Patient-Specific Alarm Pattern Selection for Nuisance Reduction
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Solution Overview
Problem
Existing patient monitoring systems generate a high number of nuisance alarms, which can lead to clinicians ignoring potentially clinically significant events due to their complexity and lack of patient-specific detection, often treating all patients equally and failing to tailor alarm generation to individual patient conditions.
Innovation Solution
A system that selects alarm patterns based on patient-specific characteristics, including thresholds and corresponding periods of time, to generate alarms only when physiological parameters meet specific criteria, reducing unnecessary alerts and improving the relevance of alarms to clinically significant conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional alarm systems use fixed thresholds for all patients, then the system is simple to operate, but it generates many nuisance alarms and fails to detect clinically significant events specific to individual patients
Solution Approach 1:
The system performs preliminary actions by establishing a baseline period where alarm thresholds are not yet active, allowing the system to learn and adapt to each patient's normal physiological patterns before clinical thresholds are applied. This preliminary adaptation phase enables the system to distinguish between normal variations and clinically significant events, reducing nuisance alarms while maintaining simplicity.
Solution Approach 2:
The alarm thresholds dynamically adapt to each patient's individual baseline characteristics rather than remaining fixed. The system continuously learns patient-specific patterns and adjusts thresholds accordingly, transforming the static alarm system into a dynamic one that improves reliability without requiring complex manual configuration.
2Reliability
If the system generates alarms for any threshold violation, then all potential events are detected, but clinicians receive too many false alerts and may ignore significant events
Solution Approach 1:
The system implements a baseline period before alarm thresholds become active, during which it learns the patient's normal physiological patterns. This preliminary learning phase allows the system to establish patient-specific baselines, ensuring that subsequent alarms are based on deviations from individual norms rather than generic thresholds, thereby reducing nuisance alarms while maintaining high detection accuracy.
Solution Approach 2:
The alarm thresholds are not fixed but are dynamically adjusted based on each patient's baseline characteristics. The system changes the parameter values of thresholds to match individual patient patterns, transforming universal thresholds into personalized ones. This parameter adaptation ensures that alarms reflect clinically significant deviations specific to each patient, reducing false alerts while improving detection reliability.
3Adaptability or versatility
If the system uses patient-specific alarm patterns, then alarm relevance to individual patients improves, but the system complexity increases due to multiple thresholds and time periods
Solution Approach 1:
The system automatically establishes a baseline period for each patient before alarm thresholds become active. This preliminary phase is automatically configured and managed by the system, creating patient-specific detection patterns without requiring complex manual setup. The automatic baseline establishment simplifies the user interface while enabling sophisticated patient-specific adaptability.
Solution Approach 2:
The system performs self-service by automatically learning and adapting to each patient's baseline characteristics during the baseline period. The alarm patterns are self-configured based on observed patient data rather than requiring manual programming of multiple thresholds and time periods. This self-adaptation capability provides patient-specific detection while keeping the system interface simple and easy to operate.
Data Source
AI summary
An alarm system can include one or more processors. Such processors can receive physiological parameter data associated with a patient. The one or more processors can also select an alarm pattern based at least in part on an indicator of an attribute of the patient. The alarm pattern can include a plurality of different thresholds and corresponding periods of time. Further, the one or more processors can generate an alarm when a value associated with the physiological parameter data satisfies at least one threshold of the plurality of thresholds for the period of time corresponding to the at least one threshold.