Lab Test Unit Validation Using Reference Measurement Ranges

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Solution Overview

Problem

Lab test data often lacks measurement units or exhibits mislabeling errors, 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 health-related attributes, generates a reference measurement range, and determines the correct measurement unit for lab test data that lack units or have mislabeling errors, improving data quality and accuracy.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If lab test data is processed without verifying measurement units, then data processing speed is improved, but data accuracy deteriorates leading to misdiagnoses and ineffective treatments

Engineering Contradiction:

Improvedata processing speedVSAvoiddata accuracy

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The system performs preliminary verification of measurement units by comparing incoming lab test data against reference measurement ranges derived from authorized healthcare data before processing. This advance checking prevents inaccurate data from propagating through the system, resolving the contradiction by ensuring data accuracy is established prior to main processing operations.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system introduces an intermediary reference measurement range that acts as a mediator between incoming lab test data and the main processing system. This reference range, derived from authorized healthcare data, serves as a validation layer that checks measurement unit accuracy without requiring the main system to perform complex verification, thus maintaining processing speed while ensuring accuracy.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Measurement precision

If comprehensive data filtering and verification is performed on all healthcare data, then data accuracy is improved, but computational resource consumption increases

Engineering Contradiction:

Improvedata accuracyVSAvoidcomputational resource consumption

Core Design Contradiction:

Measurement precisionVSUse of energy by moving object

Solution Approach 1:

The system applies verification selectively rather than uniformly to all data. It focuses computational resources on verifying measurement units for lab test data that are being compared against reference measurement ranges, while relying on pre-established authorized healthcare data for the reference values. This localized verification approach maintains data accuracy while reducing overall computational burden.

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The system performs preliminary organization and validation of healthcare data into authorized and reference measurement ranges before they are needed for comparison. By pre-processing and structuring the reference data in advance, the system reduces the computational complexity of real-time verification operations, thus maintaining high data accuracy while lowering runtime resource consumption.

Inventive Principle:

Principle #10Preliminary action

3Adaptability or versatility

If measurement units are not standardized across different data sources, then data collection flexibility is improved, but data reliability deteriorates leading to misleading research outcomes

Engineering Contradiction:

Improvedata collection flexibilityVSAvoiddata reliability

Core Design Contradiction:

Adaptability or versatilityVSReliability

Solution Approach 1:

The system introduces a standardized reference measurement range as an intermediary layer between diverse data sources with different measurement units and the analysis system. Incoming lab test data with various units are verified against this standardized reference, enabling the system to maintain flexibility in data collection while ensuring all data meet consistent reliability standards before being used for research or clinical decisions.

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentEP4661023A1Systems and methods for maintaining data integrity in a health analysis platform by assessing and modifying physiological measurements based on filtered healthcare data

Publication Date: 2025.12.10 MEDIDATA SOLUTIONS INC

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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.