Automated Clinical Data Structuring with Quality Feedback
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Solution Overview
Problem
Current electronic health record systems lack robust structured data fields, leading to inaccessible and unstructured raw clinical data, which hinders the ability to provide precision medicine care due to limitations in data accessibility and quality assurance, especially with conflicting information across different health systems and data structures.
Innovation Solution
A computer-implemented method and system for automated quality assurance testing that receives unstructured patient data, processes it to generate structured records through schema and concept mapping, validates the data for errors or incomplete information, and displays indications for user revision, enabling improved data integrity and accuracy across various data sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If automated structuring through natural language processing is used, then data accessibility is improved, but measurement precision deteriorates due to errors and incomplete information
Solution Approach 1:
The system implements automated quality assurance testing that provides feedback on data quality metrics, identifying errors and incomplete information in structured clinical data. This feedback mechanism allows continuous improvement of data accuracy while maintaining automated processing capabilities.
Solution Approach 2:
The system performs preliminary quality assurance testing and validation checks before data is fully processed and stored. By conducting quality assessments in advance, the system prevents propagation of errors through the data pipeline while maintaining automated structuring benefits.
2Measurement precision
If manual structuring of clinical data is performed, then measurement precision is improved, but productivity deteriorates due to physician burden
Solution Approach 1:
The system enables self-service automated structuring of clinical data through natural language processing, eliminating the need for clinicians to manually structure hundreds of data elements. The automated system performs data extraction and structuring independently, maintaining high accuracy without physician intervention.
Solution Approach 2:
The system replaces manual mechanical structuring processes with automated computational methods. Natural language processing algorithms automatically extract and structure clinical data from unstructured text, substituting human manual effort with automated intelligence while maintaining or improving data accuracy.
3Adaptability or versatility
If different data structures are used across health systems, then adaptability is improved, but manufacturing precision deteriorates due to conflicting information
Solution Approach 1:
The system implements universal quality assurance testing that can evaluate data across multiple different data structures and formats. The testing framework is designed to work with various health system data schemas while maintaining consistent quality standards, enabling multi-functionality across diverse systems.
Solution Approach 2:
The system changes testing parameters and validation rules based on the specific data structure being evaluated. By adapting quality assurance parameters to match different data formats and schemas, the system maintains manufacturing precision across diverse health information systems with varying structures.
Data Source
AI summary
A method includes receiving unstructured data; processing the unstructured data to generate corresponding structured records; validating the structured patient records using quality tests; causing errors incomplete information to be displayed; and receiving a revision to the unstructured patient data. A computing system includes a processor; and a memory having stored thereon instructions that, when executed by processor, cause the computing system to: receive unstructured data; process the unstructured data to generate corresponding structured records; validate the structured patient records using quality tests; cause errors incomplete information to be displayed; and receive a revision to the unstructured patient data. A computer-readable medium includes instructions that, when executed by a processor, cause a computer to: receive unstructured data; process the unstructured data to generate corresponding structured records; validate the structured patient records using quality tests; cause errors incomplete information to be displayed; and receive a revision to the unstructured patient data.