Basal Insulin Adherence Evaluation Using Missing Dose Response
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Solution Overview
Problem
Existing insulin titration systems face challenges in accurately determining treatment adherence due to missing dose data, which can be caused by technical issues or non-adherence, leading to clinical inertia and poor glycemic control.
Innovation Solution
A diabetes management system that uses a dose response algorithm to calculate expected fasting blood glucose values based on available data, distinguishing between non-adherence and missing data points by analyzing fasting blood glucose history and insulin injection history, and providing insulin dose recommendations accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If insulin titration systems use connected injection devices to track treatment adherence, then treatment monitoring capability is improved, but data completeness and reliability deteriorate due to missing dose data from technical issues or non-adherence
Solution Approach 1:
The system implements feedback by using glucose measurements to infer adherence status. When injection data is missing, the system analyzes subsequent glucose levels to determine whether the patient likely took the insulin (glucose decreased as expected) or skipped it (glucose remained high). This feedback loop allows the system to compensate for missing injection data by using glucose response as indirect evidence of adherence.
Solution Approach 2:
The system uses glucose measurements as an intermediary to bridge the gap between missing injection data and adherence determination. Instead of directly relying on injection device data alone, the system introduces glucose levels as a mediator that provides indirect information about whether insulin was taken, allowing adherence assessment even when direct injection data is unavailable.
2Measurement precision
If the system requests frequent glucose measurements to monitor adherence, then adherence detection accuracy is improved, but patient burden and loss of time increase
Solution Approach 1:
The system applies partial action by requesting glucose measurements selectively rather than continuously. Instead of demanding frequent measurements at all times, the system requests glucose data primarily when adherence is in question or at routine intervals, using measurements strategically to resolve adherence uncertainty without imposing continuous monitoring burden on patients.
3Reliability
If the system makes frequent dose recommendations to optimize glycemic control, then glycemic control improvement is achieved, but clinical inertia increases due to treatment optimization barriers
Solution Approach 1:
The system enables self-service by providing automated dose recommendations based on analyzed data patterns. The system autonomously processes injection data, glucose measurements, and adherence assessments to generate dosing recommendations without requiring complex manual clinical decision-making, thereby reducing the complexity burden on both patients and providers while maintaining reliable glycemic control.
Data Source
AI summary
A diabetes management system adapted to determine adherence for a subject in treatment according to a basal insulin regimen, the system being adapted to receive regimen data. BG data and insulin injection data. If one or more insulin injections have not been received in accordance with the prescribed regimen and thus are missing, the system is adapted to calculate for each missing injection an expected dose response BG value. By comparing received BG data, corresponding to the missing insulin injections, it can be determined for a given confidence interval whether or not the subject has been in adherence with the basal insulin regimen.