Integrating Patient Care: Evaluating Effectiveness of AI and IoT-enabled Solutions in Enhancing Health Outcomes for Geriatric Patients

Abstract

The aging global population is generating new challenges to healthcare systems, specifically concerning problems peculiar to geriatric patients. Older adults are more likely to have poor health outcomes such as frailty, falls, depression or loneliness, and hospitalization. Until now, healthcare professionals have relied on regular clinical assessments to find outpatient problems. However, these methods of diagnostics often miss dynamic and multidimensional features of the risks. Incorrectly identified challenges frequently lead to interventions that could be delayed or not very successful.  However, the development of the Internet of Things (IoT) enables potential applications for real-time health monitoring through wearable devices, smart bed sensors, and other connected technologies. These technologies can provide continuous, high-resolution data on critical metrics such as mobility, physical activity, sleep patterns, and environmental factors. Despite the above, including IoT data in mainstream clinical databases and its improved usage in predictive healthcare models is a significant gap in the research arena. The study focuses on the flaws of the existing models for predicting health outcomes among the elderly, the issues with integrating IoT-derived metrics with the classical health indicators, and the barriers to IoT adoption. The study seeks a broader perspective by constructing a comprehensive framework for forecasting and enhancing health outcomes in elderly populations through the use of IoT technologies along with machine learning techniques while solving usability and integration problems.