Clinical Risk Stratification via Multi-Source Data Synthesis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current risk stratification systems for population health management are ineffective due to their inability to accurately harness electronic health record data and claims experience, leading to inaccurate and imprecise risk assignment, which results in decreased quality of care, increased medical errors, and higher healthcare costs.
Innovation Solution
A system that uses machine learning algorithms to synthesize electronic medical records, health questionnaires, and claim histories to create a risk index, enabling administrators to categorize patients into distinct risk groups and forecast future healthcare spending, both individually and at a population level, by integrating additional data sources such as wellness information and demographic factors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional risk stratification systems are used, then population health management is provided, but risk assignment accuracy is poor due to inability to harness electronic health record data and claims experience
Solution Approach 1:
The patent combines multiple data sources including electronic health records, claims experience, and wellness information into a unified risk stratification model. This merging of previously separate data streams enables accurate risk assignment by leveraging the complementary strengths of each data source while maintaining manageable system complexity through integrated processing.
Solution Approach 2:
The risk stratification system is designed to process and analyze multiple types of health data (clinical, financial, behavioral) through a single unified platform. This multi-functional approach allows the system to harness diverse data sources for risk assessment without requiring separate specialized systems for each data type, thereby improving accuracy while controlling complexity.
2Measurement precision
If electronic health record data and claims experience are synthesized, then risk categorization accuracy improves, but system complexity increases
Solution Approach 1:
The patent segments the risk stratification process into distinct modular components: data ingestion from electronic health records, claims processing, wellness information integration, and risk calculation. This segmentation allows each component to be developed and maintained independently, improving risk categorization accuracy through specialized processing while managing overall system complexity through modular architecture.
Solution Approach 2:
The system employs intermediary processing layers that standardize and harmonize data from different sources (electronic health records, claims systems, wellness platforms) before integration. These intermediary components translate diverse data formats into a unified structure, enabling accurate risk categorization while shielding the core risk engine from the complexity of heterogeneous data sources.
3Measurement precision
If machine learning algorithms are used to synthesize multiple data sources, then forecasting accuracy improves, but computational requirements and complexity increase
Solution Approach 1:
The patent applies machine learning algorithms selectively to the most predictive features and data elements rather than processing all available data with complex models. This partial application of advanced algorithms achieves high forecasting accuracy for critical risk factors while avoiding the computational overhead and complexity of applying sophisticated machine learning to all data types, thereby balancing accuracy with manageable algorithmic complexity.
Data Source
AI summary
Improved risk categorization is provided for clinical decision support and forecasting future health care spend. A risk index is provided that improves on other risk stratification models by synthesizing electronic medical records and health questionnaires with an individual patient's claim histories. Machine learning algorithms catalogue patients into distinct group clusters, based on risk which may be associated with annual health care spending, thereby enabling administrators to forecast future health care spending on the individual and population level.