Rational Range Test for Clinical Data Translation
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Solution Overview
Problem
The translation of clinical data into a useful and reliable repository is challenging due to varying data entry protocols and potential errors, making it difficult to provide accurate and consistent information for quality care management in healthcare systems transitioning to electronic medical records.
Innovation Solution
A rational range test is applied to transform incoming data into a consistent form by selecting a transformation rule from a library based on results relative to a rational range associated with the physiologic condition, ensuring accurate and reliable data storage in a fact repository.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data entry follows different protocols at different locations, then data collection coverage is improved, but data consistency and reliability deteriorate
Solution Approach 1:
The patent applies parameter changes by transforming data from various source formats into a standardized target format using transformation rules. The system modifies data parameters (format, structure, representation) to achieve consistency while maintaining the ability to accept diverse input protocols. This resolves the contradiction by changing the state of the data itself rather than constraining the input sources.
Solution Approach 2:
The patent introduces an intermediary transformation layer between data entry and the fact repository. This intermediary component (data transformation system with rational range tests) mediates between diverse input protocols and the required consistent output format, allowing adaptability at the input stage while ensuring reliability at the output stage.
2Ease of operation
If data entry protocols vary across organizations, then ease of data entry is improved, but manufacturing precision of data quality deteriorates
Solution Approach 1:
The system changes data parameters through automated transformation rules that convert varied input formats into standardized output formats. This allows easy data entry in various formats while ensuring high data quality in the repository through systematic parameter transformation and validation.
Solution Approach 2:
The rational range test provides feedback mechanisms to validate transformed data against expected ranges and formats. This feedback loop ensures that data quality standards are maintained by identifying and correcting anomalies in the transformed data before it is stored in the fact repository.
3Productivity
If raw data is stored without transformation, then productivity is improved, but measurement precision of data utility deteriorates
Solution Approach 1:
The patent applies preliminary action by performing data transformation and validation (rational range tests) before data is stored in the fact repository. This preliminary processing ensures high data utility while maintaining productivity by automating the transformation process and avoiding the need for later manual intervention.
Solution Approach 2:
The system automatically changes data parameters from raw input formats to standardized formats suitable for the fact repository. This automated parameter transformation maintains processing speed while significantly improving data utility by ensuring the stored data is in the correct format for analysis and decision-making.
Data Source
AI summary
A method for providing a data translation may include receiving an input string comprising a free text response indicative of a physiologic condition, and applying a selected transform to the input string to transform the input string into a translated value indicative of a value associated with the physiologic condition for storage in a fact repository. The selected transform may be selected from a library of potential transforms based on results of the selected transform relative to a rational range associated with the physiologic condition. A corresponding computer program product and apparatus are also provided.


