Search Results (3,000)

Background/Objectives: The integration of nutrition into the psychological sciences and mental healthcare has gained increasing attention with the emergence of Nutritional Psychology as an interdisciplinary field. However, little is known about how professionals approach this interface or the barriers they face. This study aimed to explore the extent to which mental health and nutrition professionals engage with each other’s domains. Methods: This cross-sectional study analyzed survey responses from 196 professionals, including mental health professionals, nutrition professionals, and individuals from related fields recruited through the individual and institutional professional network of the Center for Nutritional Psychology, San Jose, CA, USA. The goal was to examine training, practices, perceived barriers, and interest in collaboration between these professions. Descriptive statistics summarized categorical and quantitative variables. Mann–Whitney U tests with rank-biserial correlation examined ordinal data. Exploratory factor analysis (principal axis factoring, varimax rotation) was applied to barrier ratings. Results: A total of 78.3% of participating mental health professionals reported addressing nutrition in their practice at least occasionally, while a similar percentage (78.5%) of participating nutrition professionals discussed mental health with clients at least occasionally. Despite this engagement, 25% of all participating nutrition professionals and 51% of all participating mental health professionals reported no formal training in the complementary field, with most of their knowledge acquired informally through self-study or workshops. A total of 11 mental health professionals and 13 nutrition professionals did not answer the question about formal education in the complementary field. The highest-rated perceived barriers included a lack of training, unclear professional boundaries, and a lack of a common language, whereas a lack of evidence was perceived as the least critical. Importantly, 99% of respondents who answered the question (n = 84, while 112 participants did not respond), or 42% of all participants, expressed interest in evidence-based interdisciplinary training. A total of 96% of those who answered the question (n = 133, while 63 participants did not answer) or 65% of all participants agreed that collaboration would improve patient outcomes. However, only 49% of all participating nutrition professionals and 50% of all participating mental health professionals reported having collaborative experience. A total of 7 mental health professionals and 16 nutrition professionals did not answer the question about collaborative experiences. Conclusions: Within this self-selected sample, respondents reported frequent cross-disciplinary engagement, limited formal training, and interest in interdisciplinary education and collaboration. These exploratory findings require confirmation in independently recruited and better-characterized samples. Full article

The growth of single-person older adult households has heightened concerns about social isolation, chronic disease management, and diminished quality of life (QoL). A systematic review with meta-analysis was conducted to examine the efficacy of human-centered AI healthcare interventions on QoL among older adults living alone. Five electronic databases (PubMed, Web of Science, CINAHL, Embase, and Cochrane CENTRAL) were searched (January 2020–March 2026) following PRISMA 2020 guidelines. Only randomized controlled trials (RCTs) were eligible. Methodological quality was appraised with the Cochrane RoB 2 tool; pooled effect estimates were derived via random-effects modeling. Fourteen RCTs (N = 2840) were included. Intervention types comprised conversational agents, socially assistive robots, remote monitoring systems, integrated platforms, and AI-driven mHealth applications. Meta-analysis demonstrated significant improvements in overall QoL (SMD = 0.40, 95% CI: 0.27–0.52, I² = 59%), depression (SMD = −0.35, 95% CI: −0.43 to −0.26), and social connectedness (SMD = 0.38, 95% CI: 0.26–0.51). Subgroup analyses showed stronger effects for interventions with personalized feedback and human interaction. GRADE certainty was moderate for all three primary outcomes. GRADE certainty for secondary outcomes was moderate for physical health but low for self-management and cognitive function. Human-centered AI healthcare interventions significantly improve QoL and psychosocial outcomes among older adults living alone. Future research should prioritize long-term effectiveness, ethical implementation, digital inclusion, and culturally adaptive models. Full article

