Contextual Medication Dosage System Using Real-Time Analyte Monitoring
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Solution Overview
Problem
Current external infusion devices for Type 1 diabetic patients rely on educated estimates for insulin dosage calculations, lacking real-time monitoring and contextual information to effectively manage glucose levels.
Innovation Solution
A system that includes real-time analyte monitoring and fluid delivery devices, using analyte sensors to transmit glucose levels for continuous analysis, updating therapy profiles, and providing contextual-based dosage recommendations based on historical data and physiological conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If educated estimates based on patient physiology are used for insulin dosage calculation, then the device complexity is reduced, but the measurement precision and reliability of dosage determination deteriorates
Solution Approach 1:
The system performs preliminary actions by continuously monitoring analyte levels and pre-calculating optimal dosage ranges before the patient needs to administer insulin. Historical dosage data and physiological parameters are pre-processed to create personalized dosage recommendations, reducing the complexity of real-time decision-making while improving dosage precision through data-driven insights rather than educated estimates.
2Reliability
If real-time analyte monitoring and contextual information processing are implemented, then the reliability of insulin therapy management is improved, but the device complexity increases
Solution Approach 1:
The system achieves multi-functionality by integrating analyte monitoring, historical data storage, contextual information processing, and dosage recommendation generation into a single comprehensive platform. This universal approach improves therapy management reliability by consolidating multiple functions that work together synergistically, rather than requiring separate devices for each function, thereby managing complexity through integration rather than multiplication of components.
Solution Approach 2:
The system implements continuous feedback loops where analyte monitoring data, historical dosage information, and physiological parameters are constantly processed to refine and update dosage recommendations. This feedback mechanism enhances reliability by enabling dynamic adjustment of therapy based on real-time and historical data, while the automated nature of the feedback processing manages complexity through algorithmic decision-support rather than manual intervention.
3Adaptability or versatility
If contextual information including past dosage administration is analyzed, then the adaptability of therapy recommendations is improved, but the loss of time for data processing increases
Solution Approach 1:
The system performs preliminary processing of contextual information by continuously analyzing and storing historical dosage data and physiological parameters as they occur, rather than processing them only when needed. This pre-processing approach builds personalized profiles and pre-calculates dosage ranges in advance, enabling rapid retrieval of adaptive recommendations when the patient needs to administer insulin, thereby reducing the time loss associated with real-time data processing while maintaining high adaptability.
Data Source
AI summary
Methods and devices for statistical determination of medication dosage level such as bolus amount based on contextual information are provided.