Literature Collection

High-need, high-cost (HNHC) adults require comprehensive strategies that address both clinical and social determinants of health (SDOH). This retrospective, propensity score-matched study (n = 526) evaluated a care model integrating monthly SDOH screenings, medication oversight, and real-time admission-discharge-transfer alerts in four urban primary care clinics. Compared to usual care, the intervention significantly reduced acute utilization within 60 days: emergency department (ED) visits decreased by 0.17 (P < 0.001) and hospital admissions declined by 0.12 (P < 0.001). Gross per-participant costs fell from $6,019 to $2,422 (a $3,597 reduction); after accounting for intervention expenses, net savings reached $3,222 (P < 0.001), yielding an estimated 6.9:1 return on investment. Patient-reported outcomes also demonstrated significant gains: EQ-5D-5L scores increased by 0.082 (P < 0.001) in the intervention cohort, exceeding the threshold for clinically meaningful change, while Net Promoter Scores rose by 8.8 points (P < 0.001). Subgroup analyses revealed slightly smaller quality-of-life gains among non-White cohorts, highlighting the need for culturally tailored approaches to advance equity. These findings align with prior Population Health Management research showing that integrated care models can reduce costs and enhance patient satisfaction. Overall, this multifaceted model effectively curbs avoidable ED visits and admissions, generates short-term cost savings, and boosts patient satisfaction-key outcomes under value-based care contracts. Future research should investigate longer-term outcomes and refine equity-focused strategies to ensure sustained and inclusive benefits.

BACKGROUND: Mental health legislation (MHL) is required to ensure a regulatory framework for mental health services and other providers of treatment and care, and to ensure that the public and people with a mental illness are afforded protection from the often-devastating consequences of mental illness. AIMS: To provide an overview of evidence on the significance of MHL for successful primary care for mental health and community mental health servicesMethod: A qualitative review of the literature on the significance of MHL for successful primary care for mental health and community mental health services was conducted. RESULTS: In many countries, especially in those who have no MHL, people do not have access to basic mental health care and treatment they require. One of the major aims of MHL is that all people with mental disorders should be provided with treatment based on the integration of mental health care services into the primary healthcare (PHC). In addition, MHL plays a crucial role in community integration of persons with mental disorders, the provision of care of high quality, the improvement of access to care at community level. Community-based mental health care further improves access to mental healthcare within the city, to have better health and mental health outcomes, and better quality of life, increase acceptability, reduce associated social stigma and human rights abuse, prevent chronicity and physical health comorbidity will likely to be detected early and managed. CONCLUSION: Mental health legislation plays a crucial role in community integration of persons with mental disorders, integration of mental health at primary health care, the provision of care of high quality and the improvement of access to care at community level. It is vital and essential to have MHL for every country.

Building on Seddio et al.'s study of ADHD symptoms, anxiety, and internalizing behaviors among college students during COVID-19, we identify key methodological limitations and propose refinements. The study's cross-sectional design, small sample size (n=200) from a single northeastern institution, high ADHD prevalence (35%), gender imbalance (82.1% female), reliance on self-report measures, and lack of ADHD subtype differentiation limit its generalizability and clinical applicability. We recommend standardized mental health screenings for internalizing behaviors, integrated care pathways within student health services, and faculty training to recognize subtle signs of distress. Future research should adopt longitudinal designs with multi-institutional cohorts, control groups, and diverse demographics to better understand comorbid ADHD and anxiety during acute stress. These improvements would strengthen the evidence base for supporting collegiate mental health.

PURPOSE: Timely identification of comorbidities is critical in sleep medicine, where large language models (LLMs) like ChatGPT are currently emerging as transformative tools. Here, we investigate whether the novel LLM ChatGPT o1 preview can identify individual health risks or potentially existing comorbidities from the medical data of fictitious sleep medicine patients. METHODS: We conducted a simulation-based study using 30 fictitious patients, designed to represent realistic variations in demographic and clinical parameters commonly seen in sleep medicine. Each profile included personal data (eg, body mass index, smoking status, drinking habits), blood pressure, and routine blood test results, along with a predefined sleep medicine diagnosis. Each patient profile was evaluated independently by the LLM and a sleep medicine specialist (SMS) for identification of potential comorbidities or individual health risks. Their recommendations were compared for concordance across lifestyle changes and further medical measures. RESULTS: The LLM achieved high concordance with the SMS for lifestyle modification recommendations, including 100% concordance on smoking cessation (κ = 1; p < 0.001), 97% on alcohol reduction (κ = 0.92; p < 0.001) and endocrinological examination (κ = 0.92; p < 0.001) or 93% on weight loss (κ = 0.86; p < 0.001). However, it exhibited a tendency to over-recommend further medical measures (particularly 57% concordance for cardiological examination (κ = 0.08; p = 0.28) and 33% for gastrointestinal examination (κ = 0.1; p = 0.22)) compared to the SMS. CONCLUSION: Despite the obvious limitation of using fictitious data, the findings suggest that LLMs like ChatGPT have the potential to complement clinical workflows in sleep medicine by identifying individual health risks and comorbidities. As LLMs continue to evolve, their integration into healthcare could redefine the approach to patient evaluation and risk stratification. Future research should contextualize the findings within broader clinical applications ideally testing locally run LLMs meeting data protection requirements.