Smart Injection Pen Dosing With Automated Adherence Tracking
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Solution Overview
Problem
Existing systems lack an automated and reliable way to track and communicate medication doses, particularly for injection-based therapies, leading to potential missed or incorrect doses in chronic conditions like diabetes, and conventional dose calculators require manual estimation which is cumbersome for users.
Innovation Solution
An intelligent medicine administering system comprising an injection pen device in wireless communication with a companion device, which includes a dose setting mechanism, dispensing mechanism, sensor unit, and electronics unit to track and recommend doses based on health and contextual data, and a software application to autonomously calculate and display recommended doses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If patients manually track and manage their medication schedules, then they maintain some level of medication intake, but they experience medication errors, missed doses, and inconsistent adherence due to human factors
Solution Approach 1:
The system enables automatic medication tracking where the smart device detects medication intake events and automatically logs them without requiring patient input. The system self-monitors adherence patterns, sends automated reminders, and provides feedback to both patients and providers, eliminating the need for manual tracking while improving reliability.
Solution Approach 2:
The system implements continuous feedback loops where medication adherence data is automatically collected, analyzed, and communicated back to patients through the mobile application and to providers through the web portal. This feedback mechanism reinforces adherence behavior and allows for real-time intervention when issues arise.
2Measurement precision
If comprehensive patient data is collected and analyzed to provide personalized recommendations, then dosing accuracy and personalization improve, but data privacy risks and system complexity increase
Solution Approach 1:
The system architecture is segmented into distinct modules: data collection layer (mobile app), data processing layer (analytics engine), and delivery layer (smart device and web portal). This segmentation allows comprehensive data collection for precise dosing recommendations while managing complexity through modular design, where each segment handles specific functions independently.
Solution Approach 2:
The system employs intermediaries including encrypted data transmission protocols, secure cloud-based data storage, and algorithmic processing layers that mediate between raw patient data and dosing recommendations. These intermediaries protect data privacy while enabling precise analysis, and they simplify the interface between data collection and clinical decision-making.
3Reliability
If real-time monitoring and automated alerts are implemented, then medication adherence improves, but device complexity and power consumption increase
Solution Approach 1:
The system uses periodic monitoring intervals rather than continuous monitoring, where the smart device checks medication status at scheduled times (e.g., at prescribed dosing intervals). Automated alerts are sent periodically based on adherence patterns rather than continuously. This periodic approach maintains reliable monitoring while significantly reducing power consumption compared to continuous operation.
Data Source
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AI summary
Systems, devices, and techniques are disclosed for administering and tracking medicine to patients and providing health management capabilities for patients and caregivers. In some aspects, a method includes receiving one or more analyte values associated with a health condition of the patient user; receiving contextual data associated with the patient user obtained by the mobile computing device, where the obtained contextual data includes information associated with a meal; determining a medicine metric value associated with an amount of medicine active in the body of the patient user; autonomously calculating a dose of the medicine without input from the user based at least on the one or more analyte values, the medicine metric value, and the information associated with a meal; and continuously displaying the calculated dose of the medicine.