Adaptive Surgical Warning System Dynamic Alert Thresholds
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Solution Overview
Problem
Conventional methods for preventing surgical errors and adverse events during surgery are insufficient, leading to complications such as anesthesia-related issues, hemorrhaging, and temperature fluctuations, due to inadequate monitoring and communication among healthcare teams.
Innovation Solution
An adaptive patient condition surgical warning system that uses real-time sensor data and historical reference data to dynamically adjust alert criteria, providing accurate and timely warnings to surgical staff through a network of monitoring devices and robotic controls, thereby preventing adverse events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional fixed alert threshold methods are used, then the system is simple to operate, but false alarms increase and reduce reliability
Solution Approach 1:
The patent implements dynamic alert thresholds that automatically adjust based on real-time surgical context, patient physiology, and procedural stage. The system transitions from static fixed thresholds to dynamic adaptive thresholds that change parameters continuously during surgery, improving alert accuracy without requiring complex manual intervention.
Solution Approach 2:
The system incorporates feedback loops where alert performance data is continuously analyzed and used to refine threshold settings. The monitoring system learns from historical surgical data and adjusts alert criteria based on actual patient responses and surgical outcomes, creating a self-optimizing system that improves reliability over time.
2Adaptability or versatility
If standardized alert criteria are applied to all patients, then the system is easy to implement, but patient-specific conditions are not adequately addressed
Solution Approach 1:
The system automatically configures patient-specific alert criteria by ingesting electronic health record data, surgical schedule information, and real-time physiological measurements. The monitoring system self-adjusts thresholds based on patient demographics, medical history, anesthesia type, and procedural factors without requiring manual physician configuration, eliminating setup time while maintaining personalization.
Solution Approach 2:
Alert criteria are pre-configured based on patient preoperative data and surgical procedure type before the surgery begins. The system performs preliminary analysis of patient risk factors and procedural complexity to establish initial personalized thresholds, allowing immediate customized monitoring without delay during surgery setup.
3Reliability
If multiple monitoring parameters are tracked simultaneously, then comprehensive patient safety is achieved, but system complexity and false alarm rate increase
Solution Approach 1:
The system applies different alert threshold criteria and monitoring sensitivity levels to different surgical phases and patient physiological states. Each surgical step has customized alert parameters tailored to the specific risks and expected physiological changes at that stage, allowing comprehensive monitoring while reducing false alarms through context-specific thresholds.
Solution Approach 2:
The monitoring system dynamically adjusts which parameters are actively monitored and their respective threshold sensitivity based on surgical progression and patient response. The system transitions between monitoring modes, intensifying surveillance of critical parameters during high-risk surgical phases while reducing sensitivity during stable periods, thereby maintaining safety without generating unnecessary false alarms.
Data Source
AI summary
Monitoring devices monitor physiological parameters of a patient undergoing surgery. The physiological parameters describe a physiological condition of the patient. A processor matches the physiological parameters to stored surgical data associated with adverse surgical events associated with surgical procedures matching the surgery. The processor determines a predicted time at which the physiological condition of the patient will meet a threshold physiological condition associated with the adverse surgical event based on a rate of change of the physiological parameters. Responsive to determining the predicted time, the processor transmits a first alert to robotic surgical controls to adjust the surgery prior to the predicted time. The processor determines that the physiological condition of the patient has met the threshold physiological condition. The processor transmits a second alert to the robotic surgical controls to terminate the surgery.