Lab Measurement Unit Validation Using Reference Health Data
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Solution Overview
Problem
Lab test data often exhibits inconsistencies due to missing or mislabeled measurement units, leading to misdiagnoses, ineffective treatments, and misleading research outcomes, compromising patient safety and hindering medical advancements.
Innovation Solution
A data integrity maintenance system filters healthcare data based on common health-related attributes, generates a reference measurement range, and determines the correct measurement unit for lab test data that lack units or exhibit mislabeling errors, improving data quality and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated data processing is implemented without validation, then processing speed increases, but data accuracy deteriorates due to missing or mislabeled measurement units
Solution Approach 1:
The system performs preliminary validation of measurement units before main data processing occurs. By checking and correcting measurement units in advance (steps 304-308), the system prevents errors from propagating through the processing pipeline, thereby maintaining both high processing speed and data accuracy.
Solution Approach 2:
The patent introduces an intermediary validation layer between raw data input and processing operations. This intermediate step (steps 302-312) acts as a mediator that verifies measurement units against reference ranges and corrects errors without requiring manual intervention, thus preserving processing efficiency while improving accuracy.
2Measurement precision
If comprehensive data validation is performed on all healthcare data, then data accuracy improves, but computational resource consumption increases
Solution Approach 1:
The system applies validation selectively rather than uniformly across all data. It focuses computational resources on specific fields that require verification (steps 304-308), such as measurement units that lack values or contain errors, while skipping already-validated data. This localized approach maintains data accuracy while reducing overall computational overhead.
Solution Approach 2:
The patent changes the validation parameter from comprehensive checking of all data points to targeted checking based on error indicators. By monitoring parameters like missing measurement units or values outside reference ranges (steps 310-312), the system dynamically adjusts validation intensity, consuming computational resources only when anomalies are detected.
3Measurement precision
If manual verification of measurement units is performed, then data accuracy improves, but processing time increases
Solution Approach 1:
The system implements self-service validation where the data processing system automatically verifies and corrects its own measurement units without requiring manual intervention. The automated validation logic (steps 304-312) compares measurement units against reference ranges and corrects errors autonomously, eliminating time loss associated with manual verification while maintaining high accuracy.
4Reliability
If data filtering based on health-related attributes is implemented, then data relevance improves, but data processing complexity increases
Solution Approach 1:
The patent segments the data processing task into distinct phases: filtering data by health-related attributes (step 302), validating measurement units (steps 304-308), and processing corrected data (step 312). This segmentation allows each phase to be optimized independently, improving data relevance through targeted filtering while managing complexity through modular processing steps.
Data Source
AI summary
Systems and methods for maintaining data integrity in a computerized health analysis platform are disclosed. For instance, a method includes (i) filtering existing healthcare data by first determining or extracting a first subset of data of data sets, such that the first subset is focused on common health-related attribute(s), (ii) generating a reference measurement range from the extracted first subset, and (iii) determining, based on the reference measurement range, measurement unit for lab test data that lack measurement unit or exhibit mislabeling error. For instance, the first subset of data sets represents measurements of physiological parameter(s) of entities. For instance, the lab test data are different from the first subset or the existing healthcare data that is used to determine the first subset. After the measurement unit is determined, a data structure representing the measurement unit is generated and stored.