Intelligent Agent Segmentation for Medical Decision Support
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Solution Overview
Problem
Current medical devices lack effective decision support systems that can provide real-time, intelligent coaching for caregivers during emergency situations such as Ventricular Fibrillation, leading to delayed interventions and reduced survival rates due to the complexity of navigating treatment protocols.
Innovation Solution
A medical device with a computing architecture that includes a primary rules-based service and an artificial intelligence module, capable of providing independent processing threads for coaching treatment based on pre-defined conditions, enabling timely and effective interventions during emergencies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a primary rules-based service is used to provide treatment coaching, then the system structure is simple and easy to implement, but the response time is delayed and survival rates are reduced due to inability to handle complex emergency situations
Solution Approach 1:
The decision support system is segmented into multiple independent intelligent agents, each specializing in specific emergency scenarios (e.g., cardiac arrest, respiratory failure). This allows the system to handle complex situations through coordinated agent responses while maintaining modular architecture that doesn't overly complicate implementation.
Solution Approach 2:
Intelligent agents are pre-configured with treatment protocols and decision-making algorithms for various emergency conditions. When an emergency is detected, the appropriate agent immediately activates pre-loaded guidance, eliminating the need for real-time complex calculations and reducing response time while maintaining reliability.
2Ease of operation
If treatment protocols are made comprehensive to cover all emergency scenarios, then the quality of care improves, but the difficulty of navigating protocols increases and response time is delayed
Solution Approach 1:
The intelligent agents autonomously monitor patient parameters and automatically navigate through appropriate treatment protocols without requiring caregiver intervention to interpret complex guidelines. The system self-adjusts treatment recommendations based on real-time data, making comprehensive protocols easy to operate while maintaining high quality of care.
Solution Approach 2:
The system continuously monitors patient response to treatment and provides real-time feedback to both caregivers and the intelligent agents. This feedback loop allows the agents to dynamically adjust protocol navigation, ensuring comprehensive care is delivered efficiently by adapting to actual patient conditions rather than following rigid predetermined paths.
3Loss of time
If real-time monitoring is implemented to detect emergencies early, then the response time improves, but the device complexity and energy consumption increase
Solution Approach 1:
The monitoring system dynamically adjusts its intensity based on patient condition and risk factors. During stable periods, monitoring operates at lower intensity to conserve energy. When anomalies are detected or during high-risk periods, the system automatically increases monitoring frequency, achieving early emergency detection without continuous high-energy consumption.
Solution Approach 2:
The system changes monitoring parameters (sampling frequency, threshold sensitivity) based on clinical context and patient acuity. For example, monitoring frequency increases automatically when vital signs approach critical thresholds, enabling early emergency detection while minimizing energy consumption during stable periods through parameter adaptation rather than constant high-level monitoring.
Data Source
AI summary
A computing architecture, system and method are disclosed for use in a medical device for providing decision support to a caregiver. The computing architecture includes a memory, a processor in communication with the memory, and an instance of a primary rules-based service configured to provide instruction events, the instance providing a primary processing thread of instruction events for coaching treatment of a patient. A software manager module includes an artificial intelligence architecture. The artificial intelligence architecture is configured to provide an instance of a conditional rules-based service for providing instruction events. The instance provided by the artificial intelligence architecture provides a processing thread of instruction events for coaching treatment of a patient that is independent of the primary processing thread and is configured to trigger an action on the occurrence of a pre-defined set of input conditions.