AI Instrument Selection Support for Catheter Use Configuration
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Solution Overview
Problem
Healthcare workers, especially those with limited experience, face challenges in selecting the appropriate shape and form of medical instruments for procedures like catheterization, which can impact the success rate of operations and hinder the adoption of these procedures due to difficulty in accurately grasping the biological lumen's state and determining the optimal instrument configuration.
Innovation Solution
A support system that acquires data on healthcare workers, medical institutions, patients, and past operation experiences, performs machine learning, and presents recommended medical instruments and their use forms based on these data, aiding in selecting suitable instruments and shapes for operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a doctor with limited experience selects medical instrument use form based on textbook theory or empirical rules, then the selection process is simple, but the accuracy and success rate of operation is reduced
Solution Approach 1:
An AI support system acts as an intermediary between the doctor and the complex decision-making process. The system processes patient data, biological lumen state information, and operation history to provide recommended instrument use forms, enabling doctors with limited experience to access expert-level decision support without requiring extensive personal experience
Solution Approach 2:
The patent replaces the mechanical system of human expert judgment with an AI-based information processing system. Instead of relying on a doctor's personal experience and empirical rules, the system uses machine learning models to analyze data and generate recommendations, substituting human cognitive limitations with computational capabilities
2Measurement precision
If a doctor carefully analyzes the state of biological lumen to determine optimal instrument use form, then the accuracy of selection is improved, but the time and workload required is significantly increased
Solution Approach 1:
The system performs preliminary analysis of patient data, biological lumen state, and operation history before the actual operation. By pre-processing this information and generating instrument use form recommendations in advance, the system eliminates the need for time-consuming analysis during the operation itself, allowing doctors to quickly review and implement recommendations
3Reliability
If extensive operation data is collected and analyzed to improve recommendation accuracy, then the reliability of recommendations is improved, but the system complexity is increased
Solution Approach 1:
The AI support system is designed to handle multiple types of data (patient information, biological lumen state, operation history) and provide comprehensive recommendations for various medical instruments and procedures. This multi-functional approach consolidates what would otherwise require multiple separate systems into a single unified platform, managing complexity while maintaining reliability
Data Source
AI summary
A support system, a support method, and a support program in the form of a non-transitory computer readable medium, which supports selection of a medical instrument and selection of a use form of the medical instrument. A support system includes the data acquisition unit that acquires health care worker data on a health care worker, medical institution data on a medical institution, patient data on a patient, and operation data on a past operation experience of the health care worker, a learning unit that performs machine learning using the health care worker data, the medical institution data, the patient data, and the operation data, and a presentation unit that presents a medical instrument recommended for use in operation on the patient and a recommended use form of the medical instrument based on a result of the machine learning.