Dynamic Document Matching for Healthcare Provider Data
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Solution Overview
Problem
Integrating disparate data sources for healthcare providers into a single data store is challenging due to heterogeneous data fields, lack of strong identifiers, and inconsistent updates, leading to errors and the impossibility of manual curation for over a million providers.
Innovation Solution
A system and method for dynamic document matching and merging using multiple matcher algorithms, statistical models, and provenance tracking to combine data from various sources while considering the trustworthiness of each field, utilizing Bayesian Identity Resolution and ElasticSearch for accurate data integration and ranking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual curation and inspection of provider data is performed, then data accuracy can be ensured, but it becomes infeasible when there are more than one million individual healthcare providers
Solution Approach 1:
The patent replaces manual mechanical review processes with automated computational systems including machine learning models, statistical algorithms, and heuristic-based matchers that can process over one million provider records automatically while maintaining data accuracy through algorithmic validation and confidence scoring
Solution Approach 2:
The system introduces intermediary automated processing layers including data normalization services, matching algorithms, and quality scoring mechanisms that act as intermediaries between raw data sources and the final curated dataset, enabling scalable processing without direct human intervention at scale
2Quantity of substance
If data from multiple disparate sources is integrated into a single data store, then comprehensive provider information can be assembled, but data inconsistencies and errors propagate through the system
Solution Approach 1:
The patent applies local quality by implementing source-specific validation rules, confidence scoring per data field, and provenance tracking that evaluates and weights data from different sources individually, allowing the system to maintain high reliability for each data element while assembling comprehensive information from multiple sources
Solution Approach 2:
The system creates a composite data structure that integrates information from multiple sources with different reliability characteristics, using weighted combinations of data sources, confidence scoring, and provenance metadata to produce a unified dataset that maintains overall consistency while incorporating comprehensive information
3Loss of time
If provider information is updated across multiple data sources, then data currency can be maintained, but without a central mechanism, data becomes out of sync among sources
Solution Approach 1:
The patent merges multiple distributed data sources into a unified data store with centralized coordination, allowing updates to be made in one location and automatically propagated to all relevant sources, maintaining data currency without requiring complex coordination between independent systems
Solution Approach 2:
The system implements feedback mechanisms including change detection, update propagation tracking, and data synchronization monitoring that automatically detect when provider information changes and trigger updates across all data sources, maintaining currency without manual intervention
4Measurement precision
If traditional exact matching methods are used for provider identification, then matching precision can be maintained, but matching completeness decreases due to name variations and errors
Solution Approach 1:
The patent implements dynamic matching that adapts between exact matching and fuzzy matching based on data quality indicators, confidence scores, and context, allowing the system to maintain precision when possible while achieving completeness through flexible matching when needed
Solution Approach 2:
The system changes matching parameters dynamically, adjusting the strictness of matching criteria based on data source reliability, field confidence scores, and contextual information, enabling the transition between exact and approximate matching to balance precision and completeness
Data Source
AI summary
A system and method for matching and merging documents from disparate data sources into a single data store for a particular entity are provided. The system and method may be particularly useful for a healthcare system to match and merge data from disparate data sources about a healthcare provider.