Automated Health Data Aggregation with Self-Service Correction
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Solution Overview
Problem
Current data aggregation methods for health-related data, such as EHRs and PROs, face challenges like missing information, inaccurate recording, and lack of standardization, leading to inefficiencies and high labor costs due to manual intervention, and are not scalable for large datasets, while also compromising patient privacy.
Innovation Solution
A system comprising a server and centralized database that processes member-specific data to identify missing or incorrect information, automatically corrects it, and attributes a value to contributors, using machine learning and blockchain technology to ensure data quality and privacy, enabling scalable and efficient data aggregation and utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual intervention is used to review and fill missing data, then data quality can be improved, but labor costs and time consumption increase significantly
Solution Approach 1:
The system enables automated self-service data completion by using machine learning models to automatically identify and fill missing data fields. The system processes EHR records, PRO records, and claims data autonomously without requiring manual reviewer intervention for each record, thereby maintaining data quality while eliminating labor-intensive manual operations.
Solution Approach 2:
The patent replaces the mechanical manual review process with an automated computer-based system that uses machine learning algorithms to review, analyze, and complete data records. This substitution transforms the manual mechanical process into an automated computational process, dramatically reducing time consumption while maintaining or improving data quality.
2Loss of information
If manual data review processes are implemented, then missing information can be identified, but scalability to large datasets is limited
Solution Approach 1:
The system implements a universal automated data processing platform that can handle multiple data types (EHR records, PRO records, claims data) and perform multiple functions (data validation, missing data identification, automated completion) through a single integrated system. This multi-functional approach enables the system to scale efficiently across large and diverse datasets without requiring separate manual processes for each data type.
Solution Approach 2:
The automated system performs self-service data completion by autonomously identifying missing information patterns and filling gaps using machine learning models trained on existing data. This self-service capability eliminates the need for proportional increases in manual reviewers as data volume grows, enabling linear or exponential scalability.
3Loss of information
If data is aggregated from multiple sources, then comprehensive health insights can be obtained, but data privacy and security risks increase
Solution Approach 1:
The system introduces an intermediary layer of automated processing and de-identification between data aggregation and analysis. Machine learning models process and anonymize data from multiple sources (EHR, PRO, claims) before aggregation, acting as a mediator that preserves data utility for comprehensive health insights while protecting patient privacy by removing or encrypting personally identifiable information.
4Productivity
If automated systems are implemented for data processing, then productivity increases, but system complexity increases
Solution Approach 1:
The automated system is segmented into distinct functional modules: data ingestion module, validation module, machine learning model module, data completion module, and output generation module. Each module performs a specific function independently, which manages system complexity by breaking down the overall complex process into manageable, testable, and maintainable components while maintaining high productivity through automated workflows.
Data Source
AI summary
A system and method are disclosed for the collection and aggregation of data from contributing members of a community, such as health-related, personal, genomic, medical, and other data of interest for individuals and populations. Contributors become members of a community upon creation of an account and providing of data or files. The data is received and processed, such as to analyze, structure, perform quality control, and curate the data. Value or shares in one or more community databases are computed and attributed to each contributing member. The data is controlled to avoid identification or personalization. Steps are taken to determine incompleteness and incorrectness of the data, and the data may be improved or completed automatically, based upon interaction with members, additional contributions of data, and so forth.