Clinical Concept Extraction for Structured EHR Trial Screening
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Solution Overview
Problem
Conventional electronic health record (EHR) and electronic medical record (EMR) systems lack the ability to capture and store critical components of a patient's history, demographics, diagnosis, treatments, and genetic markers effectively, as they focus on billing operations and regulatory compliance, leading to isolated, unstructured data that is difficult to access and interpret, hindering optimal patient care.
Innovation Solution
A system utilizing machine learning and natural language processing to automatically process clinical documents, identify and extract key characteristics, and generate refined models through continuous training data, enabling structured data extraction and analysis across multiple sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional EHR/EMR systems focus on billing operations and regulatory compliance, then billing efficiency is improved, but data accessibility and structured information extraction deteriorate
Solution Approach 1:
The patent extracts structured clinical information from unstructured medical documents by implementing NLP-based entity recognition and information extraction systems. The system separates structured data ( diagnoses, treatments, outcomes) from unstructured source documents, enabling independent access and analysis without disrupting the original billing-focused EHR workflow.
Solution Approach 2:
The patent introduces an intermediary NLP processing layer between the unstructured medical documents and the EHR system. This intermediary system automatically extracts, structures, and standardizes clinical information using trained machine learning models, bridging the gap between unstructured source data and structured database requirements without manual intervention.
2Measurement precision
If human analysts manually review and enter data from medical documents, then data accuracy is improved, but processing time and operational cost increase
Solution Approach 1:
The patent implements self-service automated data extraction systems using machine learning models that independently process medical documents, extract clinical entities, and populate EHR fields without human analyst intervention. The system continuously learns from training data and improves its extraction accuracy autonomously, maintaining high data accuracy while eliminating manual processing time.
Solution Approach 2:
The patent replaces the mechanical manual review and data entry process with automated computational systems. NLP algorithms and machine learning models perform entity recognition, relationship extraction, and data validation tasks that were previously performed manually, dramatically reducing processing time while maintaining or improving data accuracy through consistent automated application of extraction rules.
3Measurement precision
If specialized knowledge is required to interpret and extract clinical data, then extraction accuracy is improved, but system complexity and training requirements increase
Solution Approach 1:
The patent creates a universal NLP processing platform that handles multiple types of medical documents (progress notes, discharge summaries, pathology reports, radiology reports) and extracts various clinical entities (diagnoses, treatments, medications, outcomes) through a single integrated system. The multi-functional architecture reduces complexity by consolidating specialized extraction capabilities into one standardized processing pipeline that can be applied across different document types and clinical domains.
Solution Approach 2:
The patent employs parameter-based machine learning models that can be trained and adjusted to handle different clinical specialties and document types. By changing model parameters, training data, and extraction rules rather than redesigning the entire system, the platform adapts to specialized clinical knowledge requirements while maintaining a consistent underlying architecture, thereby managing system complexity.
4Volume of stationary object
If unstructured documents are stored in accompanying systems, then data storage capacity is improved, but data retrieval and analysis capability deteriorate
Solution Approach 1:
The patent segments unstructured medical documents into structured components by extracting individual clinical entities (diagnoses, treatments, medications, outcomes, genetic markers) and organizing them into standardized data fields. This segmentation transforms monolithic unstructured documents into organized, queryable structured data elements that can be easily retrieved and analyzed while preserving the original document storage capacity.
Solution Approach 2:
The patent adds a structural dimension to stored medical data by creating indexed, tagged versions of clinical information alongside the original unstructured documents. The system maintains the volumetric storage of original documents while adding dimensional organization through extracted entities, relationships, and metadata that enable efficient retrieval and analysis without losing the original data format or capacity.
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