Objective: Functional dyspepsia (FD) is frequently treated in Korean Medicine (KM) clinics in South Korea. With the recent inclusion of herbal decoctions (HDs) for FD in the national pilot program for health insurance coverage, the generation of real-world evidence has become an important policy priority. In this study, we aim to evaluate the real-world clinical outcomes and safety of individualized HD-centered KM care in patients with FD in a primary care setting. Methods: This multicenter, prospective, single-arm observational cohort study will enroll 150 participants who meet the Rome IV criteria for FD across 23 KM clinics nationwide. Participants will receive individualized HDs, prescribed at the discretion of the attending physicians, as the primary treatment for 4 weeks. Concomitant KM treatments (e.g., acupuncture, moxibustion, and cupping) are permitted as clinically needed to preserve the pragmatic nature of routine care. The primary outcome is the change in the total symptom score on the Nepean Dyspepsia Index, Korean version (NDI-K), at week 4. Secondary outcomes include the Numeric Rating Scale for overall severity, overall treatment effect, and safety monitoring. Statistical analyses will be performed using mixed models for repeated measures to account for repeated observations and missing data. Discussion: This study will evaluate the pragmatic outcomes of individualized HD-centered KM care in routine clinical practice, thereby supporting the external validity of its findings. The findings are expected to provide foundational real-world data for the national HD pilot program, and these data may serve as a reference for healthcare policies and clinical decision-making. Clinical Trial Registration: Clinical Research Information Service (registration number KCT0010254); registered on 11 February 2025. Full article

Objective: Reducing the length of hospital stay (LOS) is a core objective in the management of acute exacerbations of chronic obstructive pulmonary disease (AECOPD). This study aimed to develop an interpretable and clinically applicable machine learning tool for predicting prolonged LOS in this population. Methods: This retrospective three-center study enrolled 1342 patients, who were randomly allocated to a training set (70%) and a test set (30%). Candidate predictors were screened using least absolute shrinkage and selection operator (LASSO) regression, and six machine learning models were developed and compared: logistic regression, random forest, gradient boosting machine, CatBoost, support vector machine, and neural network. Model discrimination was evaluated using the area under the receiver operating characteristic curve (AUC), calibration plots, and decision curve analysis. Model interpretability was achieved through SHapley Additive exPlanations (SHAP) and subgroup analyses. Results: Prolonged LOS, defined as >10 days (the median LOS in the training set), occurred in 42.0% of patients. Nine predictors were identified, including daily inhaled medication use, sputum microbiological examination, antibiotic administration, corticosteroid therapy, diuretic use, ICU admission, oxygenation index, platelet count, and neutrophil count. The random forest model demonstrated superior and consistent discriminative performance, achieving an AUC of 0.778 (95% CI: 0.748–0.807) in the training set and 0.721 (95% CI: 0.672–0.771) in the test set. SHAP analysis ranked diuretic use, daily inhaled medication use, corticosteroid therapy, sputum microbiological examination, and antibiotic administration as the five most influential features, revealing treatment-related variables associated with prolonged LOS. Subgroup analyses indicated better predictive performance in lower-risk patients (age ≤ 65 years with eGFR 1–2 stage). Conclusions: Random Forest may enable to promptly identify prolonged LOS high-risk patients, enhancing clinical vigilance and optimizing healthcare resource allocation in AECOPD patients. Full article

Background/Objective: Artificial intelligence (AI) is increasingly transforming healthcare and has emerged as a promising tool in pediatric dentistry. This narrative review examines the methodological foundations, current applications, limitations, and future directions of AI in pediatric dental practice. Methods: A structured literature search was conducted in PubMed/MEDLINE, Scopus, Web of Science, and Google Scholar for relevant English-language publications from database inception through to May 2026, using predefined search terms and eligibility criteria. Following screening and full-text assessment, 72 unique publications were retained for the narrative synthesis. Results: AI applications in pediatric dentistry include early childhood caries detection and risk prediction, dental plaque assessment, identification of mesiodens and supernumerary teeth, dental age estimation, automated tooth detection, fissure-sealant evaluation, and craniofacial growth prediction. Several imaging-based models demonstrated performance comparable to experienced clinicians. However, many studies relied on retrospective, single-center datasets and lacked external or prospective validation. Additional concerns included algorithmic bias, data privacy, limited interpretability, and infrastructure requirements. Conclusions: AI has considerable potential to improve diagnostic efficiency, consistency, and personalized preventive care in pediatric dentistry. However, it should be used as a clinical decision-support tool rather than a replacement for professional judgment. Future research should prioritize prospective multicenter validation, multimodal and explainable systems, standardized reporting, and ethical clinical implementation. Full article

