Abstract

The increasing prevalence of mental health issues among the elderly necessitates a comprehensive approach to diagnosis and treatment, with a specific focus on early intervention. This review paper presents a conceptual framework for integrating health data analytics into elderly mental health diagnosis workflows, aiming to enhance the accuracy and effectiveness of mental health care for older adults. The framework emphasizes four key components: data collection, processing, analysis, and decision-making, highlighting the importance of diverse health data sources, including electronic health records, behavioral data, and real-time data from wearable devices. By leveraging advanced analytical techniques such as machine learning and predictive analytics, healthcare providers can identify early warning signs of mental health disorders, allowing for personalized care and proactive intervention strategies. The paper concludes with recommendations for healthcare providers, policymakers, and researchers to adopt data-driven approaches that foster collaboration, improve diagnostic accuracy, and ultimately enhance the mental health outcomes of elderly populations.