Predictive Health Risk Modeling via Population Segmentation
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Solution Overview
Problem
Predicting future health outcomes for individuals is challenging, particularly for serious diseases, acute incidents, and chronic disorders, which can lead to resource allocation volatility within organizations, affecting budgeting, staffing, and infrastructure.
Innovation Solution
A system and method for deriving risk patterns corresponding to medical conditions by analyzing historic treatment data of a population, using risk forecast models trained with claims data to predict the likelihood of medical conditions, and performing operations such as segmenting data, applying models, and prioritizing resource forecasts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional health outcome prediction methods are used, then individual health predictions can be made, but resource allocation volatility and prediction accuracy for serious diseases deteriorate
Solution Approach 1:
The patent segments the population into distinct risk groups based on predictive health risk scores derived from multiple data sources (claims data, demographic data, clinical data). This segmentation allows for more accurate predictions within each segment while stabilizing resource allocation by identifying specific high-risk groups that require targeted intervention rather than uniform resource distribution across the entire population.
2Measurement precision
If comprehensive data analysis is performed to improve prediction accuracy, then future health outcomes can be predicted more accurately, but system complexity and data processing requirements worsen
Solution Approach 1:
The patent introduces a risk scoring system as an intermediary that processes and synthesizes data from multiple sources (claims data, demographic data, clinical data). This risk score acts as a mediator that translates complex multi-source data into a standardized predictive metric, improving prediction accuracy while managing system complexity through a structured intermediary framework.
Solution Approach 2:
The predictive health risk assessment system is designed to be universal, accepting multiple types of input data (claims data from various payers, demographic data from different sources, clinical data from multiple providers) and producing standardized risk predictions. This multi-functional approach allows the system to handle diverse data sources through a unified framework, improving accuracy without proportionally increasing complexity.
3Adaptability or versatility
If detailed risk patterns are identified for different medical conditions, then targeted interventions can be planned, but data processing time and analysis complexity worsen
Solution Approach 1:
The patent performs preliminary risk assessment and pattern identification by analyzing historical claims data, demographic data, and clinical data to establish baseline risk scores and identify risk patterns before actual resource allocation decisions are made. This preliminary action allows targeted interventions to be pre-planned for identified high-risk groups, improving adaptability while reducing real-time data processing time when actual decisions need to be implemented.
Data Source
AI summary
In an illustrative embodiment, systems and methods for deriving risk patterns corresponding to a set of medical conditions through analyzing historic treatment of a population of individuals involve segmenting treatment data records of the population into a set of risk segments, analyzing the treatment data records to identify risk patterns indicative of each of a set of medical conditions, and calculating relative impact of each of the set of medical conditions based at least in part on prevalence of each medical condition among the population.