Background: The rapid adoption of artificial intelligence (AI) in healthcare and management information systems has posed both opportunities and challenges in assessing value across clinical, operational, governance and societal dimensions. Existing healthcare value models are inadequate for the dynamic, data-rich, and ethically complex nature of AI-enabled care and thus necessitate a broader evaluative framework. Objective: This paper proposes an AI-Augmented Healthcare Value Framework (AI-HVF) designed as a multidimensional evaluative lens for assessing value in AI-enabled healthcare across structural, process, outcome, cost, and governance domains. Methods: A narrative, theory-driven literature review was undertaken of healthcare quality frameworks, value-based healthcare, digital health, AI in medicine and public health to identify limitations of existing models and to derive the required dimensions for an updated framework. The dimensions were synthesized into an integrated conceptual model linking structures, processes, outcomes, costs and governance in AI-enabled healthcare. Results: AI-HVF transforms traditional healthcare value models in a data-rich and automated care era. Structural dimensions include digital infrastructure, workforce readiness, learning health systems, and operational efficiency; process dimensions include AI-supported clinical excellence, patient experience, prevention, and explainability; and outcome dimensions go beyond traditional clinical metrics to include equity, safety, continuity, provider well-being, sustainability, and societal value. It also includes real cost accounting and has governance, ethics and trust as an overarching layer that supports accountability, transparency and fairness across all domains. Conclusions: AI-HVF provides a multi-dimensional framework to assess, plan and govern AI in health care at the patient, organization, and system levels. It is intended to enable retrospective evaluation and prospective implementation. The framework offers a foundation for developing future indicators, pilot testing and for comparative evaluation of ethical, equitable and sustainable AI integration in healthcare. Full article

Alcohol consumption is associated with significant social and economic challenges for nations worldwide, including alcohol-related healthcare costs and alcohol-related crime. According to the U.S. Center for Disease Control (CDC), even moderate drinking correlates with greater health risks compared with not drinking at all. These health risks include physical harm to a person’s heart, brain, liver, and other organs; impaired social functioning; reduced work performance; and increased incidence of alcohol-related violence and injury (e.g., physical and sexual assault and drunk driving-related injuries and death). Although personal health and social problems are correlated with alcohol consumption across genders, women experience disproportionately worse problems associated with alcohol-related crime and family disruption. At the societal level, alcohol use coincides with substantial negative economic problems, including increased healthcare costs, often covered by the government; reduced worker productivity; and higher criminal justice system costs. This paper applies an econometric analysis to evaluate the association between alcohol consumption and key social and economic factors and suggests actions that business firms, government entities, universities, and other organizations can take to reduce alcohol-related harm, such as encouraging sobriety in the workplace, offering alcohol-related counseling services, and not establishing associations with nor accepting payments of any kind from alcohol beverage companies. The findings have implications for business managers, government leaders, policy-makers, and others. Full article

Explainable Artificial Intelligence (XAI) has emerged as a critical enabler for the adoption of machine learning models in high-stakes domains such as healthcare. While significant progress has been made in XAI for computer vision and natural language processing, tabular data—the predominant format of electronic health records—presents unique challenges and opportunities. This systematic review provides a comprehensive analysis of XAI methods specifically applied to tabular healthcare data for classification tasks. We examine 21 primary studies published between 2020 and the first half of 2026, covering three complementary perspectives: (1) intrinsically interpretable models, (2) post-hoc methods including LIME, SHAP, and their variants, and (3) evaluation frameworks that assess both model-centered fidelity and human-centered clinical alignment. Our analysis reveals that SHAP remains the dominant post-hoc method, achieving strong model fidelity but showing inconsistent alignment with clinical expert reasoning. Key findings include the significant impact of class imbalance on explanation consistency, the importance of clinician-centered evaluation, and the emergence of hybrid approaches integrating XAI with generative AI and transfer learning. We identify critical gaps, including limited adoption of XAI in AutoML pipelines, lack of standardized evaluation metrics, and predominance of single-institution validation studies. Full article

