Insulin Adherence Composite Scoring via Time-Window Binning
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Solution Overview
Problem
Current systems fail to provide effective and systematic feedback on insulin regimen adherence for diabetic patients, particularly in tracking adherence based on well-defined reference points in time, and do not adequately quantify the effects of adherence on patient health.
Innovation Solution
A method and system that compute and visualize insulin regimen adherence data by binning metabolic events into non-overlapping time windows, calculating adherence values, and combining them into a composite adherence value, providing a single representation of adherence that accounts for the impact of non-compliant meals, fasting events, and insulin injections on glucose levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional adherence tracking methods are used, then users can log their own data, but the system lacks reliability due to reliance on user expectations and manual logging accuracy
Solution Approach 1:
The system enables self-service by automatically collecting adherence data from metabolic events (glucose measurements, insulin injections, meals) without requiring user logging. The processor autonomously bins events into time windows, calculates adherence values, and generates composite scores, eliminating manual intervention while maintaining high reliability through objective physiological data.
2Measurement precision
If detailed metabolic event data is collected and analyzed, then adherence measurement precision improves, but system complexity increases
Solution Approach 1:
The system segments the complex adherence analysis task into manageable components: (1) binning metabolic events into non-overlapping time windows, (2) calculating individual adherence values for each time window, (3) combining these values into a composite adherence score. This segmentation enables precise measurement while keeping the processing system organized and manageable through modular operations.
Solution Approach 2:
The system introduces an intermediary processing layer that transforms raw metabolic event data into meaningful adherence metrics. The processor acts as an intermediary, automatically binning events, calculating adherence values, and generating composite scores, thereby simplifying the interface between complex physiological data and actionable adherence information for patients and providers.
3Loss of information
If adherence data is presented in detailed form, then information completeness improves, but ease of interpretation deteriorates
Solution Approach 1:
The system merges multiple individual adherence values from different time windows into a single composite adherence score. This combining process preserves the underlying detailed information while presenting a simplified, easily interpretable metric that allows patients and providers to quickly assess overall adherence status without being overwhelmed by granular data.
Data Source
AI summary
Systems and methods for evaluating insulin medicament dosage regimen adherence by a subject are provided. A description of metabolic events the subject engaged in is obtained. Each event comprises a timestamp and a classification that is one of insulin regimen adherent and insulin regimen nonadherent. Events are binned into consecutive time windows on the basis of time to obtain a plurality of subsets. Adherence values are computed. Each adherence value is for a subset and is computed by dividing the insulin regimen adherent events by the total number of events in the subset. Adherence values are combined into a composite value by a process that comprises downweighting a first adherence value, representing a first time window, with respect to a second adherence value, representing a second time window, when the first time window occurs in time before the second time window. The composite value is communicated as a single representation.