Automated Dose Guidance System for Diabetes Management
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Individuals with diabetes often fail to monitor their glucose levels frequently due to inconvenience, pain, and cost, leading to suboptimal management of their condition, and existing medication dose guidance systems require extensive user input and are not adaptable to individual physiology and behavior.
Innovation Solution
A dose guidance system that includes a display device, sensor control device, and medication delivery device, which uses a dose guidance application to learn a patient's dosing strategy, provide intuitive and user-friendly guidance for medication dosing, and automatically adjust dosing recommendations based on physiological, dietary, and behavioral factors, minimizing manual input and reducing the risk of hypoglycemic episodes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing medication dose guidance systems are used, then medication dosing can be guided, but extensive user input is required and the system is not adaptable to individual physiology and behavior
Solution Approach 1:
The system automatically learns the patient's dosing strategy and key dosing parameters through a learning period by analyzing glucose data and dosing patterns, eliminating the need for extensive manual user input. The system serves itself by autonomously adapting to individual physiology and behavior without requiring continuous user configuration.
Solution Approach 2:
The system continuously monitors glucose data and dosing patterns, using this feedback to automatically adjust and refine dosing recommendations. The feedback loop enables the system to adapt to individual patient characteristics over time while minimizing the initial setup burden on users.
2Reliability
If glucose monitoring frequency is increased to improve glycemic control, then better glucose management is achieved, but inconvenience, pain, and cost increase
Solution Approach 1:
The system automatically processes and analyzes glucose data without requiring manual intervention from the patient. It autonomously generates dosing recommendations based on the collected data, reducing the burden on patients while maintaining frequent monitoring for better glycemic control.
Solution Approach 2:
The dose guidance application acts as an intermediary between glucose monitoring and dosing decisions, automatically interpreting glucose data and translating it into actionable dosing recommendations. This mediator function reduces the direct burden on patients by automating the complex analysis and decision-making process.
3Productivity
If manual dosing calculations are performed, then dosing decisions can be made, but the burden on healthcare professionals and patients increases
Solution Approach 1:
The system automatically performs dosing calculations and generates dosing recommendations without requiring manual intervention from healthcare professionals or patients. It autonomously processes glucose data, applies dosing algorithms, and produces ready-to-use dosing instructions, significantly reducing the time and effort required for dosing decisions.
Solution Approach 2:
The system replaces manual mechanical calculations with automated computational algorithms. The dose guidance application uses computer-based algorithms to process glucose data and generate dosing recommendations, eliminating the need for manual mathematical calculations and reducing the time burden on users.
4Measurement precision
If dosing recommendations are provided without considering individual factors, then dosing guidance can be given quickly, but accuracy and reliability decrease
Solution Approach 1:
The system automatically learns and adapts to individual patient characteristics through the learning period, autonomously adjusting dosing recommendations based on observed glucose patterns and dosing responses. This self-adaptation capability improves dosing accuracy without requiring complex manual configuration by users.
Solution Approach 2:
The system performs preliminary learning during a dedicated learning period to establish individual patient parameters and dosing patterns before providing optimized dosing recommendations. This preliminary action enables the system to tailor future dosing advice to individual characteristics, improving accuracy while keeping the ongoing process simple for users.
Data Source
AI summary
Systems, devices and methods are provided for determining a medication dose for a patient or user. The dose determination can account for recent and/or historical analyte levels of the patient or user. The dose determination can also take into account other information about the patient or user, such as physiological information, dietary information, activity, and/or behavior. Many different dose determination embodiments are set forth, pertaining to a wide array of different aspects of the system or environment in which the embodiments can be implemented. Systems, devices and methods are provided for displaying information related to glucose levels, including a time in range display and a graph of analyte levels containing an identification of a pattern type of a segment of the day.