Structured Biomarker Collection Workflow for Interpretable Patient Data
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Solution Overview
Problem
Current devices for collecting biomarkers in chronic disease management, such as diabetes, lack structured procedures and guidance, leading to uninterpreted data and patient burden, which can discourage further therapy optimization.
Innovation Solution
A portable hand-held device with a processor that initiates and manages a structured collection procedure, segregating data into primary and secondary stores, transforming it into evaluated objects for therapy optimization, and applying error checking, while providing contextualized data for clinicians.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If unstructured biomarker collection is performed, then patient burden is reduced, but data interpretative value is lost
Solution Approach 1:
The collection procedure is segmented into discrete events with specific entry and exit criteria. Each event represents a structured collection opportunity with defined parameters (biomarker type, collection time, contextual factors), allowing systematic data gathering while maintaining patient-friendly operation through automated device guidance.
Solution Approach 2:
Entry criteria are established before collection events to predefine the conditions under which biomarkers should be collected. This preliminary structuring ensures that data is gathered with inherent interpretative context (such as fasting state, meal timing, exercise status) without requiring complex patient decisions during the actual collection moment.
2Reliability
If multiple collection procedures are requested, then diagnostic completeness is improved, but patient burden increases
Solution Approach 1:
Multiple collection procedures are merged into a unified structured collection framework managed by a single device. The device consolidates multiple biomarker collection requests into coordinated events, eliminating redundant collections and providing a single point of control that reduces patient burden while maintaining comprehensive diagnostic coverage.
Solution Approach 2:
The system uses feedback mechanisms to monitor collection progress and adjust future collection requests based on completed events and obtained results. Exit criteria trigger subsequent collection events, creating an adaptive collection schedule that ensures diagnostic completeness while avoiding unnecessary重复 collections that would increase patient burden.
3Loss of information
If structured collection procedures are implemented, then data interpretative value is improved, but device complexity increases
Solution Approach 1:
The collection device performs self-service by automatically managing the structured collection procedure without requiring complex external system integration. The device independently evaluates entry and exit criteria, schedules collection events, prompts patients appropriately, and structures the data with contextual information, thereby achieving high data interpretative value while containing device complexity within a single portable unit.
4Ease of operation
If biomarker collection lacks contextual structure, then ease of collection is improved, but therapy optimization capability is reduced
Solution Approach 1:
The system changes parameters by collecting not only biomarker values but also contextual parameters (time of day, fasting status, meal composition, exercise intensity, stress levels). These parameter changes transform simple biomarker measurements into richly contextualized data points that enable precise therapy optimization while maintaining ease of collection through automated device prompting and patient-friendly interfaces.
Data Source
AI summary
A collection device for performing a structured collection procedure may include a processor that executes program instructions communicably coupled to at least one memory. The processor can initiate a schedule of events of the structured collection procedure upon one or more entry criterions being met and segregate the at least one memory into a primary data store and a secondary data store. The processor can write structured patient data collected in accordance to the schedule of events to the secondary data store. The processor can transform a relevant portion of the structured patient data into an evaluated data object. The processor can generate a data abstraction based in part upon the evaluated data object. The processor can link the primary data store and the secondary data store with the data abstraction.


