Vital Signs Pattern Recognition for Early Patient Deterioration Detection
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Solution Overview
Problem
Current systems in intensive care units (ICUs) fail to effectively alert clinicians about patients at high risk for adverse outcomes due to limitations such as reliance on single physiologic measures, linear time series analysis, and lack of timely alerting mechanisms, leading to delayed detection of patient deterioration.
Innovation Solution
A multi-step system that analyzes streaming vital signs data to identify patterns associated with high-risk outcomes, using a symbolic representation of data to generate alerts on mobile devices for clinicians, allowing for early detection and intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional linear time series approaches are used to analyze patient data, then the analysis method is simple and easy to implement, but the predictive accuracy for patient deterioration is low
Solution Approach 1:
The patent transforms continuous physiologic time series data into discrete symbolic sequences by applying quantization to create bins and assigning symbols based on which bin each value falls into. This parameter transformation enables the use of complex pattern recognition algorithms while maintaining computational feasibility with streaming data.
Solution Approach 2:
The patent replaces traditional linear time series analysis methods with a symbolic representation system that uses pattern matching and information theory-based algorithms. This substitution enables more sophisticated analysis of temporal patterns in physiologic data without requiring complex mechanical or computational infrastructure.
2Reliability
If only one physiologic measure is monitored, then the monitoring system is simple and cost-effective, but the ability to identify at-risk patients is limited
Solution Approach 1:
The patent combines multiple physiologic measures (heart rate, respiratory rate, mean arterial pressure, temperature, etc.) into a unified analysis system. By merging these different data streams and analyzing them together using the same symbolic representation framework, the system achieves more reliable patient risk identification while maintaining a consistent and manageable architectural approach.
Solution Approach 2:
The patent creates a universal analysis framework that can process multiple types of physiologic data using the same symbolic representation and pattern recognition algorithms. This multi-functional system can analyze any physiologic parameter by transforming it into the standardized symbolic format, enabling comprehensive monitoring without proportionally increasing system complexity.
3Measurement precision
If predictive models require 12 hours of data before generating scores, then the model can use sufficient historical data for accurate predictions, but early detection of patient deterioration is rendered useless
Solution Approach 1:
The patent performs preliminary analysis by continuously transforming physiologic data into symbolic representations and identifying patterns in real-time as data arrives. By preparing the data in this standardized format continuously rather than waiting for a fixed time period, the system can immediately detect deterioration patterns when they emerge, enabling early intervention without sacrificing analytical rigor.
Solution Approach 2:
The patent implements continuous analysis of physiologic data streams, continuously transforming values into symbolic sequences and evaluating patterns without interruption or fixed time delays. This continuous action ensures that patient deterioration is detected as soon as characteristic patterns emerge, maintaining both accuracy and timeliness of detection.
4Ease of operation
If alerts are displayed on a unit screen showing all patients, then the system provides comprehensive monitoring, but clinicians cannot quickly identify and respond to individual high-risk patients
Solution Approach 1:
The patent extracts and isolates patients who exhibit high-risk patterns from the overall patient population by continuously evaluating symbolic sequences against learned patterns. By extracting only those patients whose physiologic patterns match high-risk profiles, the system delivers targeted alerts to clinicians, enabling rapid identification and response to critical cases without being overwhelmed by information about all patients.
Solution Approach 2:
The patent applies different levels of attention and alerting based on the local quality or risk level of individual patients. Rather than treating all patients uniformly, the system identifies specific patients with deteriorating patterns and provides targeted alerts, allowing clinicians to focus their attention where it is most needed while maintaining awareness of the overall patient population.
Data Source
AI summary
The system and method described herein represent a totally new paradigm for assessing risk in critically ill patients by identifying physiologic patterns associated with a poor outcome. It then uses this information to detect these patterns on future patients, sending alerts to clinicians caring for patients with one or more of them. Moreover, the system and method can be extended to outcomes other than mortality, and can be adapted for use in a specific hospital system's EMR. The “code footprint” for embedding the patterns that indicate a trigger is small and thus favorably suited for embedding into an existing clinical decision support system.