Infusion Device Glucose Response Alerts After Correction Boluses
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Solution Overview
Problem
Existing infusion pump systems generate non-actionable or late alerts for hypoglycemic or hyperglycemic events due to variations in insulin response and carbohydrate consumption, leading to user frustration and increased likelihood of ignoring alerts.
Innovation Solution
The system determines an expected glucose measurement value after a correction bolus, compares it with a current measurement, and generates alerts if the difference exceeds a threshold, indicating an anomalous response or excess insulin, using homeostasis metrics to preemptively notify users of potential imbalances before they occur.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If alerts are generated based on predicted glucose values, then users are notified of potential hypoglycemic orhyperglycemic events in advance, but the alerts may be non-actionable and of limited utility due to variations in insulin response and carbohydrate consumption
Solution Approach 1:
The system performs preliminary actions by generating alerts based on predicted glucose values before actual hypoglycemic orhyperglycemic events occur. The pump controller calculates predicted glucose values using predictive algorithms and generates preemptive alerts when predicted values indicate potential events, allowing users to take corrective action before the actual event occurs.
Solution Approach 2:
The system incorporates feedback mechanisms by comparing predicted glucose values with actual glucose measurements and adjusting alert generation accordingly. The pump controller uses feedback from glucose sensor readings and user responses to refine predictive algorithms and improve the accuracy of future alerts, reducing false alarms while maintaining early notification capability.
2Reliability
If alerts are generated based on currently sensed glucose values, then the alerts are actionable and reliable, but they are provided too late to avoid a hypoglycemic orhyperglycemic event
Solution Approach 1:
The system performs preliminary actions by generating alerts based on predicted glucose values before actual hypoglycemic orhyperglycemic events occur. The pump controller calculates predicted glucose values using predictive algorithms and generates preemptive alerts when predicted values indicate potential events, allowing users to take corrective action before the actual event occurs.
Solution Approach 2:
The system dynamically adjusts alert generation by continuously updating predicted glucose values based on current glucose measurements, insulin delivery rates, and user-specific parameters. The pump controller adapts the predictive algorithms in real-time to reflect changing physiological conditions, ensuring that alerts are generated at the optimal time to balance early notification with actionable accuracy.
3Loss of time
If predictive algorithms are used to estimate future blood glucose levels, then users receive advance notification of potential events, but the complexity of regulating blood glucose increases due to variations in insulin response and carbohydrate consumption
Solution Approach 1:
The pump controller performs multiple functions using the same hardware platform: it delivers insulin according to prescribed schedules, monitors glucose levels via sensor integration, executes predictive algorithms to estimate future glucose values, and generates alerts based on predicted events. This multi-functionality consolidates what could be separate complex systems into a single integrated device, managing complexity while providing comprehensive glucose management capabilities.
Solution Approach 2:
The system provides self-service by automatically calculating predicted glucose values and generating alerts without requiring manual user input or interpretation. The pump controller autonomously processes glucose measurements, applies predictive algorithms, determines when alerts should be generated, and notifies users of potential events, reducing the cognitive burden on users despite the underlying complexity of the control scheme.
Data Source
AI summary
Techniques disclosed herein relate to infusion devices and alerts. In some embodiments, the techniques may involve determining an expected glucose measurement value after delivery of a correction bolus based on an amount of the correction bolus and a current amount of active insulin in a body of a patient. The techniques may further involve obtaining a current glucose measurement value. The techniques may further involve detecting an anomalous response to the correction bolus responsive to determining that a difference between the current glucose measurement value and the expected glucose measurement value exceeds a predetermined threshold.