Brain Attack Triage System Integrating Biosensor Data Streams
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Solution Overview
Problem
Current biosensor systems are unable to effectively predict and recognize interruptive health events, such as brain attacks, in a timely manner to enable preventive or mitigative interventions, as they rely solely on tracking physiological signals without integrating additional health data streams to alert providers and propose interventions.
Innovation Solution
A system that integrates physiological signals from biosensors with health data from multiple streams, using an inference engine to predict and recognize brain attacks, and communicates with providers to propose interventions, including alerts and real-time interactions with individuals and caregivers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If biosensors track physiological signals alone, then device simplicity is maintained, but the ability to predict and recognize interruptive health events in time for effective intervention is insufficient
Solution Approach 1:
The patent combines multiple data streams including physiological signals from biosensors, health data from electronic health records, and environmental data into a unified monitoring system. This integration enables comprehensive analysis that improves the reliability of predicting and recognizing interruptive health events while distributing system complexity across multiple coordinated components rather than concentrating it in a single device.
Solution Approach 2:
The patent introduces an inference engine as an intermediary component that processes and integrates data from multiple sources. This intermediary analyzes the combined data streams to identify patterns and predict health events, thereby improving detection reliability without requiring the biosensor hardware itself to become overly complex. The inference engine acts as a mediator between raw data collection and clinical decision-making.
2Measurement precision
If multiple data streams are integrated to predict health events, then prediction accuracy improves, but data processing complexity increases
Solution Approach 1:
The patent segments the data processing function into distinct modules: data collection from multiple sources, data integration, pattern recognition, and prediction generation. By dividing the complex task of analyzing multiple data streams into manageable segments, the system achieves high prediction accuracy while keeping each processing component relatively simple and well-defined.
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously monitors health data streams, compares actual readings against predicted patterns, and adjusts predictions based on deviations. This feedback loop improves prediction accuracy over time by learning from actual health event outcomes, while the automated nature of the feedback processing reduces the perceived complexity for users.
3Loss of time
If real-time monitoring and provider alerting are implemented, then intervention timeliness improves, but system operational complexity increases
Solution Approach 1:
The patent enables the monitoring system to automatically perform data collection, analysis, and provider alerting without requiring manual intervention. The system self-manages the entire workflow from detecting health deviations to notifying providers, which improves intervention timeliness while reducing operational complexity for healthcare providers. The automation handles the complex coordination tasks that would otherwise require manual effort.
Solution Approach 2:
The patent implements preliminary actions by pre-configuring alert thresholds, provider contact information, and intervention protocols before health events occur. When anomalies are detected, the system can immediately execute pre-planned response actions, reducing the time loss between event detection and provider notification. This preliminary setup reduces operational complexity during actual events by eliminating the need for real-time decision-making about notification procedures.
Data Source
AI summary
Embodiments herein relate to biosensors and other elements combined in real-time for health applications that acquire health data of an individual from one or more data streams by a first element and integrate these data through a second element that is configured to predict and/or recognize an interruptive health event and then via one or more successive elements alert one or more providers of a possible interruptive health event, enable the providers to propose one or more effective interventions, communicate directly with the individual and providers and/or caregivers, and/or provide confirmation in the event that effective interventions were made. Embodiments also relate to predicting and recognizing brain attack in real-time by acquiring and integrating health data from one or more data streams, interfacing with one or more providers so that they can propose and/or effect one or more effective interventions, and communicating with an affected individual, providers, and/or caregivers.