Dynamic Biomarker Collection Protocol for Structured Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current collection devices for chronic diseases like diabetes lack structured protocols for biomarker collection, leading to uninterpreted measurements and patient burden, often resulting in inefficient data collection and discouragement of further therapy optimization.
Innovation Solution
A method and device that utilize a processor and memory to classify similar biomarker samples, calculate expected values, and adjust collection protocols based on compliance with set criteria, ensuring contextualized data collection and providing guidance to patients through alarms, tutorials, and prompts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If unstructured biomarker collection is used, then patient burden is reduced, but data interpretability and clinical value deteriorate
Solution Approach 1:
The collection protocol is segmented into multiple discrete steps, each with specific guidance on what to measure, when to measure it, and how to record it. This segmentation provides structure to the collection process without overwhelming the patient, breaking down the complex task into manageable segments that improve both interpretability and patient experience.
Solution Approach 2:
The system performs preliminary actions by pre-defining the collection protocol, including what biomarkers to measure, under what conditions, and how to interpret the results. This preliminary structuring is done before the actual collection, so patients receive clear guidance without having to navigate complex decisions during the collection process itself.
2Loss of information
If multiple collection requests are made by different clinicians, then comprehensive data collection is improved, but patient burden and collection efficiency deteriorate
Solution Approach 1:
Multiple collection requests from different clinicians are merged into a single unified protocol. The system consolidates overlapping requests, eliminates redundant measurements, and integrates complementary data collection goals into one coordinated plan, thereby reducing patient burden while maintaining comprehensive data collection.
Solution Approach 2:
The collection protocol is designed to serve multiple clinicians and multiple purposes simultaneously. A single protocol can support diagnostic questions, therapy optimization, and monitoring needs from different providers, making the system universally applicable across various clinical scenarios without requiring separate collection plans for each clinician.
3Adaptability or versatility
If manual programming of collection schedules is required, then device flexibility is improved, but device complexity and ease of use deteriorate
Solution Approach 1:
The system performs self-service by automatically generating and managing the collection protocol based on the clinical questions and patient data provided. Instead of requiring manual programming, the system autonomously creates the schedule, reminds patients, and coordinates the collection process, thereby maintaining flexibility while eliminating programming complexity.
Solution Approach 2:
The system uses parameter changes to adapt the collection protocol dynamically. Rather than requiring manual reprogramming, the system adjusts collection timing, frequency, and parameters automatically based on patient responses, clinical progress, and protocol requirements, maintaining flexibility through automated parameter modification rather than manual configuration.
Data Source
Figure 1
Figure 2
Figure 2A
AI summary
Embodiments of methods of performing a structured collection protocol on a collection de- vice comprise providing a plurality of prior biomarker sample, wherein the prior biomarker samples comprise at least one measured value and plurality of contextualized data components linked to the prior biomarker samples, setting a first criterion, wherein the first criterion classifies prior biomarker samples as similar if prior biomarker samples share at least one identical contextualized data component, grouping biomarker samples that are determined to be similar based on the first criterion, calculating expected values for future biomarker samples which satisfy the first criterion, wherein the calculation is based on at least a subset of the group of similar prior biomarker samples, setting a second criterion, wherein the second criterion is an acceptable variance from the calculated expected values, a threshold, or both, collecting one or more biomarker samples which satisfy the first criterion, and evaluating via the processor the compliance of the collected biomarker samples with the second criterion.