Care Protocol Monitoring for Real-Time Oxygen Delivery Control
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Solution Overview
Problem
Current systems lack the ability to monitor and optimize oxygen delivery and consumption during cardiopulmonary bypass in real time, leading to potential tissue hypoxia and increased morbidity and mortality due to inadequate data collection and calculation of complex clinical predictors like the indexed oxygen delivery to carbon dioxide elimination ratio.
Innovation Solution
A monitoring system that collects data from multiple sources, calculates and simulates oxygen delivery and consumption in real time, and provides real-time feedback and alerts to clinicians, allowing them to define and adjust process controls dynamically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data from multiple sources is collected to calculate complex clinical predictors like oxygen delivery to carbon dioxide elimination ratio, then measurement precision and reliability of patient monitoring is improved, but device complexity and difficulty of real-time calculation increases
Solution Approach 1:
The system segments the complex monitoring task by dividing it into distinct functional modules: data collection from multiple sources, data validation, calculation engine for complex predictors, and real-time display. This modular architecture manages complexity while maintaining measurement precision across all monitored parameters.
Solution Approach 2:
The monitoring system is designed as a universal platform capable of collecting data from various electronic devices and calculating multiple different clinical predictors simultaneously. The system handles diverse data types and calculation formulas through a unified interface, reducing overall system complexity despite the variety of functions.
2Productivity
If real-time calculation and monitoring of complex clinical predictors is implemented, then productivity and responsiveness of patient care is improved, but device complexity and operational difficulty increases
Solution Approach 1:
The system performs automatic real-time calculations of complex clinical predictors without requiring manual intervention. The processor continuously computes oxygen delivery, carbon dioxide elimination, and their ratios by automatically collecting data from connected devices and applying the appropriate formulas, enabling high productivity while maintaining ease of operation.
Solution Approach 2:
The system provides continuous real-time feedback through visual displays showing calculated clinical predictors and their trends. This immediate feedback enables clinicians to make rapid decisions while the system handles the computational complexity, separating the operational simplicity from the calculation sophistication.
3Measurement precision
If data collection from multiple electronic devices is performed, then measurement precision and comprehensiveness of patient monitoring is improved, but loss of time for data integration and machine language translation increases
Solution Approach 1:
The system establishes pre-configured data collection protocols and calculation formulas before patient monitoring begins. Data streams from multiple electronic devices are pre-mapped to specific parameters, and calculation routines are pre-loaded, enabling immediate real-time processing without time-consuming setup or translation delays during critical patient care moments.
Data Source
AI summary
A monitoring system for care protocols, comprising sensors connected to electronic devices to input data from a patient, where sensors measure critical values from a patient; an interface for receiving data and allowing users to write and change process control in real time; a processor connected to the interface to receive input parameters, wherein the processor calculates output values based on the input parameters compared to the critical value to determine whether the output values are outside acceptable range; means for setting critical values, ranges of critical value, and alarm points when the critical values are outside of the range; where the interface receives critical patient parameters and the interface includes a manual input and a machine input from one or more sensors; and wherein the system calculates and monitors critical steps or values for a patient and enables the output values for monitoring or display, in real time.