NLP Extraction of Clinical Data from XML EHRs
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Solution Overview
Problem
The healthcare industry's reliance on unstructured textual data poses challenges for IT systems, which are designed for structured data, leading to inefficiencies and inaccuracies in data ingestion and analysis, and increases the risk of data breaches due to the complexity and variability of electronic health records (EHRs).
Innovation Solution
A system and method using machine learning algorithms to extract features from XML-formatted EHRs, classify data elements, and generate configuration files to convert data into a tabular format, reducing the need for human intervention and enhancing data security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If unstructured textual data from healthcare industry is ingested by IT systems designed for structured data, then data volume and accessibility are improved, but data processing efficiency and accuracy deteriorate
Solution Approach 1:
The patent introduces an intermediary natural language processing (NLP) layer that sits between the unstructured healthcare text data and the structured IT system data warehouse. This NLP intermediary automatically extracts, classifies, and transforms unstructured clinical notes, physician documentation, and healthcare narratives into structured data elements that can be efficiently processed by existing IT systems, thereby maintaining high data volume ingestion while preserving processing efficiency
Solution Approach 2:
The system transforms the structural parameters of healthcare data from unstructured text format to structured tabular format through automated NLP processing. This parameter change enables the data to be compatible with IT system requirements while maintaining the richness and volume of the original unstructured data sources
2Quantity of substance
If multiple standardization codes and formats are used in EHR data, then data comprehensiveness and coverage are improved, but data consistency and accuracy deteriorate
Solution Approach 1:
The patent implements a universal NLP processing framework that can handle multiple healthcare data standardization codes and formats (such as ICD, CPT, HCPCS, and various clinical documentation standards) through a single unified system. This multi-functional approach maintains data comprehensiveness by supporting diverse codes while ensuring data accuracy through consistent processing rules and validation mechanisms applied uniformly across all code types
Solution Approach 2:
The system incorporates feedback mechanisms that validate extracted data against multiple standardization codes and formats, checking for consistency and accuracy. The NLP process continuously refines its extraction and classification based on feedback from validation checks, ensuring that data comprehensiveness is maintained while accuracy is verified through multiple layers of code-specific validation
3Measurement precision
If manual processing and human intervention are used for data extraction, then data accuracy is improved, but processing time and operational complexity increase
Solution Approach 1:
The patent implements self-service automated NLP extraction systems that perform data extraction, classification, and validation without requiring manual human intervention. The system serves itself by automatically learning from training data, adapting to different healthcare documentation styles, and continuously improving its extraction accuracy through machine learning algorithms, thereby eliminating time-consuming manual processing while maintaining high data accuracy
Solution Approach 2:
The system replaces the mechanical manual processing system with an automated electronic NLP processing system. This substitution uses computational algorithms and machine learning models to perform data extraction and classification tasks that were previously done manually, dramatically reducing processing time while maintaining or improving accuracy through consistent automated application of extraction rules and validation protocols
4Productivity
If automated NLP extraction is implemented, then processing efficiency is improved, but system complexity and development requirements increase
Solution Approach 1:
The patent segments the complex NLP extraction system into modular functional components, including separate modules for text preprocessing, entity recognition, relation extraction, classification, and validation. Each module performs a specific function and can be independently developed, tested, and maintained, thereby improving processing efficiency through specialized processing while managing system complexity through modular architecture that allows incremental implementation and easier troubleshooting
Data Source
AI summary
A system and method for extracting relevant data elements from a file for conversion to a tabular format includes a computing device receiving an XML format file having a loop with nested blocks. Each of the blocks has at least one data element. Features are extracted from each data element. These extracted features are processed using a machine learning algorithm to estimate a column header value for the data elements relative to a data schema. With the data element classified, a configuration file is generated to map the column header value to the data elements of the XML file. The configuration file is used to extract the data elements from the XML file to a tabular format. In the healthcare industry, the system and method may be used to extract relevant health information from a clinical document for conversion to a tabular format.