Clinical Concept Extraction for EHR Trial Eligibility Screening
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional electronic health record (EHR) and electronic medical record (EMR) systems struggle to capture and structure critical patient data, such as diagnoses, treatments, and genetic markers, due to their focus on billing operations and regulatory compliance, leading to data isolation and inaccessibility, and lack of robust data mining capabilities.
Innovation Solution
A system utilizing machine learning and natural language processing to automatically process clinical documents, extract key characteristics, and generate optimized models through continuous training data, enabling structured data extraction and integration into EHR/EMR systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional EHR/EMR systems focus on billing operations and regulatory compliance, then billing accuracy and compliance are improved, but data accessibility and structuring capability deteriorate
Solution Approach 1:
The system segments the EHR data processing into multiple components: unstructured text input, NLP processing layer, concept extraction module, structured data output, and integration with billing systems. This segmentation allows the system to maintain billing compliance while simultaneously improving data accessibility through automated structuring of clinical notes and documents.
Solution Approach 2:
The patent introduces an intermediary NLP processing layer between the unstructured clinical documents and the structured EHR database. This intermediary system automatically extracts clinical concepts, entities, and relationships from free-text notes, progress reports, and discharge summaries, converting them into structured data that improves accessibility without compromising billing accuracy.
2Measurement precision
If manual data entry by human analysts is used, then data accuracy is improved, but productivity and time efficiency deteriorate
Solution Approach 1:
The system implements self-service automation where the NLP engine autonomously processes clinical documents, extracts relevant information, and populates structured fields without human intervention. The machine learning models continuously learn from feedback and improve their extraction accuracy over time, maintaining high data accuracy while dramatically increasing processing speed and productivity.
Solution Approach 2:
The patent replaces the mechanical process of manual data entry by human analysts with an automated NLP-based information extraction system. The system uses natural language processing, entity recognition, and relationship extraction algorithms to automatically convert unstructured text into structured data, eliminating the need for manual typing while maintaining or improving data accuracy through consistent application of extraction rules.
3Quantity of substance
If unstructured documents are stored in accompanying systems, then data completeness is improved, but data integration and analysis capability deteriorate
Solution Approach 1:
The patent creates a universal structured data representation that serves multiple functions: it maintains complete clinical information from unstructured sources, enables efficient data retrieval and analysis, supports billing operations, facilitates clinical decision-making, and allows integration with various healthcare systems. The standardized concept schema and entity relationships provide a multi-functional framework that handles both data completeness and integration versatility simultaneously.
Data Source
AI summary
A method for determining whether a patient may be enrolled into a clinical trial includes the steps of examining the patient's medical record from an electronic health record system, deriving a plurality of first concepts from the medical record, normalizing each concept in the plurality of first concepts to produce, for each normalization, a normalized concept, comparing each normalized concept to a list of study criteria, to indicate if the normalized concept meets the criteria, and if each criteria is met, indicating that the patient is not ineligible for enrollment in the clinical trial