AI Patient Record Matching System with Iterative Feedback
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Solution Overview
Problem
Current patient record matching systems face challenges in accurately associating healthcare records with the correct patient, leading to risks of overmatching and undermatching, which can result in inappropriate clinical decisions and exposure of private health information.
Innovation Solution
An AI-driven record matching system that compares demographic information in patient records using specific rules and parameters to determine if records belong to the same patient, while iteratively training on feedback data to reduce overmatching and undermatching errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If patient records are matched using demographic information alone, then the matching process is simple and fast, but the accuracy is low leading to overmatching and undermatching errors
Solution Approach 1:
The matching process is divided into multiple sequential stages: initial demographic matching, household link analysis, and exclusionary data verification. Each stage processes specific types of information independently, allowing the system to maintain high accuracy while managing complexity through modular processing steps.
Solution Approach 2:
The system dynamically adjusts matching parameters based on the confidence level achieved at each stage. When demographic information alone is insufficient, the system transitions to incorporating household link parameters and exclusionary data parameters, effectively changing the parameter set being evaluated to improve accuracy.
2Reliability
If the system applies strict matching criteria to avoid overmatching, then false positives are reduced, but true matches are missed causing undermatching
Solution Approach 1:
The system incorporates feedback loops where matching results are continuously evaluated and used to adjust subsequent matching operations. Household link information and exclusionary data provide feedback that refines the matching criteria, allowing the system to maintain high reliability while improving accuracy through iterative learning.
Solution Approach 2:
The system performs preliminary analysis of household links and exclusionary data before finalizing match decisions. By pre-processing this additional information and using it to inform the matching algorithm in advance, the system can apply strict criteria without missing true matches, as the preliminary action has already identified potential true matches that warrant closer examination.
3Productivity
If demographic information is compared without additional verification, then the processing time is short, but the risk of incorrect clinical decisions increases
Solution Approach 1:
The verification process is segmented into priority levels: critical exclusionary checks are performed immediately on all matches, while less critical verifications are performed asynchronously. This segmentation allows the system to maintain high processing speed for urgent cases while ensuring reliability through comprehensive verification of all matches.
Solution Approach 2:
The system performs preliminary filtering of obviously incorrect matches using rapid demographic comparison, then applies more thorough verification only to borderline cases. This preliminary action approach maintains high processing speed by avoiding unnecessary detailed verification of clear-cut cases while ensuring reliability through focused verification of uncertain matches.
4Adaptability or versatility
If the system matches records from multiple providers and insurers, then comprehensive patient views are achieved, but the complexity of determining patient identity increases
Solution Approach 1:
The system implements a universal matching framework that can process demographic information, household links, and exclusionary data from any provider or insurer in a standardized manner. This multi-functional approach allows the system to handle diverse data sources without increasing complexity, as the same core algorithms are applied universally across all providers and insurers.
Solution Approach 2:
The system introduces household link information as an intermediary element that connects records from different providers and insurers. Rather than directly comparing diverse demographic formats from multiple sources, the system uses household links as a mediating structure that standardizes the comparison process, reducing complexity while maintaining multi-provider compatibility.
Data Source
AI summary
An AI record matching system includes processors that may compare patient records using one or more rules, criteria, or parameters and determine whether any of the patient records include different demographic information but include same medical information for a same person based on comparing the patient records using the one or more rules, criteria, or parameters. The processors may receive feedback data indicating an overmatching of the patient records to the same person or an undermatching of the patient records to the same person. The processors may be trained by modifying the one or more rules, criteria, or parameters based on the feedback data. The processors may iteratively repeat one or more of examining the patient records, determining whether any of the patient records include the different demographic information but the same medical information for the same person, receiving the feedback data, and training the one or more processors.