Anesthesia Phase Detection via Rule-Based Parameter Analysis
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Solution Overview
Problem
Current perioperative care systems lack an efficient method for automatically detecting the phase of anesthesia delivery, which can lead to delays and inefficiencies in managing patient care and resource allocation.
Innovation Solution
A system that uses one or more processors to output a graphical user interface (GUI) displaying the phase of anesthesia delivery, receives monitoring parameters from an anesthesia delivery machine, applies a set of rules to identify changes in anesthesia phases, and updates the GUI accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual monitoring of anesthesia phases is used, then patient care can be personalized and adjusted, but time consumption and labor intensity increase
Solution Approach 1:
The system enables automatic self-detection of anesthesia phases by processing monitoring parameters through predefined rules, eliminating the need for manual clinician assessment. The processor automatically identifies phase transitions (induction, maintenance, emergence) by analyzing parameters such as oxygen concentration, carbon dioxide levels, and patient vitals, thereby resolving the contradiction between detection accuracy and time consumption.
Solution Approach 2:
The patent replaces manual mechanical monitoring with an automated electronic system that uses processors and algorithms to detect anesthesia phases. The system substitutes human judgment with rule-based automated analysis of monitoring parameters, achieving both high precision in phase detection and significant time savings by continuously processing data without human intervention.
2Productivity
If automated phase detection is implemented, then efficiency and time management improve, but system complexity increases
Solution Approach 1:
The system segments the anesthesia process into distinct phases (induction, maintenance, emergence) and applies specific detection rules to each phase. By dividing the monitoring task into phase-specific rule sets, the system manages complexity through modular organization, allowing efficient automated detection while maintaining manageable system structure through segmented rule applications.
Solution Approach 2:
The system uses a universal rule-based framework that can detect all anesthesia phases using a single integrated processing system. The same processor and rule-engine architecture handle multiple phases and various monitoring parameters, reducing overall system complexity compared to having separate dedicated systems for each phase or parameter.
3Reliability
If continuous monitoring of multiple parameters is performed, then detection reliability improves, but data processing load and system resources increase
Solution Approach 1:
The system applies partial monitoring by selecting specific key parameters (such as oxygen concentration, carbon dioxide levels, and vital signs) that are most critical for phase detection, rather than continuously processing all available data streams. This selective approach maintains high detection reliability by focusing on the most informative parameters while reducing overall computational resource consumption.
Solution Approach 2:
The system dynamically adjusts which parameters are monitored and processed based on the current anesthesia phase and clinical context. By changing the set of active monitoring parameters according to the detected phase, the system maintains high reliability during critical transitions while reducing processing load during stable phases, thereby optimizing resource consumption.
Data Source
AI summary
Systems are provided for perioperative care. In an example, a system includes one or more processors and memory storing instructions executable by the one or more processors to output a graphical user interface (GUI) including a first visual representation of a default or previously-determined phase of anesthesia delivery for a patient; receive a plurality of monitoring parameters of the patient over an observation window, at least a portion of the plurality of monitoring parameters obtained from an anesthesia delivery machine; identify, by applying a selected set of rules to the plurality of monitoring parameters, whether an event signaling a change to a new phase of anesthesia delivery for the patient is detected; based on the event being detected, update the GUI to display a second visual representation of the new phase; and based on the event not being detected, maintain the first visual representation on the GUI.