventions
Solution Approach 1:
The patent segments the patient population into distinct risk categories (low risk, medium risk, high risk, very high risk) based on multiple healthcare utilization metrics. This segmentation approach allows for more precise identification of patients with higher health needs by dividing the heterogeneous patient population into homogeneous subgroups, thereby improving measurement precision without requiring overly complex individualized assessments for each patient.
Solution Approach 2:
The categorization model serves multiple functions simultaneously: it identifies patients with higher health needs, stratifies risk levels, and guides resource allocation. By creating a universal framework that can be applied across diverse patient populations and healthcare settings, the system achieves high accuracy without proportionally increasing complexity, as the same model structure handles various identification tasks.
2Loss of energy
If targeted healthcare interventions are provided only to identified high-risk patients, then healthcare costs are reduced for low-risk patients, but the risk of missing undetected high-need patients increases
Solution Approach 1:
The patent applies different levels of care and intervention intensity to different risk categories. Low-risk patients receive minimal intervention, while high-risk and very high-risk patients receive intensive care management. This local quality approach ensures that resources are allocated efficiently to where they are most needed, reducing overall healthcare costs while maintaining high reliability through differentiated care strategies tailored to each risk level.
Solution Approach 2:
The system performs preliminary risk stratification and categorization before implementing targeted interventions. By pre-identifying and categorizing patients into risk groups using the unsupervised learning model, the system ensures that high-need patients are detected and flagged for intensive care before critical events occur, thereby maintaining reliability while enabling cost-efficient resource allocation.
3Adaptability or versatility
If unsupervised learning models are used to categorize patients, then bias in patient identification is reduced and accuracy improves, but the computational complexity and data processing requirements increase
Solution Approach 1:
The patent transforms complex patient data into standardized risk scores and categorical variables that can be processed by the unsupervised learning model. By changing the parameters from raw healthcare utilization data to normalized risk metrics, the system achieves unbiased and versatile patient categorization while managing computational complexity through dimensionality reduction and feature engineering.
Solution Approach 2:
The unsupervised learning model automatically categorizes patients without requiring manual labeling or expert intervention for each patient. The model self-adjusts and learns patterns from the data, reducing the need for complex manual assessment protocols while achieving high adaptability and unbiased categorization across diverse patient populations.
Data Source
AI summary
Various examples of techniques for assessing patient risk and patient impact for health interventions are disclosed. In one example, a computer implemented method is disclosed that includes obtaining from a patient database patient information for a plurality of patients, the patient information including at least a risk score, clustering by a processor the plurality of patients based on the patient information using a categorization model, where the categorization model is trained using unsupervised learning, and assigning by the processor a risk category to each patient based on the clustering.