Insulin Pump Rule Engine for Personalized Dosing Setup
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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 therapy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional insulin therapy methods (multiple daily injections or insulin pumps) are used, then blood glucose management is achieved, but user convenience deteriorates and operational complexity increases
Solution Approach 1:
The system enables self-service by automatically generating insulin delivery recommendations based on user-input data. The processor analyzes blood glucose readings, carbohydrate intake, and insulin sensitivity factors to autonomously calculate optimal insulin dosing, eliminating the need for users to manually compute complex dosing schedules while maintaining reliable blood glucose management.
Solution Approach 2:
The system introduces an intermediary layer between the user and insulin delivery decisions. Rather than requiring users to directly manage complex dosing calculations, the system acts as a mediator that processes multiple input parameters (blood glucose, carbohydrates, insulin sensitivity) and translates them into actionable insulin delivery recommendations, simplifying the user interface while preserving therapeutic reliability.
2Adaptability or versatility
If insulin pump settings are manually configured, then therapy customization is achieved, but device complexity and programming difficulty increase
Solution Approach 1:
The system performs preliminary actions by pre-calculating optimal insulin pump settings based on user-specific parameters such as insulin sensitivity, carbohydrate ratios, and target blood glucose ranges. These pre-computed settings are stored and automatically applied when needed, eliminating the need for users to programmatically configure complex pump parameters while maintaining high therapy adaptability to individual needs.
Solution Approach 2:
The system manages device complexity through parameter changes by dynamically adjusting insulin delivery parameters (basal rates, bolus amounts, correction factors) based on real-time inputs. Rather than requiring users to program fixed complex settings, the system automatically modifies delivery parameters in response to changing physiological conditions, maintaining versatility while simplifying user interaction to simple data entry.
3Loss of information
If users track insulin doses and blood glucose levels manually, then therapy monitoring is achieved, but time consumption and operational burden increase
Solution Approach 1:
The system merges multiple monitoring functions into a single integrated process. Users input data once (blood glucose readings, carbohydrate intake), and the system simultaneously performs multiple tasks: tracking blood glucose trends, calculating insulin requirements, adjusting insulin sensitivity factors, and generating delivery recommendations. This consolidation maintains comprehensive therapy monitoring accuracy while significantly reducing the time and effort users would spend on separate tracking tasks.
Solution Approach 2:
The system implements automated feedback loops that continuously monitor therapy effectiveness. By analyzing trends in blood glucose readings and comparing them against target ranges, the system automatically adjusts insulin delivery recommendations and notifies users of patterns requiring attention. This feedback mechanism maintains accurate therapy monitoring while reducing manual intervention time, as the system autonomously processes and responds to monitoring data.
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.