Demographic Data Processing with Fault Tolerant Human Intervention
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Solution Overview
Problem
The existing systems face challenges in accurately and efficiently processing and consolidating demographic information from various healthcare providers, which often contains inconsistent, mislabeled, or corrupt data, leading to difficulties in automated parsing and reimbursement processes.
Innovation Solution
A system utilizing machine learning algorithms to analyze and reformat demographic data, identifying correct field types based on semantic content, shape, and metadata, while generating a score for accuracy and allowing for human intervention to resolve faults, thereby creating a standardized and autonomous export entity file.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated systems parse demographic information from healthcare providers, then processing speed increases, but accuracy decreases due to inconsistent formatting and nomenclatures
Solution Approach 1:
The patent introduces an intermediary layer consisting of a machine learning model and rule-based system that mediates between the raw demographic data from healthcare providers and the standardized output requirements. This intermediary translates diverse formats and nomenclatures into consistent standardized fields, enabling both high processing speed through automation and high accuracy through intelligent translation and validation.
2Measurement precision
If human review of demographic data is performed manually, then data accuracy improves, but processing time and resource consumption increase
Solution Approach 1:
The system performs self-service by automatically detecting faults in demographic data and generating structured notifications that enable human reviewers to address issues efficiently. The machine learning model continuously learns from resolved faults to improve automated detection accuracy, reducing the burden on humans while maintaining high accuracy standards.
Solution Approach 2:
The patent implements feedback mechanisms where human reviewers provide corrections and resolutions to fault notifications, which are then fed back into the machine learning model to refine its detection algorithms. This continuous feedback loop ensures improving accuracy over time while reducing manual review requirements through better automated detection.
3Loss of substance
If tabular data is consolidated by combining rows, then data redundancy decreases, but information integrity is lost due to inability to account for variables in specific columns
Solution Approach 1:
The patent segments the demographic data into distinct fields and categories (e.g., provider information, practice information, contact information) while maintaining the ability to preserve variable-specific characteristics. This segmentation allows the system to identify and eliminate redundant data across rows while preserving the integrity of variable-specific columns through structured validation and type checking.
Data Source
AI summary
The present disclosure is directed to methods and non-transitory program storage devices for identifying demographic information in an input data file even in the face of known errors that would otherwise prevent the method from operating. When a fault condition is detected, a fault handler may attempt to fix the faulty data, remove the faulty data from the input data file being processed, or provide the faulty data at a user interface so a human user can intervene. The method and storage devices may continue processing the data regardless of whether human input has been received because the system can bypass or remove the data, thereby keeping the method fault tolerant.