PubMed چکیده/رکورد

[Patient navigation in severe mental illness-challenges, models, and perspectives].

استودیوی صوتی مقاله

پخش حرفه‌ای فارسی و انگلیسی

در حال بررسی نسخه‌های صوتی ذخیره‌شده…

صوت تولیدشده با هوش مصنوعی است. برای کاربرد علمی یا درمانی، متن و منبع اصلی را بررسی کنید.
خواندن هوشمند فارسی و انگلیسی در حال آماده‌سازی صداهای مرورگر…
تنظیم صدای طبیعی و سرعت

صداهایی که در نامشان «Natural»، «Neural» یا «Online» دیده می‌شود معمولاً طبیعی‌ترند. انتخاب صدا به صداهای نصب‌شده در ویندوز و مرورگر شما بستگی دارد.

چکیده اصلی

Severe mental illness (SMI) is associated with chronic courses, significant functional impairments, and increased mortality. At the same time, care for this population is characterized by fragmentation, lack of continuity, and insufficient coordination. Against this background, patient navigation has gained importance as an approach to improving the quality and efficiency of care. The aim of this narrative review is to present key evidence-based models of care for individuals with SMI, to analyze structural challenges in service provision, and to derive principles for effective patient navigation.The evidence shows that community-based, multidisciplinary, and continuous care approaches-such as assertive community treatment, crisis intervention, and early intervention programs-can improve clinical and functional outcomes, reduce hospitalizations, and enhance continuity of care. At the same time, it becomes clear that the isolated implementation of individual intervention models is insufficient to sustainably address structural deficits.Integrated and stepped care models enable the systematic linkage of evidence-based interventions along a continuous care pathway. Current findings indicate that such approaches can improve quality of care while being implementable without additional costs and may even generate cost savings. Patient navigation thus represents a key strategy to strengthen continuity of care, reduce fragmentation, and sustainably improve patient outcomes as well as resource efficiency.

متن کامل اصلی

متن در JumpToDate ذخیره نشده است.

برای بررسی دسترسی کتابخانه‌ای یا خرید، رکورد اصلی را باز کنید.

رفتن به منبع اصلی

کلیدواژه‌ها

Assertive community treatmentIntegrated careMental health carePatient navigationSevere mental illnessStepped care
در همین زیرشاخه

مقاله‌های مرتبط

PubMed2026

[THE TRADITIONAL AND COMPLEMENTARY MEDICINE IN THE PUBLIC HEALTH SYSTEM OF THE RUSSIAN FEDERATION: ANALYSIS OF EVIDENCE BASE AND LEGAL ASPECTS OF INTEGRATION].

The article considers placement of methods of traditional and complementary medicine in the public health system of the Russian Federation, focusing on prevention and management of chronic non-communicable diseases. The analysis of the evidence base on key methods of traditional and complementary medicine (acupuncture, phytotherapy, homeopathy, manual therapy, osteopathy) was carried out, including differentiated estimate of level of e…

PubMed2026

[Chinese expert consensus on the clinical application of lung biopsy in interstitial lung disease].

Interstitial lung disease (ILD) comprises a highly heterogeneous group of pulmonary disorders, whose diagnosis often requires the integration of clinical, radiologic, and pathologic evidence. Lung biopsy is a key method for obtaining a histopathologic diagnosis, but unified standards are lacking for determining indications, selecting biopsy techniques, managing patients perioperatively, and integrating pathologic and clinical informati…

PubMed2026

Targeted US Screening in High-Risk Newborns and Infants: Indications, Techniques, and Disease Findings.

Newborns and infants with certain perinatal exposures, congenital conditions, and genetic syndromes are at increased risk for clinically significant diseases and may benefit from early detection. In these children, imaging serves as a targeted screening tool to identify actionable abnormalities during a window in which intervention may reduce morbidity and long-term sequelae. The authors summarize evidence-based US screening strategies…

PubMed2026

The Promises and Perils of Clinical Decision Support Artificial Intelligence.

Evidence-based clinical decision support artificial intelligence (AI) is rapidly expanding, but its safe and effective use depends on rigorous validation, trustworthy evidence sources and careful integration into clinical workflows. Current available systems show strong potential to improve diagnostic accuracy, reduce clinician workload and possibly benefit patient care, but challenges remain before its real-world adoption. We must be …