Claims Data Segmentation for Risk Assessment Precision
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Solution Overview
Problem
Health insurance companies face challenges in efficiently processing and analyzing large volumes of medical claims data to identify unaccounted patient diagnoses and unpaid claims, which hinders their ability to receive transfer payments and maintain high star ratings under the Patient Protection and Affordable Care Act (PPACA).
Innovation Solution
A computing system and method that automatically selects a subset of claims data, generates graphical user interfaces to display unaccounted diagnoses and unpaid claims, and optimizes computing resources, allowing health insurance company personnel to identify and address discrepancies, thereby improving transfer payments and star ratings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If insurers process the complete collection of claims and related data to accurately determine transfer payments and star ratings, then the accuracy and completeness of risk assessment is improved, but the processing time and computational resource consumption increase significantly
Solution Approach 1:
The patent segments the complete claims collection into multiple subsets based on different criteria (e.g., time periods, provider types, claim categories). This allows the system to process manageable portions of data separately while maintaining overall accuracy in determining transfer payments and star ratings, thus reducing processing time without sacrificing assessment precision.
Solution Approach 2:
The system performs preliminary processing and filtering of claims data before the main analysis. By pre-categorizing, pre-validating, and pre-aggregating claims into structured subsets, the system reduces the complexity of subsequent processing steps, enabling faster and more efficient risk assessment while maintaining accuracy.
2Loss of information
If insurers store and analyze the complete collection of claims data to ensure comprehensive risk evaluation, then the completeness of data analysis is improved, but the storage requirements and processing complexity increase
Solution Approach 1:
The patent divides the complete claims collection into multiple organized subsets based on relevant characteristics such as time periods, healthcare providers, and claim types. This segmentation maintains data completeness for accurate risk evaluation while reducing processing complexity by allowing targeted analysis of specific subsets rather than handling the entire dataset as a single complex unit.
Solution Approach 2:
The system introduces intermediary data structures and processing layers that mediate between the raw complete claims collection and the final analysis. These intermediaries (such as aggregated summary statistics, pre-processed subsets, and intermediate results) simplify the complexity of analyzing the complete dataset while preserving the necessary information for comprehensive risk evaluation.
3Measurement precision
If insurers manually review and select relevant claims from the complete collection to identify unaccounted diagnoses and unpaid claims, then the precision of claim selection is improved, but the labor requirements and processing time increase
Solution Approach 1:
The patent implements automated systems that perform the selection and identification of relevant claims without requiring manual human review. The system automatically processes claims subsets, identifies unaccounted diagnoses, detects unpaid claims, and generates necessary outputs, thereby maintaining high precision in claim selection while dramatically improving processing efficiency and eliminating manual labor requirements.
Data Source
AI summary
Various systems and methods are provided that graphically allow health insurance company personnel to identify patient diagnoses that are not accounted for by the health insurance company. Furthermore, the various systems and methods graphically allow health insurance company personnel to identify patients that have not submitted claims for documented ailments or conditions. Thus, the health insurance company may be able to improve its chances of receiving transfer payments from other health insurance companies and/or receiving higher star ratings.