Rule-Based Insulin Pump Setting Guidance From Patient Questioning
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Solution Overview
Problem
Conventional insulin therapy methods, such as multiple daily injections and insulin pumps, are inconvenient and require significant user effort and learning to manage blood glucose levels effectively, often leading to variations in insulin delivery and potential complications from high or low blood sugar levels.
Innovation Solution
A device with a controller that includes an input/output module and a rule module, which presents questions to users, receives patient information, and applies rules to generate suggested insulin pump settings, such as basal rates, correction factors, and carbohydrate ratios, to assist in maintaining and adjusting insulin pump therapy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional insulin therapy (multiple daily injections) is used, then insulin delivery can be achieved, but user convenience deteriorates and requires significant tracking and learning
Solution Approach 1:
The system performs self-service by automatically calculating insulin delivery parameters (basal rates, correction factors, carbohydrate ratios) based on patient data, eliminating the need for manual tracking and complex user calculations. The controller autonomously generates pump settings from patient information without requiring user expertise in insulin therapy management.
Solution Approach 2:
The controller acts as an intermediary between the patient and the insulin pump, translating patient information into optimized pump settings. This intermediary layer simplifies the user interface while maintaining sophisticated therapy management capabilities, bridging the gap between simple operation and advanced functionality.
2Ease of operation
If insulin pump therapy is used, then convenience and flexibility improve, but device complexity increases and requires learning curve
Solution Approach 1:
The insulin pump system performs self-service by automatically determining optimal delivery parameters (basal rates, correction factors, carbohydrate ratios) from patient data. This eliminates the need for users to manually program complex settings or understand sophisticated insulin therapy calculations, maintaining convenience while reducing the learning curve.
Solution Approach 2:
The system performs preliminary action by pre-calculating and storing optimized insulin delivery parameters based on patient information before actual insulin delivery. This advance preparation allows the pump to operate with simple, pre-determined settings rather than requiring real-time complex calculations or user programming during therapy.
3Extent of automation
If automated rule-based system is implemented, then determination of insulin delivery parameters is simplified, but device complexity increases
Solution Approach 1:
The controller is segmented into distinct functional modules (I/O module for data input and rule module for parameter calculation), allowing complex automation to be organized into manageable, independent components. This modular architecture reduces the learning curve by separating concerns while maintaining high automation capability.
Data Source
AI summary
An apparatus comprising a controller. The controller includes an input/output (I/O) module and a rule module. The I/O module is configured to present a question for a patient when communicatively coupled to a user interface and receive patient information in response to the question via the user interface. The rule module is configured to apply a rule to the patient information and generate a suggested insulin pump setting from application of the rule. Other devices, systems, and methods are disclosed.