Structured Biomarker Collection Device for Chronic Disease Management
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Solution Overview
Problem
Current devices for chronic disease management, such as diabetes, lack structured guidance and functionality for biomarker collection, leading to unstructured data collection that reduces interpretative value for clinicians and discourages patients from seeking further therapy optimization.
Innovation Solution
A portable hand-held device with a structured collection system that contextualizes biomarker data by linking it to surrounding conditions, such as time, food, and exercise, and provides adherence criteria to ensure data quality, thereby optimizing therapy management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If unstructured biomarker collection is used, then ease of operation is improved, but measurement precision and interpretative value deteriorate
Solution Approach 1:
The system pre-configures structured collection procedures with defined protocols, schedules, and contextual parameters before data collection begins. This preliminary structuring guides patients through standardized measurement routines, ensuring consistent data quality and interpretative value while maintaining ease of use through automated guidance.
Solution Approach 2:
The collection device acts as an intermediary between the patient and the clinical decision-making process. It structures the data collection process by automatically prompting for contextual information, validating entries, and organizing measurements according to predefined protocols, thereby enhancing interpretative value without increasing patient burden.
2Reliability
If multiple collection schedules are requested by different clinicians, then completeness of data collection is improved, but device complexity and patient burden increase
Solution Approach 1:
The system merges multiple collection schedules from different clinicians into a single integrated calendar view. It automatically detects overlaps, consolidates redundant measurements, and presents a unified schedule to the patient, thereby maintaining data completeness while reducing apparent complexity and patient burden.
Solution Approach 2:
The system provides feedback to both patients and clinicians about collection schedule conflicts and data completeness status. It notifies patients of upcoming measurements, alerts clinicians when scheduled collections overlap or conflict, and tracks completion status, enabling proactive management of multiple schedules without increasing device complexity.
3Measurement precision
If structured collection procedures are implemented, then measurement precision and data quality are improved, but ease of operation deteriorates
Solution Approach 1:
The collection device provides self-service functionality by automatically guiding patients through structured collection procedures. It prompts for required measurements, validates entries in real-time, and organizes data according to protocol requirements without requiring patient knowledge of the underlying structure, thereby maintaining ease of operation while ensuring high data quality.
4Ease of operation
If biomarker collection lacks contextual information, then ease of operation is improved, but loss of information increases
Solution Approach 1:
The system segments the data collection process into distinct contextual categories (e.g., time of day, meal status, physical activity, medication intake). Each measurement is automatically tagged with relevant contextual segments based on patient responses to targeted prompts, preserving comprehensive information while maintaining a simple, step-by-step user interface.
Data Source
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AI summary
In one embodiment, a method of performing a structured collection protocol by utilizing a collection device comprising a processor may include collecting at least one sample using the collection device. The at least one sample can be associated with biomarker data. A classification can be associated with the at least one sample via the processor. The classification can be based upon an intended use of the at least one sample. Compliance with an adherence criteria for the at least one sample can be determined via the processor. When the at least one sample is not compliant with the adherence criteria, an adherence event can be associated with the at least one sample, and the processor can perform at least one additional task that is based upon the classification associated with the at least one sample.