Patient Intake Data Resolution Using Confidence-Based Correlation
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Solution Overview
Problem
Healthcare systems face challenges in managing low-quality patient intake data due to manual errors, miscommunication, and discrepancies, leading to unintended operating behavior and inefficiencies that manual correction cannot scale effectively.
Innovation Solution
A computer-implemented method using machine learning algorithms to automatically resolve mapping errors in patient intake data by generating resolution candidate records through record correlator models, with confidence-based automation and manual review for ambiguous cases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual review and correction processes are used to fix errors in patient intake data, then data quality can be improved, but the process becomes time-intensive, expensive, and does not scale effectively
Solution Approach 1:
The system enables self-service by implementing automated self-correction mechanisms where the entity resolution model automatically identifies and corrects mapping errors in patient intake data without requiring manual intervention for each record, allowing the system to process and correct its own data errors at scale
Solution Approach 2:
The patent replaces the mechanical manual review process with an automated computational system. The entity resolution model uses machine learning algorithms to substitute human experts in detecting and resolving data mapping errors, transforming a labor-intensive manual process into an automated electronic system that can handle large volumes of data simultaneously
2Productivity
If automated processing is implemented to handle large volumes of patient intake data, then productivity increases, but errors and inconsistencies may go undetected without proper validation
Solution Approach 1:
The system implements feedback through the entity resolution model that continuously learns from validation results. The model processes patient intake data automatically, validates corrections against established data standards and patterns, and uses the feedback from validation outcomes to improve future resolution accuracy, creating a self-improving automated system
Solution Approach 2:
The patent applies preliminary action by pre-establishing validation rules, data standards, and resolution criteria before automated processing begins. The system prepares the entity resolution model with training data and predefined correction protocols in advance, enabling it to automatically detect and correct errors while maintaining data accuracy without requiring real-time human intervention
Data Source
AI summary
A computer-implemented method for automated data record resolution includes: receiving an unmapped data record comprising a plurality of data fields, wherein at least one data field of the plurality of data fields causes a mapping error between the unmapped data record and a plurality of validated data records; generating, from the plurality of validated records, a resolution candidate record for the unmapped data record based on detecting the mapping error between the unmapped data record and the plurality of validated data records; and automatically re-assigning one or more data records of the computer database that are digitally associated with the unmapped data record to the resolution candidate record.