Data Conflict Resolution via Ordered Analysis Sequences

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Solution Overview

Problem

Healthcare data matching systems face challenges in resolving conflicts between data objects associated with a common entity, leading to inaccurate or confusing results due to differing demographic information across various source systems, which becomes exacerbated when data is shared across clinically integrated networks or galaxies.

Innovation Solution

An ordered sequence of data analysis processes is assigned to each data field of data objects associated with a common entity, with each process performing a different technique to resolve conflicts, ultimately determining a consensus value for the field, allowing for accurate demographic information identification and record generation.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If matching algorithms determine data objects are associated with a common patient, then data integration across source systems is achieved, but conflicting demographic information creates inaccurate patient records

Engineering Contradiction:

Improvedata integration capabilityVSAvoidpatient record accuracy

Core Design Contradiction:

Adaptability or versatilityVSManufacturing precision

Solution Approach 1:

The patent segments the conflict resolution process into multiple distinct data analysis techniques (e.g., plurality analysis, preferred record analysis, aggregate analysis) that are applied in an ordered sequence to different data fields. Each technique handles specific types of conflicts, allowing systematic resolution of demographic discrepancies across source systems while maintaining integration capabilities.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system changes parameters by applying different data analysis techniques based on the specific data field and conflict type. The ordered sequence allows the system to adapt the resolution approach for each field (e.g., using plurality analysis for some fields, preferred record analysis for others), thereby resolving conflicts while preserving data integration.

Inventive Principle:

Principle #35Parameter changes

2Manufacturing precision

If multiple data analysis techniques are applied to resolve conflicts, then data accuracy improves, but processing time and computational resources increase

Engineering Contradiction:

Improvedata accuracyVSAvoidprocessing time

Core Design Contradiction:

Manufacturing precisionVSLoss of time

Solution Approach 1:

The patent applies preliminary action by establishing a predetermined ordered sequence of data analysis techniques before conflict resolution begins. This pre-planned sequence allows the system to efficiently apply the most appropriate technique first, reducing the need to execute all techniques and thereby minimizing processing time while maintaining data accuracy.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system uses partial action by applying only the necessary number of data analysis techniques from the ordered sequence until conflicts are resolved. Not all techniques need to be executed for every data field - the process stops when sufficient accuracy is achieved, reducing computational overhead while maintaining precision.

Inventive Principle:

Principle #16Partial or excessive action

3Adaptability or versatility

If data is shared across clinically integrated networks, then data availability and collaboration improve, but conflict resolution becomes more complex and burdensome

Engineering Contradiction:

Improvedata sharing capabilityVSAvoidconflict resolution complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent implements universality by creating a standardized ordered sequence of data analysis techniques that can be applied across multiple source systems and clinically integrated networks. This universal approach handles conflicts consistently regardless of the number or type of source systems, simplifying conflict resolution while maintaining broad data sharing capabilities.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The ordered sequence of data analysis techniques acts as an intermediary mechanism between conflicting data objects from different source systems. This mediator systematically processes conflicts through predefined techniques, reducing the complexity of direct conflict resolution between multiple heterogeneous source systems across clinically integrated networks.

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS11133088B2Resolving conflicting data among data objects associated with a common entity

Publication Date: 2021.09.28 MERATIVE US LP

AI summary

Resolving conflicting data among data objects associated with a common entity includes assigning an ordered sequence of data analysis processes to a corresponding data field of a plurality of data objects associated with a common entity. At least two of the data objects include different values for the corresponding data field, and each data analysis process performs a different technique to resolve conflicts between different values of data. The ordered sequence of data analysis processes is executed to determine a consensus value to serve as a value for the corresponding data field of each of the plurality of data objects. The data analysis processes are successively executed in the ordered sequence until the consensus value is determined for the corresponding data field.