AI-Driven Continuous Improvement Frameworks for Reducing Medication Waste in Geriatric Care

Abstract

Medication waste in geriatric care is a critical challenge that impacts healthcare costs, patient safety, and environmental sustainability. The complexities of medication management in aging populations, including overprescription, improper disposal, and medication non-adherence, contribute significantly to this issue. AI-driven continuous improvement frameworks offer a promising solution by optimizing medication management processes, reducing errors, and enhancing resource utilization. By leveraging predictive analytics, automation, and machine learning, AI can improve prescription accuracy, monitor real-time medication use, and personalize treatment plans. Additionally, AI-powered systems enable proactive interventions, reducing medication wastage and improving patient outcomes. This paper explores various AI-driven strategies for minimizing medication waste, examining real-world applications and case studies that demonstrate their effectiveness. Furthermore, the challenges associated with implementing AI in geriatric care, such as data privacy, ethical concerns, and adoption barriers, are discussed. The findings suggest that AI-driven frameworks have the potential to revolutionize medication management in geriatric care, paving the way for a more sustainable and efficient healthcare system.