Data Harmonization Schema Mapping for Clinical Trial Standardization
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Solution Overview
Problem
Existing data integration and standardization processes are challenging due to the variety of formats, structures, and terminologies in datasets, limiting data-driven analysis, especially in fields like medical research where large volumes of clinical trials are scattered across different formats and languages, requiring complex technical expertise and manual reshaping that is resource-intensive.
Innovation Solution
A data harmonization suite with a scalable, rule-based system and non-technical user interface that allows experts to define and execute rules, mapping datasets to standardized schemas and content values, facilitating bulk data cleaning and transformation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data integration and standardization processes are performed manually to handle variety of formats, structures, and terminologies, then data quality and consistency are improved, but resource consumption and time requirements increase significantly
Solution Approach 1:
The system enables data harmonization to execute automatically without continuous manual intervention. The processor-based system autonomously performs matching, mapping, and transformation operations on datasets according to predefined schemas and rules, allowing the process to serve itself once initialized.
Solution Approach 2:
A standardized target schema acts as an intermediary framework between diverse source datasets. The system introduces this intermediate representation layer that mediates the conversion of various data formats, structures, and terminologies into a unified standardized form, enabling automated processing while maintaining data quality.
2Manufacturing precision
If complex technical expertise is applied to reshape and standardize scattered clinical trials data, then data standardization accuracy is improved, but operational complexity and expertise requirements increase
Solution Approach 1:
The data harmonization process is segmented into distinct operational stages: receiving source datasets, matching to target schema, mapping data elements, and transforming data types. Each stage is handled by the processor executing specific instructions, breaking down the complex task into manageable segments that maintain accuracy without requiring continuous expert intervention.
Solution Approach 2:
The target schema serves as a universal framework that can accommodate multiple diverse source datasets with different formats, structures, and terminologies. The system applies the same standardized schema across various clinical trials data, enabling consistent standardization accuracy through a multi-functional approach that handles diverse data types uniformly.
3Stability of the object's composition
If manual reshaping processes are used to harmonize large volumes of clinical trials data, then data consistency is improved, but processing time and resource intensity increase
Solution Approach 1:
The patent replaces manual mechanical reshaping processes with an automated processor-based system. The processor executes instructions to automatically perform data matching, mapping, and transformation operations, substituting human manual labor with automated computational mechanisms that maintain data consistency while dramatically reducing processing time.
Solution Approach 2:
The system performs preliminary matching of source datasets to the target schema before actual transformation occurs. By pre-defining the mapping relationships and data type transformations in advance, the system prepares the harmonization framework beforehand, enabling faster processing while ensuring data consistency is maintained throughout the transformation process.
Data Source
AI summary
System and method for data cleaning and/or transformation according to certain embodiments. For example, a method includes: receiving a raw source dataset including one or more data types; matching the raw source dataset to a target schema corresponding to a domain, the target schema including one or more standardized variables; and transforming the one or more data types in the raw source dataset to the one or more standardized variables.