Ventilator Setting Recommendation System
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Solution Overview
Problem
Current medical systems for ventilator settings recommendations are not efficient in providing quick and comprehensible treatment suggestions, often requiring extensive data collection and complex decision-making processes, which can lead to user confusion and delayed adjustments.
Innovation Solution
A medical system with a data interface, measured value interface, control storage unit, processing unit, and output unit that receives static and dynamic patient data, applies stored assignment rules to determine recommended ventilator settings, and outputs these recommendations graphically, ensuring timely and transparent decision-making support for clinicians.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the medical system requires extensive data collection and complex decision-making processes, then the accuracy of ventilator setting recommendations is improved, but the time required to provide recommendations increases and user confusion occurs
Solution Approach 1:
The patent segments the recommendation generation process into distinct modules: a data interface for receiving patient data, a control storage unit for storing assignment rules, a processing unit for evaluating data against rules, and an output unit for presenting recommendations. This modular segmentation allows the system to provide accurate recommendations quickly by processing only relevant data through specialized components, avoiding the need for extensive complex analysis of all available data.
Solution Approach 2:
The control storage unit stores pre-defined assignment rules that map patient parameter value ranges to recommended ventilator settings. These rules are prepared in advance, allowing the processing unit to quickly retrieve and apply appropriate recommendations without performing complex real-time calculations. This preliminary preparation of decision logic enables fast recommendation delivery while maintaining accuracy.
2Loss of information
If the medical system provides detailed and frequent recommendations, then the comprehensiveness of treatment guidance is improved, but user confusion increases and unnecessary recommendations are generated
Solution Approach 1:
The processing unit applies different evaluation criteria to different patient parameters based on their clinical significance. The system selectively generates recommendations only for parameters where deviations from target values are clinically meaningful, rather than providing uniform recommendations for all parameters. This local quality approach ensures comprehensive guidance where needed while avoiding unnecessary recommendations that would confuse users.
Solution Approach 2:
The system uses pre-defined assignment rules that serve as templates for recommendation generation. These rules encapsulate clinical expertise and are repeatedly applied to different patient cases, ensuring consistent and comprehensive guidance without requiring complex real-time decision-making. The rules act as copied knowledge structures that maintain comprehensiveness while simplifying the recommendation process.
3Productivity
If the medical system uses simple mapping rules for quick recommendations, then the speed of recommendation delivery is improved, but the device complexity increases to manage and store multiple rules
Solution Approach 1:
The patent extracts the complex rule management functionality into a separate control storage unit that is distinct from the core processing components. This extraction allows the main processing unit to focus on simple, fast evaluation of patient data against stored rules, while the rule management complexity is isolated in a dedicated component. This separation enables quick recommendation delivery while managing rule complexity in a dedicated module.
Solution Approach 2:
The control storage unit acts as an intermediary between the simple processing unit and the complex set of assignment rules. It stores and manages the rules, providing them to the processing unit in a standardized format for efficient evaluation. This intermediary component shields the core processing logic from rule complexity while enabling fast recommendation generation through structured rule access.
Data Source
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AI summary
The invention relates to a medical system (100) for providing a setting recommendation (105) for a ventilator (102) for the ventilation of a patient, comprising a data interface (110), a measurement interface (120), a control storage unit (130), and a processing unit (150). The data interface is configured to receive static patient data (112), wherein static patient data includes values of patient parameters that are essentially unchanged during the course of treatment. The measurement interface is configured to receive dynamic patient data (122), wherein dynamic patient data includes values of patient parameters that change during the course of treatment. The processing unit is configured to determine a new, current patient data set (152) immediately after receiving the patient data, based on the currently available patient data.Furthermore, the processing unit is designed to initiate a comparison between the values of the current patient record and stored assignment rules (132) triggered by the determination of the new current patient record, and based on this comparison to receive at least one device setting (134) of the ventilator recommended in view of the current patient record from the control storage unit, and based on a comparison with a current device setting (154) to trigger a recommendation output (156).