Empowering Older Adults with AI Agent-driven Medication Management: Enhancing Adherence, Independence, and Health Outcomes

Abstract

As the aging population grows, the healthcare system faces challenges in meeting the unique needs of older adults. Medication non-adherence among older adults is a critical issue that leads to severe health complications and reduced independence. Traditional medication management tools often fail to address this problem due to their technological limitations, complexity, lack of personalization, and poor accessibility. These shortcomings make it challenging for older adults to manage complex medication routines, increasing health risks and dependence on caregivers. Research indicates that approximately 40% of older adults struggle with medication adherence due to memory issues, complex routines, and accessibility barriers. This study aims to design an AI Agent-driven medication management solution to empower older adults by simplifying their medication routines and reducing cognitive and physical strain. The proposed system aims to improve medication adherence, promote greater independence, and minimize caregiver reliance through personalized and accessible support. A human-centered design methodology guided this research with initial qualitative user studies through interviews and surveys to identify gaps in existing solutions. Quantitative scenarios-based testing evaluated the system’s impact on user confidence and accessibility. The system integrates advanced AI capabilities, including real-time contextual processing and multimodal integration, which ensures adaptability to user needs and seamless interactions. The proposed solution delivers a personalized, user-centered experience that simplifies medication routines for older adults by employing AI technologies, including large language models (LLMs), large multimodal models (LMLs), and other existing tools, such as various apps on the phone and computer to function effectively with contextual information. Key features include medication reminders, meal planning, activity suggestions, and family monitoring tailored to an individual’s health condition, prescribed medication, avoiding dietary lists, recent grocery shopping data, current weather, and other necessary contextual factors for a patient’s health management. This holistic system promotes medication adherence and supports broader health management needs for an active lifestyle. The project focuses on iterative refinement guided by user feedback to create a design that provides usability and accessibility to users. Results from user testing show a significant rise in confidence among the target users, who reported feeling more comfortable and independent in managing their health with the AI-powered medication management solution. This study emphasizes the potential of AI to reduce health risks among older adults by promoting their medication adherence. By incorporating AI potential with human-centered design, this solution proves how to bridge the gap between user needs and technological possibilities. This system approach improves health management needs and independence and reduces caregiver burden, encouraging a balanced support system. These findings highlight the broader implications of AI in healthcare, offering a scalable and impactful model for addressing the challenges of an aging population and providing a pathway for context-based AI assistive solutions in UX design that improve the quality of life for older adults.

Keywords: AI-driven Healthcare, Medication Adherence, Aging Population, Older Adults, Large Language models (LLMs), Accessible Technology

DOI: 10.54941/ahfe1006434