Data Abstraction System for Interoperable EMR Aggregation
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Solution Overview
Problem
The healthcare industry faces significant challenges in achieving interoperability between disparate electronic medical record (EMR) systems, leading to inefficiencies in data sharing and decision-making, particularly highlighted by the COVID-19 pandemic, where accurate data aggregation is hindered by the lack of interoperability and intentional data blocking by EMR vendors.
Innovation Solution
A data abstraction system that consolidates EMR records from various platforms, integrating with artificial intelligence and analytics, allowing for scalable and customizable data access without requiring changes to existing EMR systems, using Health Level-7 (HL7) and Fast Healthcare Interoperability Resources (FHIR) standards, and employing data provider connector modules for efficient data processing and storage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If data abstraction systems are implemented to consolidate EMR records, then data aggregation capability is improved, but system complexity increases
Solution Approach 1:
The patent implements an intermediary data abstraction layer between disparate EMR systems and the data aggregation platform. This mediator translates and standardizes data from multiple sources without requiring changes to the source EMR systems, thereby improving data aggregation capability while managing system complexity through controlled integration points.
Solution Approach 2:
The system is divided into modular components including data provider connector modules, data translators, and analytics engines. Each component handles specific tasks independently, allowing the system to aggregate data from multiple EMR sources while maintaining manageable complexity through clear separation of concerns.
2Productivity
If interoperability standards are enforced across EMR systems, then data sharing efficiency is improved, but implementation difficulty increases
Solution Approach 1:
Instead of requiring EMR systems to conform to interoperability standards, the patent inverts the approach by having the data abstraction layer adapt to each EMR system's unique data structure. This maintains data sharing efficiency while eliminating implementation difficulty by removing the burden of standardization from the source systems.
Solution Approach 2:
The system dynamically adjusts translation parameters and data mapping configurations based on the specific EMR vendor and version being accessed. This allows efficient data sharing across diverse systems while simplifying implementation, as each system is configured individually rather than forcing all systems to adopt a complex unified standard.
3Measurement precision
If comprehensive patient data is aggregated from multiple sources, then decision-making quality is improved, but data security risks increase
Solution Approach 1:
The patent extracts only the specific data elements needed for decision-making from comprehensive EMR records, rather than aggregating all available data. This improves decision-making quality by focusing on relevant information while reducing data security risks by minimizing the volume of sensitive data stored and transmitted in the aggregation platform.
Data Source
AI summary
Described are data abstraction systems, methods, and media for aggregating and abstracting data records from data providers, which are not substantially interoperable with each other. Features include data provider connector modules dynamically loaded, based on definitions stored on disk, that facilitate data mapping and individual matching.