Clinical Data Schema Transformation With Consumability Scoring
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Solution Overview
Problem
Converting electronic health data between different standard data formats often results in lost information and incomplete records due to a lack of prescriptive data standards, leading to difficulties in data interpretation and usability across various entities.
Innovation Solution
A data transformation system that determines consumability scores for transformed data by calculating characteristics, weighting scores, and generating recommendations based on intended use, using machine learning models to ensure data quality and usability across different schemas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If electronic health data is converted between different standard data formats, then data can be made usable for different purposes, but information loss and incomplete records occur due to lack of prescriptive data standards
Solution Approach 1:
The system performs multiple transformation passes where the output of one transformation is fed back as input to subsequent transformations. This iterative feedback process allows the system to progressively refine the transformed data, recovering information that may have been lost or obscured in intermediate transformation steps, thereby reducing overall information loss while maintaining adaptability across different data standards
Solution Approach 2:
The system pre-processes source data to identify and preserve critical information elements before transformation occurs. By performing preliminary analysis and marking important data elements ahead of time, the system ensures that essential information is maintained through the transformation process, preventing information loss while enabling versatile data usage across different standards
2Adaptability or versatility
If data transformation is performed without prescriptive standards, then flexibility in data interpretation is achieved, but data quality and completeness deteriorate
Solution Approach 1:
The system implements quality assessment feedback loops that evaluate transformed data against expected quality criteria. This feedback mechanism identifies quality issues and triggers re-transformation or manual review, ensuring that data quality is maintained despite the flexibility needed to handle different interpretation standards
Solution Approach 2:
The system dynamically adjusts transformation parameters and mapping rules based on the specific data being transformed and the target usage requirements. By changing parameters adaptively rather than applying fixed rigid rules, the system maintains both interpretation flexibility and data quality, allowing optimal transformation outcomes for different scenarios
3Reliability
If manual intervention is used to correct translated records, then data completeness is improved, but processing time and complexity increase
Solution Approach 1:
The system implements automated self-correction mechanisms that identify and fix common data completeness issues without requiring manual intervention. The transformation engine automatically detects missing or incomplete records and applies corrective transformations, enabling the system to service itself and maintain data completeness while minimizing time loss
Solution Approach 2:
The system applies automated correction to all potentially problematic records, even those that may not ultimately require intervention. By performing partial automated corrections on a broad scope of records, the system reduces the overall burden of manual work, achieving acceptable data completeness with minimal manual time investment
Data Source
AI summary
Systems and methods determine consumability scores of data to be transformed from a source clinical data schema to a target clinical data schema. Determining the consumability score can include calculating values for characteristics of the source data set transformed from the clinical data schema, weighting the individual scores, and aggregating the weighted scores. The consumability score may indicate a predicted suitability of the transformed data for a target use. Further, the system can generate recommendations for using the target data set based on the consumability score. The determination can include predicting whether the transformation produces elements of a target data set are sufficient for the intended purposes of users of the target data set, whether the source data set includes sufficient information that can be mapped to the target clinical data schema, and whether the transformation captures the source data set in sufficient quantity and quality.