Insulin Dosage Assessment System for Glycemic Control
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Solution Overview
Problem
Current methods for managing insulin dosages in diabetes patients lack effective mechanisms for adjusting basal and bolus insulin therapies based on real-time blood glucose data, leading to suboptimal glycemic control and requiring frequent manual adjustments by healthcare professionals.
Innovation Solution
A computer-implemented method that analyzes patient data to identify predominant bolus and basal outcomes relative to a target blood glucose range, recommending dosage adjustments by calculating percentages and generating recommendations to maintain, increase, or decrease insulin dosages, and employing structured testing procedures to validate insulin therapy effectiveness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual insulin dosage adjustments are made frequently by healthcare professionals, then glycemic control can be maintained, but the workload and complexity of diabetes management increases
Solution Approach 1:
The system enables self-service by automatically analyzing blood glucose data and generating insulin dosage recommendations without requiring manual intervention from healthcare professionals. The processor automatically identifies patterns, determines outcomes, and provides actionable recommendations, allowing the diabetes management system to serve itself in optimizing insulin therapy.
Solution Approach 2:
The system implements feedback by continuously monitoring blood glucose measurements, comparing actual outcomes against target ranges, and using this information to generate refined insulin dosage recommendations. The system processes historical data and provides feedback loops that adjust recommendations based on observed glycemic responses to previous insulin administrations.
2Ease of operation
If personalized insulin dosage recommendations are provided, then patient adherence to therapeutic regimens improves, but the need for frequent manual adjustments by healthcare professionals increases
Solution Approach 1:
The system performs preliminary action by proactively analyzing blood glucose data trends and generating insulin dosage recommendations before manual review is needed. The processor continuously processes data in the background, identifying patterns and preparing recommendations in advance, so that when healthcare professionals or patients review the data, actionable insights are already available, reducing the need for reactive manual adjustments.
3Reliability
If real-time blood glucose data is analyzed continuously, then glycemic control optimization improves, but the processing complexity and data requirements increase
Solution Approach 1:
The system applies extraction by isolating and focusing analysis on specific, clinically relevant parameters from the blood glucose data stream. Rather than processing all possible data points equally, the processor extracts key metrics such as time-in-range, glucose excursions, and pattern recognition, simplifying the analysis while maintaining optimization effectiveness. This selective extraction reduces processing complexity while preserving clinical utility.
Data Source
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AI summary
A computer-implemented method is presented for recommending insulin dosage adjustments for a patient having diabetes. The method includes: identifying a plurality of bolus events from patient data; grouping bolus events having a recommended bolus dosage substantially equivalent to the amount of administered insulin into a first subset of bolus events; determining a bolus outcome for each of the bolus events in the first subset of bolus events, where the bolus outcome is expressed in relation to a target range of blood glucose values and is selected from a group including above the target range, in the target range and below the target range; determining whether one of the bolus outcomes is predominant amongst the bolus events in the first subset of bolus events; and generating a recommendation pertaining to insulin dosage for the patient in response to a determination that one of the bolus outcomes is predominant.