AI-Driven Medication Monitoring

Our client, a leading healthcare company in the UK, identified a critical issue: many patients purchase medications but often forget to take them as prescribed. This non-adherence negatively impacts both treatment outcomes and patient wellness. Recognizing the need for a reliable solution to monitor and remind patients about their medication intake, the client sought to develop a cutting-edge tool. Due to our high ranking on Clutch and our proven track record in the UK market, they chose UKAD to bring this vision to life.

Medication non-adherence is a common challenge in healthcare, with many patients forgetting to take their prescribed pills or discontinuing their use prematurely. The client aimed to create a robust system to remind patients about their medications and notify doctors of any missed doses. The solution needed to leverage advanced technology to ensure accuracy and user engagement, handle a high volume of users, and comply with stringent UK healthcare industry standards.

UKAD conducted a thorough business analysis and developed a proof of concept (POC) to validate the proposed solution. We designed and developed a neural networks-powered mobile application using .NET Core, Python, WebcamJS, OpenCV, Yolo, and CNN Darknet technologies. The app utilizes machine learning algorithms to analyze various types of medication blisters, identifying missed doses through image recognition.

Key Features:

• Computer Vision and Machine Learning: By utilizing YOLO real-time object detection and CNN Darknet, the app scans and analyzes blister packs to detect whether a pill has been taken. This process ensures accurate tracking and reduces manual input from patients.
• Notifications and Reminders: If a patient forgets to scan their blister pack or misses a dose, the app sends automated notifications. Persistent non-adherence triggers a follow-up call to ensure patient compliance.
• High Volume Handling: The platform is designed to support a large number of users, ensuring scalability and reliability.
• Compliance with Healthcare Standards: The app meets all UK healthcare industry standards, ensuring data security and patient confidentiality.

The mobile application has been successfully deployed, providing a seamless experience for both patients and healthcare providers. Patients receive timely reminders to take their medication, while doctors can monitor adherence and intervene when necessary. The extensive training of the model to recognize various blister pack types has resulted in high accuracy and user satisfaction.

UKAD's expertise in Python development and our commitment to leveraging the latest technologies have been pivotal in the success of this project. Our business analysis and POC development services ensured that the solution was both innovative and practical, addressing the client's needs effectively.

This project underscores UKAD's ability to deliver high-quality healthcare solutions, combining advanced machine learning techniques with user-friendly mobile applications.