Blood Glucose Data Management and GUI for Adaptive Dosing
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Solution Overview
Problem
Patients often fail to comply with physician treatment instructions for managing blood glucose levels, leading to improper dosage of medication and prolonged illness, and there is a lack of dynamic healthcare information and support for self-titration.
Innovation Solution
A computer-implemented method using machine learning algorithms to generate and revise treatment plans based on user data, including blood glucose measurements, medication timing and dosage, exercise, and dietary intake, with real-time notifications and personalized recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If physicians provide treatment instructions manually, then patient care is personalized, but patient compliance is low and physician workload is high
Solution Approach 1:
The system enables patients to self-monitor blood glucose levels using a glucose meter, automatically log data through the mobile application, and receive automated treatment recommendations based on their measurements. This self-service approach increases compliance by empowering patients to manage their own condition without requiring constant physician intervention.
Solution Approach 2:
The system provides continuous feedback to patients through the mobile application, showing their blood glucose trends, compliance status, and personalized recommendations. The system also provides feedback to physicians through automated reports, enabling them to adjust treatment plans based on objective data without manual patient reporting.
2Loss of information
If physicians monitor patient compliance manually during visits, then treatment instructions can be adjusted, but healthcare costs increase and physician-patient interactions are limited
Solution Approach 1:
The system collects and analyzes compliance data continuously before physician visits through automated monitoring and patient logging. This preliminary data collection ensures that when physicians do review compliance information, they have comprehensive, pre-processed data ready, eliminating the need for manual data gathering during limited visit times.
Solution Approach 2:
The mobile application and automated monitoring system serve as an intermediary between patients and physicians, continuously collecting compliance data and transmitting it to physicians. This intermediary handles the tedious data collection and organization tasks, allowing physicians to focus on clinical decision-making during their limited interactions with patients.
3Manufacturing precision
If insulin dosage is adjusted iteratively through titration, then proper dosage can be achieved, but treatment time is extended and patient burden increases
Solution Approach 1:
The system dynamically adjusts insulin dosage recommendations based on real-time blood glucose measurements and trends. Rather than following a fixed iterative titration schedule, the system adapts dosage recommendations continuously based on the patient's actual glucose levels, meal intake, and activity data, accelerating the process of finding the optimal dosage.
Solution Approach 2:
The system changes multiple parameters simultaneously during dosage adjustment, including insulin dosage, timing relative to meals, and carbohydrate intake recommendations. This multi-parameter approach allows the system to converge on the optimal treatment regimen more quickly by adjusting several factors in coordination rather than changing one parameter at a time through traditional titration.
Data Source
AI summary
Methods and devices include database management and graphical user interfaces for measurements collected by analyzing blood.