ASA-PS Classification Using NLP-Based EMR Summarization
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Solution Overview
Problem
The current ASA-PS classification system faces inconsistencies due to subjective judgment among healthcare professionals, particularly in distinguishing between classes 2 and 3, and requires manual summarization of diverse clinical data, leading to inefficiency and variability.
Innovation Solution
A system and method using natural language processing (NLP) to automatically generate medical summaries from electronic medical records, incorporating large language models and multi-agent collaboration networks, to accurately classify ASA-PS classes, provide interpretability, and quantify uncertainty.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual summarization of clinical data is performed by healthcare professionals, then the ASA-PS classification can be completed, but the consistency and objectivity of classification deteriorate due to subjective judgment
Solution Approach 1:
The system enables automated self-service classification by extracting clinical data from EMR and automatically generating ASA-PS classification results through NLP models, eliminating the need for manual human judgment while maintaining high consistency and objectivity in classification outcomes
Solution Approach 2:
The patent replaces the mechanical manual review process with an automated NLP-based system that processes clinical data through trained language models, substituting human subjective judgment with algorithmic objective analysis to improve classification consistency
2Measurement precision
If manual summarization of diverse clinical data is performed, then ASA-PS classification can be achieved, but the time required increases significantly
Solution Approach 1:
The system performs automated data extraction and summarization from EMR records without requiring manual human intervention, significantly reducing the time investment while maintaining accurate ASA-PS classification through algorithmic processing of clinical data
Solution Approach 2:
The NLP model performs preliminary extraction and organization of relevant clinical data from unstructured EMR records before classification, preparing the data in advance to enable rapid and accurate ASA-PS assessment without time-consuming manual review
3Measurement precision
If comprehensive clinical data analysis is performed manually, then accurate ASA-PS classification can be achieved, but the workload and complexity increase
Solution Approach 1:
The NLP-based system performs multiple functions including data extraction, summarization, and classification assessment within a single automated framework, handling diverse clinical data types uniformly without increasing operational complexity for users
Data Source
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AI summary
The present application relates to a method and a system for automatically generating medical summary information and predicting pre-anesthetic physical status (ASA-PS) class of patients by analyzing various clinical data of an electronic medical record with a natural language processing technology. The present application extracts various clinical data from an electronic medical record database, such as surgical information, hospitalization initial diagnosis, nursing initial diagnosis, hospitalization progress, vital signs, test results, and clinical observation records of the patients, and then automatically generates medical summary information of the patients using an artificial intelligence-based natural language processing system. The generated medical summary information is used as an input of a medical classification model to classify the pre-anesthesia physical status of the patients, and visualizes and provides a prediction basis of the medical classification model.