Patient Parameter Fusion with Motion-Aware Signal Qualification
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Solution Overview
Problem
Current physiologic monitoring systems in emergency care suffer from corrupted or ambiguous signals due to chaotic environments and lack of context, leading to incorrect healthcare decisions and delayed responses.
Innovation Solution
A system that combines physiologic monitoring data from multiple sensors and incorporates motion, position, and orientation (MPO) sensors to analyze and qualify signal integrity, providing context-driven feedback to caregivers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple sensors are used to monitor patient vital signs, then measurement precision is improved, but signal corruption and ambiguity increase due to chaotic emergency environments
Solution Approach 1:
The patent combines data from multiple sensors (ECG, SpO2, capnography, blood pressure, temperature) and integrates them with motion, position, and orientation sensor data to create a unified patient status assessment. This merging allows the system to cross-validate signals and distinguish true physiological changes from motion-induced artifacts, thereby maintaining measurement precision while improving signal reliability in chaotic emergency environments.
Solution Approach 2:
The system introduces MPO sensors as intermediary devices that measure motion, position, and orientation to serve as mediators between the physical environment and the physiological monitoring system. These intermediary sensors provide context about environmental chaos and patient movement, allowing the system to qualify and interpret physiological signals more accurately despite signal corruption from the chaotic emergency setting.
2Device complexity
If individual sensor feedback is provided to caregivers, then device complexity is reduced, but loss of contextual information increases leading to incorrect healthcare decisions
Solution Approach 1:
The system merges individual sensor feedback with MPO sensor data and patient care event information into a unified contextual framework. Instead of presenting separate sensor readings, the system integrates all data sources to provide comprehensive feedback that includes both physiological parameters and their contextual interpretation, preventing information loss while managing complexity through unified data processing.
Solution Approach 2:
The system implements enhanced feedback mechanisms that provide caregivers with context-driven alarms and interpretations rather than raw sensor data. The feedback includes qualified information about signal reliability, patient position context, and motion-related artifacts, enabling caregivers to make informed decisions without being overwhelmed by complex individual sensor readings.
3Loss of time
If physiologic monitoring signals are collected in real-time during emergency care, then response time is improved, but signal corruption from motion and equipment failure increases
Solution Approach 1:
The system performs preliminary qualification of physiological signals by comparing them against MPO sensor data in real-time. Motion, position, and orientation information are used beforehand to predict and filter potential signal corruption before it affects clinical decision-making. This preliminary action allows the system to maintain high response speed while preemptively addressing signal quality issues from patient movement or equipment instability.
Solution Approach 2:
MPO sensors serve as intermediary devices that provide real-time context about patient movement and equipment position. These intermediary measurements are continuously compared with physiological signals to identify and flag corrupted data in real-time, enabling the system to maintain fast response times while filtering out motion-induced artifacts and equipment-related signal corruption.
Data Source
AI summary
An example method is performed by a computing device executing instructions stored in data storage, and includes receiving physiologic monitoring data from a plurality of sensors coupled to a patient, receiving information indicating a measurement of patient motion during the patient care event, determining whether the measurement of patient motion is above a threshold, based on determining whether the measurement of patient motion is above the threshold, generating, for the physiologic monitoring data, a respective quality indicator, analyzing, by the computing device, (i) a combination of the physiologic monitoring data from the plurality of sensors and (ii) the respective quality indicator for the physiologic monitoring data to generate a response dependent upon the combination of the physiologic monitoring data as weighted by the respective quality indicator, and based on analyzing, outputting caregiver feedback by the computing device according to the response.