Patient Risk Stratification System Using Multi-Source Data Integration
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Solution Overview
Problem
Traditional patient diagnosis and treatment methods are limited in scope and timeliness, often relying on individual healthcare provider interpretations and failing to provide holistic views of patients, leading to inaccurate diagnoses and delayed treatments.
Innovation Solution
The development of novel techniques for obtaining, processing, and employing patient data to assist in diagnosis and treatment decision-making, integrating multiple healthcare data sources, including structured, unstructured, and semi-structured data, to generate patient-specific reports and treatment plans.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional patient diagnosis methods relying on individual healthcare provider interpretation are used, then the process is simple and quick, but the diagnostic accuracy and comprehensiveness deteriorate due to limited scope and lack of holistic patient view
Solution Approach 1:
The patent merges multiple data sources including structured clinical data, unstructured free text notes, imaging data, and genomic data into a unified diagnostic system. This integration allows comprehensive analysis of all patient information simultaneously, improving diagnostic accuracy while managing complexity through standardized data processing pipelines and AI/ML models that can handle diverse data types.
Solution Approach 2:
The patent introduces AI/ML models as intermediary components that process and interpret complex multi-source patient data. These models act as mediators between raw data and clinical decision-making, transforming unstructured and semi-structured data into actionable insights, thereby improving diagnostic accuracy without requiring direct human analysis of all data elements.
2Productivity
If traditional treatment assessment methods are used, then the process is straightforward, but the timeliness deteriorates with delays of months or years instead of real-time assessment
Solution Approach 1:
The patent implements continuous monitoring and real-time analysis of patient data through automated data collection from multiple sources and continuous processing by AI/ML models. This enables ongoing assessment of patient condition and treatment effectiveness without interruption, providing real-time diagnostic and treatment recommendations rather than periodic assessments, thereby eliminating time delays.
3Adaptability or versatility
If generic ETL and AI modeling techniques are used, then data processing capability is provided, but healthcare-specific requirements and regulatory compliance deteriorate due to lack of industry-specific design
Solution Approach 1:
The patent applies local quality by implementing healthcare-specific data processing components tailored to medical data characteristics and regulatory requirements. This includes specialized preprocessing pipelines for clinical data, structured free text notes with medical terminology recognition, and AI/ML models trained on healthcare datasets. These localized solutions address regulatory compliance and data quality requirements specific to healthcare while maintaining overall system integration.
Data Source
AI summary
Provided are techniques including receiving patient data; generating, based on the patient data, a patient risk stratification including: generating stratification scoring based on the patient data; and determining, based on the stratification scoring, a binary classification; generating, based on the patient data, a patient risk level assignment including: generating risk level scoring based on the stratification scoring and the binary classification; and determining, based on the risk level scoring, a risk category; generating a set of patient next best actions including: determining, based on the patient data, a patient outcome prediction; and generating, based on the predictions of patient outcomes, the set of patient next best actions; generating a patient disease state transition prediction including: determining, based on the patient data, a set of transition probabilities; generating, a patient unknown identification prediction including: determining, based on the patient data, a disease propensity score; and generating a corresponding patient diagnosis report.