EpiCare: Innovative Pediatric Medicine Delivery System for Epilepsy Management
by Ishita Srivastava
Coauthors: Jonanne Talebloo (UC Berkeley Student, Mi4 Intern of 1+ year)
Medical Devices & Digital Health
Effective medication administration is crucial for pediatric patients, particularly those with epilepsy, to achieve optimal treatment. However, challenges in accurate dosage administration and medication adherence often hinder therapeutic efficacy. To address these challenges, we are proposing the development of a novel pediatric medicine delivery system, specifically tailored for pediatric epilepsy. Epilepsy affects a large population of minors worldwide, with approximately 470,000 children diagnosed annually. Dosing errors are detrimental and are likely to occur far more in pediatric cases due to factors such as weight-based dosing, calculation errors, and lack of appropriate pediatric formulas.
This innovative pediatric medicine delivery system comprises a smart dispenser, a mobile application, and a cloud-based data management system. The smart dispenser utilizes advanced sensors, including weight sensors and image recognition, to accurately measure and dispense medication in various dosage forms. This ensures precise dosage administration, reducing the risk of under- or over- medication which can prevent fatal effects. The accompanying mobile application serves as a comprehensive tool for caregivers of children with epilepsy. It provides medication reminders, dosage instructions, and alerts for potential drug interactions or contraindications. Caregivers can track medication adherence, record seizure activity, and access educational resources specific to epilepsy management. By promoting adherence and empowering caregivers with valuable information, the application aims to enhance treatment outcomes and quality of life for pediatric epilepsy patients.
Furthermore, the cloud-based data management system securely stores and analyzes medication-related data, including adherence rates, seizure frequency, and treatment outcomes. Through data-driven insights, healthcare providers can remotely monitor medication adherence, identify patterns in adherence, and intervene promptly to optimize treatment strategies. This integrated approach enhances communication between caregivers and healthcare professionals, facilitating personalized care and enabling timely adjustments to medication regimens.
Additionally, a large amount of guesswork is currently used to determine which anti-seizure drug the patient will respond to, leading to a prolonged trial-and-error process. For new epilepsy patients, our product also consists of a deep-learning prediction model, utilizing clinical information from pediatric patients to accurately predict the dosage and type of anti-seizure medication needed.
The development of this system will undergo a rigorous design and engineering process. The system’s hardware components are engineered to ensure accurate medication measurement, user safety, and durability. Advanced image processing algorithms are employed to identify medication types, while machine learning techniques enable continuous improvement and customization based on individual patient needs.
The implementation of the proposed system has the potential to revolutionize pediatric medication administration, improving treatment outcomes and reducing human error. By combining sensing technology, mobile applications, and cloud-based data management, the system is able to provide accurate dosage administration, facilitate medication adherence, and enhance communication among caregivers and healthcare providers. Future research and development efforts will focus on refining the device’s functionalities, adding an interactive feature to gamify health education for the child (including an informative chatbox) conducting clinical trials, and applying it to a variety of disorders requiring dose-specific medications.