This paper analyzes contemporary medicine as a prime arena for the expression of “engaged spirituality”, understood as an action-oriented approach aimed at social and clinical change. The focus is on palliative care, a person-centered model that challenges biomedical reductionism by integrating the physical, psychological, and spiritual dimensions of the patient. Through a review of the state of the art, the article reconstructs the historical separation between “health” and “salvation”, highlighting the critical issues of the Italian context: “institutional inattention” and difficulties in managing religious pluralism. In contrast, international experiences and the adoption of technical tools demonstrate that spiritual care is now measurable and essential clinical competency. The analysis presents the results of empirical research which, through the voices of healthcare professionals, shows how palliative care translates spirituality into operational practice. By valuing the concept of “total pain”, this approach not only improves quality of life at the end of life but also serves as a holistic paradigm exportable to other medical fields, ensuring the recognition of the person’s integral dignity. Full article

Background: The widespread use of online review platforms has transformed how patients evaluate healthcare providers and has created a valuable source of patient-generated feedback on healthcare quality. Negative online reviews provide valuable insights into patient experiences and sources of dissatisfaction. This narrative review aimed to synthesize the main themes and concerns expressed in negative online patient reviews across healthcare and dentistry and to explore recurring patterns reported in the available literature. Methods: A narrative literature search was conducted between January 2015 and May 2026. Results: Twenty-two studies were included in the narrative synthesis. Negative online reviews reflected multiple dimensions of the patient experience, including interpersonal, communication, organizational, financial, and clinical concerns. Although the relative prominence of these themes varied across healthcare settings and specialties, dental studies more frequently emphasized treatment expectations, professional trust, and perceived value for money. Conclusions: The findings indicate that patient dissatisfaction expressed in negative online reviews is multidimensional and cannot be interpreted solely through clinical outcomes. Rather than establishing the relative importance of different determinants of patient satisfaction, this review characterizes the themes most commonly expressed in negative online reviews. As a complementary source of patient-generated insights, online reviews may inform patient-centered quality improvement initiatives and support improvements in healthcare service delivery. Full article

Background: Although the diagnostic and treatment pathway for Obstructive Sleep Apnea (OSA) is well established, long-term management is still not standardized. Methods: We conducted a narrative review of studies published between 2000 and 2026 in PubMed/MEDLINE. Results: Continuous Positive Airway Pressure (CPAP) adherence and healthcare resource allocation remain the real critical issue of long-term management for OSA patients. Early proactive interventions like phone calls and early medical visits may be particularly valuable for improving CPAP nightly usage. Telemedicine, educated and trained home-care providers or primary care can lead to improved adherence, long-term monitoring and earlier identification of treatment-related issues. Patient-reported outcomes, comorbidities, endotypes, and phenotypic stratification should be integrated into follow-up management. Conclusions: OSA long-term care should shift from a device-centered approach toward a personalized, patient-centered model with the integration of multidimensional risk stratification, multidisciplinary care, telemedicine and primary care support. Full article

Durable medical devices (DMD) are essential healthcare assets, yet their management in public hospitals is constrained by fragmentation, limited traceability, reactive maintenance, and weak lifecycle integration. This study proposes a framework for digitally enabled, sustainable, and circular DMD management. A mixed-methods design integrated a literature review, expert consultation using the Best–Worst Method, weighted technology nominations, and case-based process mapping in Brazilian hospitals. Eleven experts assessed the criteria guiding Industry 4.0 technology selection for DMD management and the technologies best responding to these priorities; nine consistent judgments were aggregated. Patient-Centered Care, Operational Efficiency, and Resource Efficiency and Cost Reduction emerged as the leading influences on technology selection. Big Data and Analytics, Artificial Intelligence, the Internet of Things, Cloud Computing, Cyber-Physical Systems, Smart Sensors, and Machine Learning formed the priority portfolio, accounting for 84% of the weighted score. The cases contextualized these priorities by revealing discontinuous information flows, limited asset visibility, corrective maintenance, fragmented governance, and weak end-of-life practices. By connecting decision priorities and technological capabilities with observed gaps, the TO-BE framework organizes sustainable procurement, traceable use, predictive maintenance, redeployment, refurbishment, and responsible disposal through material and information flows, providing a pathway for digital and circular transformation in resource-constrained healthcare systems. Full article

The 1st International Online Conference on Healthcare (IOCH 2026) was held online from 25–26 March 2026. IOCH 2026 aimed to explore the current landscape, address existing challenges and opportunities, and discuss future directions that will shape the evolution of healthcare delivery and systems. Full article

