Clinical Language Understanding Engine for Free-Form Note Parsing
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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 in free-form narratives, limiting the efficiency of clinical documentation and data extraction.
Innovation Solution
A method and system that automatically extracts clinical facts from free-form narrations provided by clinicians, using a clinical language understanding engine to parse and normalize the text, allowing for the maintenance of linkages between extracted facts and their original text portions, enabling efficient data entry and documentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If clinicians manually enter structured data into electronic medical record systems, then data structure and completeness are improved, but time consumption and documentation burden increase
Solution Approach 1:
The patent replaces the mechanical process of manual data entry with an automated natural language processing system. The clinical language understanding engine automatically parses free-form narrations and extracts structured clinical facts, substituting the manual mechanical typing process with intelligent automated text analysis, thereby resolving the contradiction between data structure quality and time consumption
Solution Approach 2:
The system enables the narration text to serve itself by automatically extracting and structuring clinical facts without requiring separate manual data entry. The free-form narration contains all necessary information, and the system self-extracts the structured data needed for the electronic medical record, eliminating redundant manual work while maintaining data completeness
2Ease of operation
If clinicians use free-form narration for documentation, then documentation efficiency and ease of use are improved, but data extraction and structuring become difficult
Solution Approach 1:
The patent introduces a clinical language understanding engine as an intermediary between the free-form narration and the structured data requirements. This intermediary component parses the natural language, identifies clinical entities and relationships, and transforms the unstructured text into structured clinical facts, thereby bridging the gap between easy free-form documentation and the need for structured data extraction
3Productivity
If free-form narrations are used instead of structured data entry, then clinician workflow speed is improved, but data quality and specificity may deteriorate
Solution Approach 1:
The system implements feedback by allowing clinicians to review and correct the automatically extracted clinical facts before finalizing the electronic medical record. The clinical language understanding engine processes the free-form narration and generates structured data, then presents it to the clinician for verification and modification, ensuring both workflow efficiency and data quality through this iterative feedback loop
Data Source
AI summary
A set of one or more clinical facts may be collected from a clinician's encounter with a patient. It may be determined that an unspecified diagnosis not included in the set of facts may possibly be ascertained from the patient encounter. A user may be alerted that the unspecified diagnosis may possibly be ascertained from the patient encounter.