Personalized Capnography Using Patient-Specific Baselines
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Solution Overview
Problem
Medical monitoring devices, such as capnographs, often trigger unnecessary alarms due to deviations from absolute baselines, leading to alarm fatigue and potential overlooking of true alerts, which can be critical.
Innovation Solution
Implementing personalized capnography that uses patient-specific baselines based on characteristics and background diseases to compute deviations, thereby reducing false alarms and enhancing the reliability of alerts by tailoring alarm settings to individual patient data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If absolute baseline thresholds are used for alarm triggering, then alarm sensitivity is improved, but false alarm rate increases
Solution Approach 1:
The patent applies local quality by transitioning from a universal absolute baseline threshold to patient-specific personalized baseline thresholds. Each patient receives monitoring parameters and alarm thresholds tailored to their individual characteristics, such as age, weight, and medical history, rather than applying a single standardized threshold to all patients. This resolves the contradiction by maintaining high alarm sensitivity for each individual while reducing false alarms that occur when absolute thresholds don't account for patient variability.
Solution Approach 2:
The patent implements parameter changes by dynamically adjusting alarm thresholds based on patient-specific parameters including demographic data, physiological characteristics, and medical history. The system modifies the baseline CO2 values and deviation thresholds from fixed absolute values to variable personalized values, thereby maintaining reliable detection of true abnormalities while adapting to individual patient norms and reducing false positive alarms.
2Object-generated harmful factors
If personalized baseline thresholds are used, then false alarm rate is reduced, but device complexity increases
Solution Approach 1:
The patent applies preliminary action by collecting and storing patient-specific data (demographics, medical history, physiological parameters) before the actual CO2 monitoring begins. This pre-collection of personalized information allows the system to establish individualized baseline thresholds in advance, simplifying the real-time monitoring process while maintaining low false alarm rates. The complex data processing is performed upfront rather than continuously during monitoring.
Solution Approach 2:
The system uses copying by creating personalized baseline profiles for each patient based on their historical data and characteristics. These copied profiles serve as reference templates that can be quickly compared against real-time measurements, reducing the computational complexity during active monitoring while still providing personalized alarm thresholds that minimize false positives.
3Reliability
If frequent alarms are triggered, then detection sensitivity is improved, but clinical workflow disruption increases
Solution Approach 1:
The patent resolves this contradiction by implementing local quality through personalized alarm thresholds adapted to each patient's baseline characteristics. Instead of using uniform absolute thresholds that trigger alarms for normal variations in different patients, the system establishes individualized norms, thereby maintaining high detection sensitivity for true abnormalities while reducing unnecessary alarms that disrupt clinical workflow.
Solution Approach 2:
The system employs feedback mechanisms by continuously comparing real-time CO2 measurements against personalized baseline thresholds and adjusting alarm triggering based on patient-specific patterns. This feedback loop enables the system to distinguish between normal physiological variations and true abnormalities, maintaining high detection sensitivity while minimizing false alarms that would disrupt clinical workflow and cause alarm fatigue.
Data Source
AI summary
Control logic, device and method including same configured to receive a measured carbon dioxide (CO2) related parameter of a patient, to obtain a patient specific baseline for said CO2 related parameter, the patient specific baseline determined based on a characteristic of the patient; to compute a deviation of the measured CO2 related parameter from the patient specific baseline; and to trigger an alarm when the deviation crosses a predetermined threshold value.