Chronic Care Treatment Control with Error Correction
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Solution Overview
Problem
Current glucose control methods for diabetic patients are hindered by high variability in blood glucose levels due to inaccurate estimation of treatment parameters, such as food intake and physical activity, leading to repetitive errors and complex counteractive actions, with existing solutions failing to provide a systematic basis for accurate estimation and correction.
Innovation Solution
A method and apparatus that record and track arbitrary factor types, analyze response errors, and isolate errors in factor correlations to refine estimation and improve treatment outcomes through a continuous feedback system, enabling patients to form a consistent basis for managing blood glucose levels effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If patients manually estimate treatment parameters (food intake, physical activity), then they can manage their diabetes with available tools, but estimation accuracy deteriorates leading to high blood glucose variability
Solution Approach 1:
The system continuously monitors actual blood glucose levels and compares them with predicted levels based on estimated treatment parameters. This feedback loop identifies estimation errors and adjusts future predictions, progressively improving accuracy while maintaining ease of use for patients.
Solution Approach 2:
The system automatically performs complex calculations and error analysis without requiring patient intervention. It self-corrects estimation errors by analyzing the relationship between treatment parameters and actual glucose responses, providing improved accuracy while keeping the patient interface simple.
2Productivity
If patients make repetitive estimates about treatment parameters, then they can control blood glucose levels, but error accumulation increases leading to poor control
Solution Approach 1:
The system analyzes the relationship between repeated treatment parameter inputs and actual blood glucose responses to identify systematic errors. This feedback mechanism allows the system to learn from repetitive patterns and improve the reliability of glucose control predictions over time.
Solution Approach 2:
The system dynamically adjusts prediction parameters based on observed patterns in patient data. By changing the internal models based on actual responses to repetitive treatments, the system maintains high productivity while improving reliability through adaptive parameter refinement.
3Measurement precision
If complex mathematical models are used to predict metabolic response, then prediction accuracy improves, but system complexity increases making communication difficult
Solution Approach 1:
The complex mathematical model is segmented into multiple simpler sub-models, each handling a specific aspect of metabolic response (e.g., food intake, exercise, medication). This segmentation maintains prediction accuracy while reducing overall system complexity and improving communicateability with patients and providers.
Solution Approach 2:
The system introduces an intermediary layer between complex mathematical models and user interface. This intermediary translates complex model outputs into simple, actionable insights for patients and providers, maintaining high prediction accuracy while hiding the underlying complexity from end users.
4Ease of manufacture
If standardized databases of food and activities are used, then estimation basis is provided, but accuracy deteriorates for arbitrary or custom factors
Solution Approach 1:
The system combines standardized databases with flexible, adaptive modeling capabilities. It can handle both standardized factors (using database values) and arbitrary/custom factors (using learned patterns from patient data), providing a universal solution that maintains ease of use while improving accuracy for diverse scenarios.
Solution Approach 2:
The system transitions from static standardized databases to dynamic, adaptive models that learn from patient-specific responses. This allows the system to maintain the simplicity of standardized approaches for common factors while dynamically adapting to handle arbitrary and custom factors with high accuracy based on individual patient patterns.
Data Source
AI summary
A method and apparatus to record and track patient's estimation of arbitrary factor types, to analyze response errors utilizing discrete measurements, to isolate errors in various factor types and their response correlations, to enable patient in refining factor mix to reduce estimated outcome variations, and to improve patient estimation with corrections using a continuous feedback system. The input factors for the treatment in case of a Diabetic patient may be food intake, medication, activity, stress level etc, while the treatment outcome or response may be blood glucose level that the patient needs to keep within specific limits. Present invention enables a chronic care patient, such as a Diabetic patient, to make better and informed decisions about their treatment, reduce input factor estimation errors, reduce outcome parameter variations, and improve the patient well being by better treatment control.