Clinical Language Understanding Engine for Structured Data Extraction
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Solution Overview
Problem
Current electronic medical record systems require clinicians to enter structured data manually, which can be time-consuming and restrictive, especially for those who prefer to dictate notes verbally, as they need to conform to standardized terms and formats, limiting the ability to provide free-form clinical documentation efficiently.
Innovation Solution
A method and apparatus for automatically extracting clinical facts from a clinician's free-form narration using a clinical language understanding (CLU) engine, which processes textual or audio inputs to identify discrete clinical data items, maintaining linkages to the original text, and providing indicators for user review, allowing for efficient conversion into structured electronic medical records.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If clinicians manually enter structured data into electronic medical record systems, then data accuracy and standardization are improved, but time consumption and operational complexity increase
Solution Approach 1:
The system performs preliminary processing of free-form clinical narratives by segmenting text, identifying entities, and extracting structured data elements before final record creation. This preliminary structuring reduces the time required for manual data entry while maintaining accuracy standards.
Solution Approach 2:
The patent introduces an intermediary natural language processing layer between free-form clinical documentation and structured electronic medical records. This intermediary system automatically extracts entities, relationships, and clinical facts from unstructured text, converting them into standardized formats without requiring manual re-entry by clinicians.
2Ease of operation
If clinicians use free-form verbal dictation for documentation, then ease of operation and productivity are improved, but data structuring and extraction complexity increase
Solution Approach 1:
The system segments free-form clinical narratives into discrete units such as sentences, phrases, and individual clinical entities. This segmentation allows the complex processing task to be broken down into manageable steps including entity recognition, relationship identification, and structured data assembly.
Solution Approach 2:
The patent transforms unstructured text parameters into structured data parameters through automated processing. The system changes the state of clinical documentation from free-form narrative to standardized structured formats by applying natural language understanding algorithms and clinical knowledge bases.
3Stability of the object's composition
If manual data entry is required for electronic medical records, then data standardization is improved, but productivity and efficiency deteriorate
Solution Approach 1:
The system enables self-service automated extraction of structured data from free-form clinical narratives. The natural language processing system independently identifies and extracts clinical entities, relationships, and facts without requiring manual intervention, thereby maintaining standardization while improving productivity.
Data Source
AI summary
An original text that is a representation of a narration of a patient encounter provided by a clinician may be received and re-formatted to produce a formatted text. One or more clinical facts may be extracted from the formatted text. A first fact of the clinical facts may be extracted from a first portion of the formatted text, and the first portion of the formatted text may be a formatted version of a first portion of the original text. A linkage may be maintained between the first fact and the first portion of the original text.