Glucose Data Analysis Software for Automated Diabetes Therapy

Solution Overview

Problem

Current diabetes management systems lack efficient tools for processing glucose data based on clinical standards, requiring high expertise and being time-consuming, especially in providing effective therapy decisions and support guidance for healthcare providers and patients.

Innovation Solution

A software application integrated with analyte measurement devices that analyzes glucose data using clinically-rational scales and algorithms to provide expert therapy recommendations, automate data analysis, and optimize self-monitoring schedules, enabling non-experts to make informed decisions.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Loss of information

If conventional software graphs are provided for glucose data analysis, then data visualization is achieved, but time consumption increases and expertise requirement increases

Engineering Contradiction:

Improvedata analysis effectivenessVSAvoidtime consumption

Core Design Contradiction:

Loss of informationVSLoss of time

Solution Approach 1:

The system automatically analyzes glucose data and generates therapy recommendations without requiring manual intervention or expert analysis. The automated algorithm processes the data and provides actionable insights, enabling the system to serve itself rather than requiring continuous human expertise.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The invention transforms raw glucose data into standardized clinical parameters and risk assessments. By converting complex data patterns into simplified clinical recommendations, the system reduces the expertise required while maintaining analytical effectiveness.

Inventive Principle:

Principle #35Parameter changes

2Loss of information

If conventional software graphs are provided for glucose data analysis, then data visualization is achieved, but expertise requirement increases

Engineering Contradiction:

Improvedata analysis effectivenessVSAvoidexpertise requirement

Core Design Contradiction:

Loss of informationVSEase of operation

Solution Approach 1:

The system automatically analyzes glucose data and generates therapy recommendations without requiring manual intervention or expert analysis. The automated algorithm processes the data and provides actionable insights, enabling the system to serve itself rather than requiring continuous human expertise.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The invention transforms raw glucose data into standardized clinical parameters and risk assessments. By converting complex data patterns into simplified clinical recommendations, the system reduces the expertise required while maintaining analytical effectiveness.

Inventive Principle:

Principle #35Parameter changes

3Productivity

If automated therapy recommendations are provided, then productivity increases, but reliability may decrease without expert review

Engineering Contradiction:

Improvetherapy decision speedVSAvoiddecision accuracy

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The system incorporates feedback mechanisms that allow continuous learning and improvement. By analyzing outcomes and adjusting recommendations based on patient responses, the system maintains high reliability while preserving automated decision-making speed.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS20210090737A1Method of hypoglycemia risk determination

Publication Date: 2021.03.25 ABBOTT DIABETES CARE INC

AI summary

Presented herein are one or more software applications to help a user manager their diabetes. Embodiments and descriptions of the various applications are provided below in conjunction with an analyte measurement device.