Patient Intake Data Resolution Using Confidence-Based Entity Mapping
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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 inefficient and costly manual corrections that fail to scale with large datasets.
Innovation Solution
A computer-implemented method using machine learning algorithms to automatically resolve mapping errors in patient intake data by generating resolution candidates based on confidence scores, dynamically selecting models, and providing a graphical interface for user control.
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 accuracy is improved, but the process becomes time-intensive and expensive
Solution Approach 1:
The system performs self-correction of data mapping errors through automated machine learning models that identify and resolve unmapped entities without human intervention. The entity resolution service automatically correlates unmapped data with validated records and applies corrections, enabling the system to fix its own data quality issues.
Solution Approach 2:
The patent replaces manual mechanical review processes with automated machine learning-based entity resolution systems. The ML models compute correlations between unmapped entities and validated records, automatically resolving mapping errors that previously required human expert review.
2Reliability
If manual correction processes are used for patient intake data, then data quality is improved, but the process fails to scale with large datasets
Solution Approach 1:
The automated entity resolution service enables the system to handle large volumes of data quality issues independently. The machine learning models process unmapped entities at scale by automatically computing correlations against validated records, eliminating the bottleneck of manual review and enabling scalability.
Solution Approach 2:
The patent substitutes manual correction mechanisms with automated machine learning-based entity resolution that can process large datasets efficiently. The system scales by leveraging computational algorithms that can evaluate numerous unmapped entities against validated records simultaneously, rather than requiring sequential human review.
3Productivity
If automated machine learning resolution is implemented, then processing speed and scalability are improved, but system complexity increases
Solution Approach 1:
The patent introduces an intermediary entity resolution service that sits between data intake and downstream processing systems. This service layer handles the complexity of machine learning model selection, configuration, and execution, shielding end users from underlying system complexity while delivering automated resolution capabilities.
Solution Approach 2:
The system segments the automated resolution process into distinct components: entity resolution service, machine learning model repository, correlation computation engine, and validation mechanisms. This modular architecture manages complexity by organizing functions into separate, independently configurable units.
4Ease of operation
If confidence-based automated resolution is used, then manual effort is reduced, but the risk of incorrect automated corrections increases
Solution Approach 1:
The system implements feedback mechanisms where resolution confidence scores are computed and evaluated against thresholds. High-confidence resolutions are applied automatically, while lower-confidence cases are flagged for review. The system learns from outcomes to improve future resolution accuracy, creating a feedback loop that enhances reliability.
Solution Approach 2:
The patent applies partial automation based on confidence levels. Instead of fully automating all resolutions, the system selectively applies automated corrections only when confidence thresholds are met, while preserving manual review for uncertain cases. This balanced approach reduces manual effort for clear cases while maintaining accuracy for ambiguous ones.
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.