LLM Clinical Note Query Refinement via Iterative Evidence Feedback
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Solution Overview
Problem
Conventional methods for extracting useful information from clinical notes are tedious, time-consuming, and prone to user error, and require extensive manual labeling of data for machine learning models.
Innovation Solution
A system utilizing a large language model (LLM) to automatically analyze and extract information from clinical notes by generating and refining queries through a specialized interface, allowing users to test queries, view responses with supporting evidence, and iteratively refine their queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If trained health professionals read through all clinical notes manually, then information extraction is performed, but the process is tedious, time-consuming, and prone to user error
Solution Approach 1:
The system enables self-service by allowing health professionals to define their own query specifications and iteratively refine them through testing on test sets. The LLM system then automatically processes clinical notes according to these user-defined specifications, eliminating the need for manual reading while maintaining user control over extraction criteria.
Solution Approach 2:
The patent replaces the mechanical process of manual reading and information extraction with an LLM-based automated system. The LLM processes clinical notes programmatically according to user-defined query specifications, substituting human cognitive effort with automated language processing while preserving the ability to handle complex, nuanced medical information.
2Productivity
If keyword searches are performed on clinical notes, then the amount of reading is reduced, but information that is not flagged by search may be missed
Solution Approach 1:
The system changes the parameters of information retrieval by moving from simple keyword matching to LLM-based semantic analysis. Users can specify complex query conditions involving multiple criteria, relationships between entities, and contextual understanding, allowing the system to retrieve relevant information that keyword searches would miss while maintaining high processing speed.
3Extent of automation
If machine learning models are developed for each specific information element, then automated analysis is achieved, but extensive manual labeling of data is required
Solution Approach 1:
The patent implements a universal LLM-based system that can handle multiple different information extraction tasks through a single platform. Instead of requiring separate trained models for each information element, the system uses user-defined query specifications to configure the LLM for different extraction needs, eliminating the time-consuming process of training multiple specialized models while maintaining automation.
4Ease of operation
If users define query specifications for LLM processing, then automated information extraction is performed, but the process requires iterative refinement and testing
Solution Approach 1:
The system incorporates feedback mechanisms by allowing users to test query specifications on test sets of clinical notes and review the results. Users can iteratively refine their query definitions based on the observed performance and extracted information, creating a feedback loop that improves both the ease of operation and the accuracy of information extraction while managing complexity through systematic refinement.
Data Source
AI summary
A method includes: (a) for each of a set of queries, receiving a specification of that query; (b) receiving an indication of a set of clinical notes; (c) for each query, prompting an LLM system based on the respective specification of that query and receiving a response from the LLM system to each query for each clinical note, each response including a label and evidence from that clinical note supporting the label; (d) for at least one query, displaying the label and evidence for each clinical note generated in response to the at least one query; (e) in response to displaying, receiving a revised specification of the at least one query; and (f) prompting the LLM system based on the revised specification of the at least one query and receiving an updated response from the LLM system to the at least one query for each clinical note, each updated response including an updated label and updated evidence from that clinical note supporting the updated label.