Insulin Delivery Pattern Selection via Meal Type GUI
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Solution Overview
Problem
Current insulin delivery pumps require users to preprogram bolus delivery patterns, which can be arbitrary and difficult to navigate, leading to inadequate glycemic control due to complex data input and lack of user-friendly interfaces, often resulting in inappropriate insulin administration based on meal type.
Innovation Solution
A drug delivery device with a graphical user interface that stores pre-programmed bolus delivery patterns, allowing users to select a pattern based on nutritional characteristics of meals, such as glycemic index, and providing a simplified method for choosing the appropriate delivery rate and duration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If users preprogram bolus delivery patterns manually, then the device can deliver insulin according to specific patterns, but the interface becomes complex and difficult to navigate
Solution Approach 1:
Instead of requiring users to select from complex preprogrammed patterns manually, the system inverts the approach by automatically detecting meal type through sensors and selecting the appropriate bolus delivery pattern automatically. The interface presents simplified meal type options rather than complex delivery pattern parameters, making operation easier while maintaining adaptability.
Solution Approach 2:
The system performs automatic bolus calculation and pattern selection based on sensor data from the meal, eliminating the need for manual user programming. The device serves itself by autonomously determining the appropriate insulin delivery parameters based on detected nutritional characteristics.
2Manufacturing precision
If complex data input is required for bolus programming, then precise insulin delivery can be achieved, but user frustration increases and glycemic control becomes inadequate
Solution Approach 1:
The system replaces manual data input mechanisms with automatic sensor-based detection. Instead of requiring users to manually enter nutritional data, sensors automatically detect meal composition and characteristics, substituting the mechanical input process with an automated sensing and calculation system that maintains precision without complexity.
Solution Approach 2:
The system introduces an intermediary processing layer that automatically translates sensor data into appropriate bolus delivery parameters. This intermediary layer handles the complex calculations and pattern selections, shielding users from complexity while ensuring precise insulin delivery based on detected meal characteristics.
3Adaptability or versatility
If multiple preprogrammed bolus delivery patterns are stored, then appropriate patterns for different meal types can be selected, but the selection process becomes more complex
Solution Approach 1:
The system performs preliminary classification of meals into types based on sensor data before the user needs to select a delivery pattern. By pre-processing the meal characterization and automatically matching it with appropriate bolus patterns, the system eliminates the need for users to navigate complex pattern selection interfaces while maintaining adaptability to different meal types.
Solution Approach 2:
The system extracts only the essential meal type characteristics needed for bolus selection from the full spectrum of possible meal variations. By focusing on key distinguishing features rather than all possible parameters, the system simplifies the selection process while maintaining sufficient adaptability to handle different meal types appropriately.
Data Source
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AI summary
Methods, systems and devices for sustained medical infusion of fluids are described. Some implementations describe portable infusion methods, systems and devices for selecting a pattern of fluid delivery. Some implementations describe skin securable insulin dispensing methods, systems and devices for selecting a delivery pattern according to the nutritional characteristics (e.g. glycemic index, GI, fat content, etc.) of caloric intake of the user.