EHR Data Consolidation Using Recursive ML Mapping

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Solution Overview

Problem

The heterogeneity of electronic health data formats from various sources complicates the creation of comprehensive electronic health records, leading to inefficiencies and errors in healthcare data exchange and access.

Innovation Solution

A method and system that utilize machine learning to recursively map and translate heterogeneous data elements into a unified electronic health record database, using a message processing engine and natural language processing to generate standardized messages.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If heterogeneous data formats from various sources are used, then data diversity and comprehensiveness are improved, but data exchange complexity and error rates increase

Engineering Contradiction:

Improvedata diversityVSAvoiddata exchange complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent introduces an intermediary translation layer that converts heterogeneous data formats from various sources into a standardized internal format. This mediator component handles format variations without requiring changes to the core system, thus maintaining data diversity while reducing exchange complexity.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system dynamically adjusts data representation parameters by detecting the source format and applying appropriate transformation rules. This allows the system to accommodate diverse data formats by changing parameters such as data structure, encoding, and field mapping according to the specific source.

Inventive Principle:

Principle #35Parameter changes

2Measurement precision

If manual data mapping and translation methods are used, then data accuracy can be maintained, but processing time and resource consumption increase

Engineering Contradiction:

Improvedata accuracyVSAvoidprocessing time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system performs preliminary actions by pre-defining mapping rules and translation templates for common data formats. This preparation work is done in advance, allowing the system to quickly process incoming data by matching it against pre-configured patterns rather than creating mappings from scratch each time.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent implements feedback mechanisms where the system learns from successful data translations and automatically refines its mapping rules. This feedback loop improves accuracy over time while reducing the need for manual intervention, thereby decreasing processing time for future translations.

Inventive Principle:

Principle #23Feedback

3Quantity of substance

If comprehensive data collection from multiple sources is implemented, then electronic health record completeness is improved, but data integration difficulty increases

Engineering Contradiction:

Improvedata completenessVSAvoiddata integration difficulty

Core Design Contradiction:

Quantity of substanceVSDevice complexity

Solution Approach 1:

The patent segments the data integration process into distinct modular components: data ingestion, format detection, translation, validation, and storage. Each component handles a specific aspect of integration, making the overall complex process manageable and maintainable while enabling comprehensive data collection from multiple sources.

Inventive Principle:

Principle #1Segmentation

4Productivity

If standardized data formats are enforced at data source level, then data exchange efficiency is improved, but source system flexibility and adaptability decrease

Engineering Contradiction:

Improvedata exchange efficiencyVSAvoidsource system flexibility

Core Design Contradiction:

ProductivityVSAdaptability or versatility

Solution Approach 1:

The standardization function is placed in an intermediary translation layer rather than being enforced at the source system level. This mediator converts diverse source formats into standardized internal representations without requiring source systems to change, thus maintaining both efficiency and flexibility.

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS12547937B2Method and system for consolidating heterogeneous electronic health data

Publication Date: 2026.02.10 VERTO INC

AI summary

A method for consolidating heterogenous electronic health data involves obtaining a native message including a multitude of data elements and generating a markup message including the multitude of data elements in a hierarchical structure. The method further involves generating a standardized message that represents the multitude of data elements in a format of a unified electronic health record database by recursively applying a machine learning model to the multitude of data elements, based on the hierarchical structure to determine a mapping between the plurality of data elements in the markup message and the plurality of data elements in the standardized message. The method also involves writing the standardized message to the unified electronic health record database.