The widespread adoption of in vitro fertilization (IVF) has profoundly transformed the management of infertility and established IVF as the most effective assisted reproductive technology for many clinical indications. Nevertheless, intrauterine insemination (IUI) remains widely used because of its lower invasiveness, reduced treatment burden, lower costs, and broader accessibility. The contemporary role of IUI in an IVF-dominated era remains a subject of ongoing debate. This narrative review aims to critically evaluate the current role of IUI in modern reproductive medicine by examining its clinical indications, effectiveness, limitations, cost-effectiveness, and patient-centered implications in comparison with IVF. A narrative review of the literature was conducted using PubMed/MEDLINE, Scopus, Web of Science, and Google Scholar. Relevant studies, systematic reviews, meta-analyses, randomized controlled trials, and international guidelines published primarily between 2010 and 2025 were identified and synthesized. Particular attention was given to recommendations from the European Society of Human Reproduction and Embryology (ESHRE) and the American Society for Reproductive Medicine (ASRM). Current evidence supports the use of IUI, particularly when combined with ovarian stimulation, as an appropriate first-line treatment in selected couples with unexplained infertility, mild male-factor infertility, and minimal-to-mild endometriosis. Although IVF generally provides higher live birth rates per treatment cycle and remains the preferred option for several infertility diagnoses, IUI continues to offer meaningful clinical benefits in appropriately selected patients. Beyond effectiveness, treatment decisions should also consider cost-effectiveness, accessibility, treatment burden, safety, and patient preferences. Recent evidence increasingly supports individualized and prognosis-based approaches to fertility treatment selection. Evidence also highlights the importance of female age, infertility duration, and ovarian reserve in determining the relative benefit of IUI versus IVF. Despite the clinical dominance of IVF, IUI remains an evidence-based and clinically relevant component of contemporary fertility care. Rather than being viewed as competing interventions, IUI and IVF should be considered complementary treatment options within a personalized infertility management strategy. Appropriate patient selection, particularly considering female reproductive prognosis, remains essential to maximize reproductive outcomes while minimizing treatment burden and healthcare costs. Full article

Background and Objectives: In-hospital cardiac arrest (IHCA) in critically ill patients with COVID-19 is among the most severe clinical outcomes, associated with high mortality and a significant risk to healthcare workers during cardiopulmonary resuscitation (CPR). The aim of this study was to evaluate the incidence, characteristics, associated factors, and outcomes of IHCA among COVID-19 patients treated in the intensive care unit (ICU), with particular emphasis on resuscitation outcomes and survival. Materials and Methods: A retrospective cohort study was conducted including critically ill patients with confirmed SARS-CoV-2 infection treated in the ICU of the Clinical-Hospital Center (KBC) in Kosovska Mitrovica between March 2020 and December 2022. Patients were categorized into two groups: (1) CA group—patients who experienced CA in the ICU, and (2) non-CA group—patients who did not experience CA during ICU treatment. Results: A total of 222 patients were analyzed, of whom 114 (51.4%) experienced IHCA. Patients with IHCA were significantly older, more frequently obese, and had a higher burden of comorbidities. They also exhibited more pronounced hematological and inflammatory abnormalities, including lower erythrocyte and hemoglobin levels, thrombocytopenia, and elevated leukocyte counts, fibrinogen, C-reactive protein, and procalcitonin levels. In addition, higher lactate and D-dimer concentrations were observed, along with a more frequent occurrence of hyperkalemia and hypernatremia. The predominant cause of IHCA was respiratory failure, most commonly associated with severe hypoxemia (59.6%), while non-shockable initial rhythms (asystole and pulseless electrical activity (PEA)) were most common (76.3%). Among patients with IHCA, return of spontaneous circulation (ROSC) was achieved in 11 patients (9.6%), and 3 patients (2.6%) survived to hospital discharge. Conclusions: Despite rapid response and CPR in the ICU setting, outcomes remained poor. More favorable outcomes were observed mainly in cases with potentially reversible etiologies (such as myocardial infarction and pulmonary embolism (PE)), in contrast to hypoxia-mediated CA. Full article