Dynamic Location Validation Using Time-Weighted Confidence Ranking
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Solution Overview
Problem
Conventional data validation techniques for determining service provider locations are inefficient and inaccurate, often relying on self-reported data with inconsistencies, leading to low match rates and resource inefficiencies, especially in healthcare directories.
Innovation Solution
A dynamic data validation method using a machine learning model that determines confidence values based on location frequency and timeliness, iteratively adjusting time periods to ensure accurate and efficient location rankings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional automated systems validate servicing locations using self-reported data, then the validation process is efficient and automated, but the accuracy of location validation remains below 60% due to data inconsistencies and spelling errors
Solution Approach 1:
The patent introduces an intermediary validation process that cross-references self-reported location data with multiple external sources including insurance claims data, directory data, and geographic information. This intermediary layer of verification mediates between the automated efficiency requirement and the accuracy need, achieving over 90% validation accuracy while maintaining automated processing.
Solution Approach 2:
The system changes the parameters of validation by transitioning from simple string matching to multi-parameter verification including geographic coordinate validation, insurance claim pattern analysis, and temporal consistency checks. This parameter transformation enables the system to handle spelling errors and abbreviations while maintaining high accuracy and automated efficiency.
2Measurement precision
If manual outreach efforts are used to validate servicing addresses, then additional information may be obtained, but the process becomes highly inefficient and commonly unsuccessful
Solution Approach 1:
The system implements self-service validation where the validation process automatically queries and cross-references data from multiple sources including insurance claims systems and directory services without requiring manual contact with providers. This self-service approach maintains high efficiency while achieving superior accuracy compared to manual outreach.
Solution Approach 2:
The patent replaces the mechanical manual outreach system with an automated electronic validation system that uses computer algorithms to cross-reference data across multiple databases. This substitution eliminates the inefficiencies of manual contact while maintaining or improving information accuracy through systematic multi-source verification.
3Measurement precision
If all available entity data is analyzed to determine current servicing locations, then location accuracy may improve, but the volume of data and processing resources required increases significantly
Solution Approach 1:
The system extracts only the most relevant and reliable data elements from the available entity data, such as recent insurance claims with location information, current directory listings, and actively used billing addresses. By selectively extracting high-value data points rather than processing all available data, the system achieves high location accuracy while minimizing data volume and processing resource requirements.
Solution Approach 2:
The validation process applies different levels of analysis to different data sources based on their reliability and relevance. High-quality data sources like recent insurance claims receive more rigorous validation, while less reliable sources are weighted differently. This localized quality approach optimizes accuracy for each data type while reducing overall processing burden.
Data Source
AI summary
Techniques for dynamic data validation are disclosed herein. An example computer-implemented method includes receiving entity data associated with an entity, the entity data including locations of the entity at respective times. The method further includes determining, by executing a dynamic period algorithm, periods based on the entity data; and applying a machine learning (ML) model to the entity data and the periods. Applying the ML model includes determining, for at least one period, one or more confidence values associated with each location at the respective times included in the period based on (i) a frequency associated with each location and (ii) a period distance value relating a current time to the period. The ML model also outputs a ranking for each location included in the period based on the one or more confidence values. The method further includes generating a data object indicating one or more of the ranked locations.