Leveraging Digital Twin Technology For Personalized Dementia Management In Aging Populations: A Case Study On Reducing Hospital Admissions

Dementia in aging populations is a growing global health challenge, often leading to frequent hospital admissions and placing a significant burden on healthcare systems. This study explores the application of Digital Twin technology to personalize dementia management and reduce hospitalizations among older adults. By creating a virtual health model that integrates real-time data from wearable sensors, electronic medical records, and behavioral metrics, we aim to enhance early intervention and preventive care for individuals with dementia. Using a simulated case study of a ۷۵-year-old patient with early-stage dementia, we modeled disease progression and tested targeted interventions. Our findings indicate that Digital Twins can predict potential health crises, such as falls or medication mismanagement, with high accuracy, enabling timely adjustments to treatment plans or environmental modifications. Preliminary results suggest that this approach could reduce hospital admissions by up to ۳۰%, significantly alleviating the strain on healthcare resources. However, challenges such as data privacy concerns, implementation costs, and the need for robust data integration remain critical barriers to widespread adoption. These findings highlight the transformative potential of Digital Twin technology in revolutionizing geriatric care, while also underscoring the ethical and practical considerations that must be addressed for its successful integration into healthcare systems.

کلیدواژه ها:

Digital Twin ، Dementia ، Aging Population ، Personalized Healthcare ، Hospital Admissions ، Preventive Interventions