Healthcare Claims Value Estimation With Causal Risk Modeling
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Solution Overview
Problem
Existing predictive modeling techniques in healthcare fail to accurately handle the complexity and variability of healthcare data, leading to inaccuracies in risk stratification and cost predictions, and lack robust preprocessing and advanced machine learning techniques, limiting the ability to estimate financial impacts and policy changes effectively.
Innovation Solution
A novel value estimation method integrating advanced predictive modeling techniques, comprehensive data preprocessing, and rigorous causal inference approaches, including Propensity Score Matching, Difference-in-Difference analysis, supervised and unsupervised classification, and Instrumental Variables, with sophisticated machine learning algorithms to enhance accuracy and reliability of risk assessments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional actuarial methods are used for risk assessment, then the process is simple and easy to implement, but the accuracy of cost predictions and risk stratification is insufficient
Solution Approach 1:
The patent transforms raw healthcare data into standardized features through multiple preprocessing steps including handling missing values, outlier detection, and feature engineering. This transformation changes the parameter quality from raw heterogeneous data to cleaned standardized features suitable for machine learning models, thereby improving prediction accuracy while managing complexity through systematic processing
Solution Approach 2:
The patent combines multiple machine learning techniques (supervised learning, unsupervised learning, ensemble methods) with traditional actuarial approaches to create a hybrid predictive model. This composite approach integrates the simplicity and interpretability of traditional methods with the predictive power of advanced machine learning, achieving high accuracy without完全 abandoning established practices
2Reliability
If advanced machine learning techniques are integrated, then the accuracy of risk assessments is improved, but the complexity of the system increases
Solution Approach 1:
The patent divides the complex predictive modeling task into distinct modular components: data ingestion module, preprocessing module (with sub-steps for missing values, outliers, transformations), feature engineering module, model training module, and validation module. Each module handles a specific aspect of the pipeline, making the overall complex system manageable through clear segmentation and independent development of each component
Solution Approach 2:
The patent introduces intermediate processing layers between raw data and final predictions, including data validation intermediaries, feature transformation intermediaries, and model ensemble intermediaries. These intermediary components standardize and refine data at each stage, ensuring reliable input to subsequent processing steps while maintaining system reliability through controlled transformations
3Measurement precision
If comprehensive data preprocessing is applied, then the quality of predictive models is enhanced, but the processing time and computational resources increase
Solution Approach 1:
The patent performs data preprocessing actions in advance before model training, including handling missing values through imputation or removal, detecting and treating outliers, and engineering features from raw data. By completing these time-consuming preprocessing tasks beforehand, the system ensures high-quality input data for modeling while separating the preprocessing time from the critical model training and deployment timeline
Solution Approach 2:
The patent applies parameter transformations and standardizations to data in advance, converting heterogeneous raw data into standardized features with consistent formats and scales. This preliminary parameter transformation improves model quality by ensuring uniform data quality while allowing the transformed data to be reused across multiple modeling iterations without repeating the full preprocessing pipeline
4Measurement precision
If sophisticated machine learning algorithms are used, then the precision of cost prediction is improved, but the interpretability of results decreases
Solution Approach 1:
The patent implements feedback mechanisms that provide interpretable insights from machine learning models, including feature importance rankings, model performance metrics, and validation results that feedback into the modeling process. This feedback loop maintains interpretability by continuously monitoring and explaining model behavior, allowing stakeholders to understand how predictions are generated while still leveraging sophisticated algorithms for high precision
Data Source
AI summary
A value estimation method for a delegated healthcare model generating a user interface to receive patient-generated data from multiple sources, transforming this data based on predefined parameters, and training a predictive analytics model with this data. The model generates baseline ratings for risk, utilization, and causal inference. Using these baseline ratings, the method predicts distinct risk ratings, healthcare utilization ratings, and causal impact ratings. A value creation estimate, indicating per member per month cost adjustment and utilization adjustment, is then generated based on these predictions and predefined parameters. The delegated model is reconciled based on comparing the value creation estimate to the observed value for the same period of time.