Injury Recovery Estimation System with Data Validation
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Solution Overview
Problem
Estimating injury recovery time is challenging due to the complexity of variables involved, with existing software tools lacking the capability to accurately identify and analyze necessary data and detect outliers, leading to errors and inconsistencies in insurance claim processing.
Innovation Solution
A method that extracts claimant medical data, including age, gender, and injury, correlates it with demographic data using programmed estimation rules, updates the data with medical treatment and prescription information, and provides the estimated recovery time via a graphical user interface, while identifying outliers to prevent fraudulent claims.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If prior software estimation tools are used to assist with estimating recovery time, then a graphical user interface is provided for data input and organization, but the tools fail to accurately identify and obtain necessary data and properly analyze the data to determine a reliable estimated recovery time
Solution Approach 1:
The patent introduces an intermediary processing layer between data input and recovery time estimation. This layer includes data validation modules that verify data quality, completeness checks that ensure all necessary fields are populated, and normalization processes that standardize data formats. These intermediary steps filter and prepare data before it reaches the estimation algorithm, resolving the contradiction by ensuring high measurement precision while maintaining ease of operation through automated data processing.
Solution Approach 2:
The patent replaces manual data analysis mechanisms with automated computational algorithms. Instead of relying on operators to manually identify and analyze recovery time data, the system uses programmed estimation algorithms that automatically process demographic data, injury data, and treatment data to generate reliable recovery time estimates. This substitution maintains user-friendly interfaces while dramatically improving measurement precision through consistent, error-free computational analysis.
2Measurement precision
If comprehensive medical data is collected and analyzed to improve estimation accuracy, then more variables are considered, but the complexity of identifying and obtaining correct data increases
Solution Approach 1:
The patent segments the comprehensive data collection process into distinct modular components: demographic data collection modules, injury data collection modules, treatment data collection modules, and outcome data collection modules. Each module handles specific data types independently with dedicated validation and processing logic. This segmentation reduces overall system complexity by breaking down the complex task of handling comprehensive medical data into manageable, specialized sub-tasks while maintaining high estimation accuracy through complete data coverage.
Solution Approach 2:
The patent implements parameter changes by dynamically adjusting data collection and processing based on claim-specific characteristics. The system identifies relevant parameters such as injury type, severity, claimant demographics, and treatment protocol, then automatically configures the data collection and analysis process to focus on the most relevant variables for each case. This adaptive approach maintains high measurement precision by considering comprehensive data while reducing device complexity by eliminating processing of irrelevant parameters.
3Measurement precision
If demographic medical data from multiple prior claimants is correlated using statistical methods, then estimation accuracy improves, but the time required to process and analyze large datasets increases
Solution Approach 1:
The patent applies preliminary action by pre-processing and pre-analyzing demographic medical data from the claims database before it is needed for specific recovery time estimations. The system performs preliminary statistical analysis on historical data to establish baseline relationships between demographic factors, injury types, and recovery outcomes. These pre-computed statistical models and correlation coefficients are stored and readily applied to new claims, maintaining high measurement precision through robust statistical analysis while dramatically reducing processing time by avoiding redundant calculations.
Solution Approach 2:
The patent uses copying by creating simplified representative models of complex statistical relationships. Instead of repeatedly analyzing entire datasets for each new claim, the system generates compact statistical models that capture the essential correlations from historical data. These copied models can be quickly applied to new cases, preserving the reliability of estimates based on comprehensive statistical analysis while eliminating the time cost of re-processing large datasets for each individual claim.
Data Source
AI summary
Disclosed technology includes extracting claimant medical data including a current claimant's age, gender, and at least one injury from an electronic claims document. Estimated injury recovery time data is determined by correlating demographic medical data comprising prior estimated injury recovery time data associated with different prior claimant's ages, genders, and injuries based on programmed estimation rules configured to identify statistical correspondence between different combinations of the ages, the genders, and the injuries in the demographic medical data and the claimant medical data comprising at least the current claimant's age, gender, and at least one injury. The determined estimated injury recovery time data is updated based on at least identified and obtained medical treatment data and prescription medication data associated with the current claimant's at least one injury. The updated estimated injury recovery time data is provided via a graphical user interface to a claim management device.