A generative AI-enhanced intelligent service system with contextual retrieval and adaptive interaction for hospital use.

Depression is a widespread and serious public health concern. It is a common mental health condition that significantly impairs an individual’s psychological well-being and social functioning, ultimately diminishing their quality of life. It also imposes substantial emotional and financial burdens on the families of those affected. The condition is typically characterized by ongoing fatigue, a consistently low mood, loss of interest in activities, and difficulty concentrating []. The impact of depression extends beyond individual suffering, it is also associated with reduced quality of life, increased risk of chronic illness, and even elevated mortality rates []. Globally, this disorder affects more than 300 million people and has become the leading cause of disability worldwide [, ]. In the United States, depression is recognized as a major contributor to mortality, morbidity, disability, and economic costs []. Recent estimates indicate that approximately one in five U.S. adults (18.5%) has been diagnosed with a depressive disorder in their lifetime []. The annual estimated cost of treating depression in the US is reported to be about $210 billion []. According to the CDC, Tennessee ranks third among U.S. states in depression prevalence, with 24.4% of adults reporting a diagnosis of depression, surpassed only by Kentucky and West Virginia []. Without timely intervention, depression’s burden is projected to grow, and it is expected to become the leading cause of disease burden globally by 2030, underscoring the urgency for improved detection and prevention efforts []. Early detection of depression remains challenging, despite its high prevalence. Many individuals with depression go undiagnosed or untreated even in high-income countries, nearly half of people suffering from depression are not receiving the care they need [, ]. Contributing factors include social stigma, lack of routine mental health screening in primary care, and barriers to accessing behavioral health services []. Consequently, depression often remains hidden until it reaches a more severe stage, leading to prolonged suffering and higher risk of complications. This treatment gap highlights the need for proactive surveillance and community-level data to identify those at risk. Public health experts emphasize that early identification and intervention are essential to reducing depression’s long-term impact []. Robust surveillance data can help fill this gap by providing insights into who is affected and what risk factors are associated with depression in the general population, enabling health authorities to respond sooner with targeted outreach and resources. To monitor conditions like depression in the general adult population, public health agencies rely on surveillance systems that capture data beyond clinical settings. The Behavioral Risk Factor Surveillance System (BRFSS) is a cornerstone of such surveillance in the U.S., providing a source of recent data on health status and behaviors. BRFSS is a publicly available, ongoing state-based health survey that each year collects information from a large, representative sample of adults in every state on topics ranging from chronic conditions to health behaviors and mental health status []. These extensive scale and sampling design ensure that BRFSS data are broadly generalizable to the non-institutional adult population. BRFSS is the primary source of timely and accurate health data, used to identify emerging health problems and to inform the development and evaluation of interventions []. Notably, BRFSS includes measures related to mental health (such as lifetime self-reported depression diagnoses), making it a valuable tool for tracking depression prevalence and trends across communities. Additionally, the BRFSS collects extensive data on established risk factors for depression, including sociodemographic characteristics, general and mental health status, physical well-being, disability, and health behaviors such as tobacco and alcohol use []. It also captures information on comorbid conditions like cancer, hypertension, diabetes, stroke, heart attack, cognitive impairment, and adverse childhood experiences (ACEs), all of which are known to be associated with increased risk of depression [–]. This study capitalizes on the latest available data for the state of Tennessee and ensures that our analysis addresses the current scope of depression in the state’s adult population and thereby supporting contemporary public health planning.