Automated Data Mapping System for Healthcare IT Integration

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Solution Overview

Problem

The current manual process for data integration between healthcare IT applications is inefficient, requiring significant manual effort and being prone to errors, due to the diversity of HIT applications and heterogeneity of hospital information systems.

Innovation Solution

A method and system for automated data mapping and transformation using trained machine learning algorithms to extract, normalize, and transform data between source and target data schemas, with schema validation to identify mapping anomalies and provide feedback to users.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If manual data integration process is used, then experts can handle complex schema mapping, but the process requires significant time and manual effort

Engineering Contradiction:

Improvedata integration accuracyVSAvoidintegration time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system enables self-service automated schema mapping by extracting schema information directly from data sources and automatically generating mapping rules without requiring expert intervention for routine mapping tasks

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

Manual expert analysis and mapping creation is replaced by an automated system that uses machine learning algorithms to extract schema information and generate mapping rules automatically

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Manufacturing precision

If manual schema mapping is performed iteratively, then mapping accuracy can be improved, but the process requires repeated manual effort

Engineering Contradiction:

Improvemapping accuracyVSAvoidintegration speed

Core Design Contradiction:

Manufacturing precisionVSProductivity

Solution Approach 1:

The system performs preliminary automated schema extraction and mapping rule generation before manual review, preparing accurate mapping candidates in advance that can be quickly validated

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system provides automated feedback on mapping quality through validation algorithms that identify mapping anomalies, enabling rapid iteration without repeated manual effort

Inventive Principle:

Principle #23Feedback

3Adaptability or versatility

If extensive training of IT staff is provided, then data integration expertise is improved, but the training process takes several months

Engineering Contradiction:

Improvestaff expertiseVSAvoidtraining duration

Core Design Contradiction:

Adaptability or versatilityVSDuration of action of moving object

Solution Approach 1:

The system makes data integration capabilities self-service by automating schema mapping, allowing IT staff to perform integrations without extensive specialized training

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The automated mapping system provides universal functionality that handles diverse schema types and data sources, replacing the need for specialized expert knowledge with a general-purpose automated solution

Inventive Principle:

Principle #6Universality (Multi-functionality)

4Reliability

If deep experience from numerous data integrations is required, then integration quality is maintained, but the process becomes complex and time-consuming

Engineering Contradiction:

Improveintegration qualityVSAvoidprocess complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system performs preliminary automated schema extraction and mapping rule generation, preparing accurate mapping candidates that maintain integration quality without requiring experienced experts to manually analyze each integration case

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system replaces the mechanical process of expert knowledge application with automated machine learning algorithms that extract schema information and generate mapping rules consistently across different integration scenarios

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentEP4567620A1Methods and systems for automated data-integration

Publication Date: 2025.06.11 KONINKLIJKE PHILIPS NV

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AI summary

A method (300) for mapping data between a source data schema and a target data schema, comprising: extracting (320) and normalizing data to generate a normalized data set (462); analyzing (330), using a first trained machine learning algorithm (463), the normalized data set to identify a source data schema; deriving (340), using a second trained machine learning algorithm (464), mapping rules configured to transform data from the identified source data schema to the target schema; transforming (350), using the derived mapping rules, at least some data of the identified source data schema to data of the target schema; analyzing (360), using a schema validation algorithm (466), the transformed data to identify any mapping anomalies; and providing (370), to a user via a user interface, one or more of the identified source data schema, at least some of the data transformed to the target schema, and any identified mapping anomalies.