Medication Container Code Verification for Adaptive Dosing
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Solution Overview
Problem
Existing systems fail to track a patient's symptoms while taking medications and adjust prescriptions and treatment plans accordingly, lacking the ability to provide valuable clinical insights and risk identification for health insurance purposes.
Innovation Solution
A system that monitors medication compliance through a container with a code accessible only upon opening, using a database to associate the code with the patient, and employs computer vision and AI to confirm medication intake, adjusting prescriptions based on patient feedback and symptoms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If a system only tracks medication compliance without tracking symptoms, then the system is simple, but it cannot provide valuable clinical insights or adjust prescriptions effectively
Solution Approach 1:
The system combines multiple functions into a single integrated platform: tracking medication compliance through code scanning, monitoring patient symptoms via mobile app inputs, analyzing data with AI algorithms, and providing clinical insights for prescription adjustments. This multi-functional approach resolves the contradiction by capturing comprehensive information (improving clinical insight) while consolidating functions into one system (managing complexity).
Solution Approach 2:
The system implements continuous feedback loops where patient symptom data and compliance information are collected, analyzed by AI, and used to generate actionable clinical insights that feed back to healthcare providers for prescription adjustments. This feedback mechanism ensures comprehensive data capture improves clinical decision-making without creating unmanageable system complexity.
2Productivity
If manual prescription adjustment is used, then the process is simple to implement, but it is time-consuming and less accurate
Solution Approach 1:
The system replaces manual mechanical processes (physicians manually reviewing compliance data and adjusting prescriptions) with automated AI algorithms that analyze patient data, track compliance through code scanning, and generate prescription adjustment recommendations. This substitution dramatically improves productivity and accuracy while the modular AI architecture manages the complexity of automation.
Solution Approach 2:
The system enables self-service capabilities where the automated platform independently performs data collection, analysis, and generates prescription adjustment recommendations without requiring constant manual intervention. The AI system serves itself by continuously learning from patient data and improving its analytical capabilities, thereby improving efficiency while containing automation complexity through self-managing algorithms.
3Reliability
If codes are accessible without container opening, then the system is easier to use, but it cannot verify actual medication intake
Solution Approach 1:
The system performs preliminary actions by placing the verification code inside the container before medication distribution, ensuring the code is only accessible after the container is opened. This preliminary placement of the code guarantees that code scanning occurs only after actual medication access, thereby verifying true compliance without compromising ease of use through the simple scan-to-verify process.
Solution Approach 2:
The code acts as an intermediary element between the container and the verification system. By placing the code inside the container, it serves as a mediator that can only be accessed after the container is opened, providing reliable verification of medication access. The scanning device then reads this intermediary code to confirm compliance, maintaining both reliability and ease of operation through this intermediary mechanism.
Data Source
AI summary
A system for tracking medication compliance having a container having medication and a code on an interior of the container, the code only accessible once the container is opened, a database associating the code with the medication and a patient, a computer in data communication with the database, the computer receiving the code from a patient device, software executing on the computer confirming that the patient took the medication by comparing the code received from the patient device with codes in the database, a healthcare device in data communication with said computer, software executing on the healthcare device receiving confirmation that the patient took the medication from the computer, and software executing on the healthcare device determining a potential adjustment for a future dose of medication for the patient based on the confirmation.