Preclinical Care System with Dynamic Module Control
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Solution Overview
Problem
Existing emergency care systems lack an intelligent decision-making system at the system level that can implement situation-optimized measures both with and without the emergency responder, relying on predefined instruction plans and decision logic at the module level without true system-level intelligence.
Innovation Solution
A networked system with sensor devices, time measuring devices, and data memory that links status signals with time signals and address identifiers for secure communication, allowing for dynamic decision-making based on sensor data, involving the emergency responder through various perception methods and accepting feedback, with a master/slave concept for module control and prioritization of actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple interconnected modules are combined to provide comprehensive emergency care, then the functionality and coverage of emergency measures are improved, but the system complexity and coordination difficulty increase
Solution Approach 1:
The emergency care system is divided into independent functional modules (ventilation module, defibrillation module, monitoring module, etc.), each capable of autonomous operation. This segmentation allows the system to provide comprehensive emergency care through module combination while managing complexity by keeping individual modules simple and standardized.
Solution Approach 2:
The system employs universal communication interfaces and standardized data protocols that allow different modules to interact seamlessly. The control unit can coordinate various modules for different emergency scenarios, making the system adaptable to multiple situations without requiring separate dedicated systems for each function.
2Speed
If decision logic is distributed at the module level, then the responsiveness and autonomy of individual components are improved, but the lack of system-level intelligence reduces optimization capability
Solution Approach 1:
The system implements multi-level feedback mechanisms where modules provide status information to the control unit, which processes this data and generates coordinated control signals. This feedback loop enables the control unit to develop system-level intelligence by analyzing aggregated data from all modules and making optimized decisions that consider the overall system state rather than just individual component states.
Solution Approach 2:
The control unit merges data from multiple independent modules to create a comprehensive system state representation. By combining sensor data, module status, and operational parameters into a unified model, the control unit can make system-optimized decisions that balance the responsiveness of individual modules with overall system coordination.
3Reliability
If predefined instruction plans are used to guide emergency response, then the ease of operation and reliability are improved, but the inability to adapt to dynamic situations reduces flexibility
Solution Approach 1:
The system uses dynamic protocols where the instruction sequence is not fixed but adapts based on real-time sensor data and system state. The control unit can modify the execution of predefined plans by incorporating live feedback from modules, allowing the system to maintain reliability through structured protocols while gaining flexibility through adaptive response to changing conditions.
Solution Approach 2:
The system allows parameters of the instruction plans to be dynamically adjusted based on sensor data. For example, ventilation parameters can be automatically modified based on oxygen saturation readings, or defibrillation energy levels can be adjusted based on rhythm analysis, enabling the system to adapt to dynamic situations while following reliable procedural frameworks.
4Productivity
If real-time data processing and communication between modules are implemented, then the situation-dependent optimization is improved, but the data transmission requirements and communication complexity increase
Solution Approach 1:
The system extracts only the critical data elements needed for decision-making from the full sensor data stream. By identifying and transmitting only the most relevant parameters (such as oxygen saturation, heart rate, rhythm patterns) rather than all available data, the system achieves real-time optimization capability while minimizing data transmission requirements and reducing communication bandwidth needs.
5Loss of time
If the system operates autonomously without emergency responder intervention, then the response time and consistency are improved, but the need for human oversight and complex control algorithms increases
Solution Approach 1:
The system is designed to perform self-monitoring and self-adjustment within predefined safety parameters. Modules automatically detect anomalies and trigger appropriate responses without requiring constant human intervention, reducing response time and improving consistency. The control algorithms are kept relatively simple by relying on rule-based decision support rather than complex AI, maintaining autonomy while avoiding excessive complexity.
Data Source
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AI summary
The invention relates to a system consisting of preclinical emergency care modules which produce a complex condition-dependent control system that is as integral as possible in combination with a human emergency worker. At the system level, an intelligent decision-making system is provided which leads to measures that are optimized for the situation with and without the emergency worker. The modules exhibit a different behavior depending on the situation and interaction. In the process, the emergency worker can be utilized as an additional sensor/actuator module. Based on all obtained sensor data, which is weighted differently, decision-making support is proposed to the emergency worker, or the system makes decisions automatically. The protected communication of the modules is of particular importance for this purpose.