NLP-Based ASA-PS Classification From Unstructured Clinical Records
Find Innovative SolutionsGenerate Solutions
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 lacks consistency in summarizing patient status from unstructured clinical data, leading to inefficiencies and potential misclassification.
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 pre-anesthesia physical status (ASA-PS) while providing interpretability and uncertainty quantification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual summary preparation is performed by healthcare professionals, then clinical data can be reviewed, but time consumption increases and consistency deteriorates
Solution Approach 1:
The patent replaces the manual mechanical process of healthcare professionals reviewing and summarizing clinical data with an automated NLP-based system. The system extracts entities, relationships, and summaries from electronic medical records using natural language processing algorithms, eliminating the time-consuming manual review process while maintaining consistent classification standards through computational accuracy rather than human subjectivity.
Solution Approach 2:
The system enables self-service by automatically processing clinical data without requiring manual intervention from healthcare professionals for the summary generation task. The NLP system independently extracts information, identifies ASA-PS class, and provides explanations, allowing the system to serve itself rather than requiring human experts to perform the repetitive summarization task.
2Adaptability or versatility
If subjective judgment is used in ASA-PS classification, then clinical context can be considered, but classification consistency deteriorates
Solution Approach 1:
The patent changes the fundamental parameter of classification from subjective human judgment to objective computational analysis. By transforming clinical data into structured representations that NLP algorithms can process consistently, the system maintains adaptability to clinical contexts while achieving reliable consistency through standardized algorithmic application rather than variable human interpretation.
Solution Approach 2:
The system incorporates feedback mechanisms where the NLP model processes clinical data, generates ASA-PS classification, and provides explanations that can be reviewed. This feedback loop allows the system to maintain flexibility by allowing clinician review while ensuring consistency through standardized processing, as the algorithm applies the same logical framework to all cases rather than relying on individual clinician judgment.
3Measurement precision
If comprehensive clinical data analysis is performed manually, then accurate patient status summary can be generated, but productivity decreases
Solution Approach 1:
The patent replaces the manual mechanical process of comprehensively reviewing multiple clinical data types with an automated NLP system that processes the same comprehensive data electronically. The system extracts and analyzes information from various clinical sources simultaneously, maintaining the thoroughness required for accurate patient status summaries while dramatically improving productivity by eliminating manual review time.
Solution Approach 2:
The system enables continuous automated processing of clinical data without interruption or fatigue. Unlike manual review which requires breaks and varies in intensity, the NLP system continuously processes comprehensive clinical data streams, maintaining constant attention to detail required for accurate summaries while operating at high speed to improve productivity.
4Reliability
If automated NLP-based classification is implemented, then consistency and objectivity improve, but system complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the complex NLP processing into distinct functional modules: entity extraction, relationship identification, summary generation, and ASA-PS classification. This modular approach manages system complexity by organizing the processing pipeline into manageable components that can be developed, tested, and maintained independently, while collectively achieving reliable objective classification.
Solution Approach 2:
The system introduces an intermediary NLP processing layer between the raw clinical data and the final ASA-PS classification. This intermediary layer standardizes and structures the data through automated extraction and summarization, acting as a buffer that translates complex clinical information into standardized representations that facilitate consistent, objective classification while managing the complexity through structured intermediate processing steps.
Data Source
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.