Search Results (810)

Background: Mediastinal staging plays a critical role in guiding treatment decisions for non-small cell lung cancer (NSCLC). While mediastinoscopy has been the gold standard for assessing mediastinal lymph node involvement, endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) has emerged as a minimally invasive alternative with comparable diagnostic accuracy. This systematic review evaluates the diagnostic performance, safety, cost-effectiveness, and feasibility of EBUS-TBNA versus mediastinoscopy for mediastinal staging. Methods: A systematic literature review was conducted in accordance with PRISMA guidelines, including searches in Medline, Scopus, EMBASE, and Cochrane databases for studies published from 2010 onwards. A total of 1542 studies were identified, and after removing duplicates and applying eligibility criteria, 100 studies were included for detailed analysis. The extracted data focused on sensitivity, specificity, complications, economic impact, and patient outcomes. Results: EBUS-TBNA demonstrated high sensitivity (85–94%) and specificity (~100%), making it an effective first-line modality for NSCLC staging. Mediastinoscopy remained highly specific (~100%) but exhibited slightly lower sensitivity (86–90%). EBUS-TBNA had a lower complication rate (~2%) and was more cost-effective, while mediastinoscopy provided larger biopsy samples, essential for molecular and histological analyses. The need for general anaesthesia, longer hospital stays, and increased procedural costs make mediastinoscopy less favourable as an initial approach. Combining both techniques in select cases enhanced overall staging accuracy, reducing false negatives and improving diagnostic confidence. Conclusions: EBUS-TBNA has become the preferred first-line mediastinal staging method due to its minimally invasive approach, high diagnostic accuracy, and lower cost. However, mediastinoscopy remains crucial in cases requiring posterior mediastinal node assessment or larger tissue samples. The integration of both techniques in a stepwise diagnostic strategy offers the highest accuracy while minimizing risks and costs. Given the lower hospitalization rates and economic benefits associated with EBUS-TBNA, its widespread adoption may contribute to more efficient resource utilization in healthcare systems. Full article

Heart failure (HF) poses a substantial global burden due to its high morbidity, mortality, and healthcare costs. Accurate prognostication is crucial for optimizing treatment, resource allocation, and patient counseling. Prognostic tools range from simple clinical scores such as ADHERE and MAGGIC to more complex models incorporating biomarkers (e.g., NT-proBNP, sST2), imaging, and artificial intelligence techniques. In acute HF, models like EHMRG and STRATIFY aid early triage, while in chronic HF, tools like SHFM and BCN Bio-HF support long-term management decisions. Despite their utility, most models are limited by poor generalizability, reliance on static inputs, lack of integration into electronic health records, and underuse in clinical practice. Novel approaches involving machine learning, multi-omics profiling, and remote monitoring hold promise for dynamic and individualized risk assessment. However, these innovations face challenges regarding interpretability, validation, and ethical implementation. For prognostic models to transition from theoretical promise to practical impact, they must be continuously updated, externally validated, and seamlessly embedded into clinical workflows. This review emphasizes the potential of prognostic models to transform HF care but cautions against uncritical adoption without robust evidence and practical integration. In the evolving landscape of HF management, prognostic models represent a hopeful avenue, provided their limitations are acknowledged and addressed through interdisciplinary collaboration and patient-centered innovation. Full article

This article describes the design, methods, and baseline characteristics of the social needs assessment (SNA) of participants enrolled in an ongoing randomized clinical trial implementing a comprehensive approach to improving diabetes self-management and providing an intensive Diabetes Self-Management Education and Support (iDSMES) Program at St. Vincent’s House Clinic, a primary care practice serving resource-challenged diverse populations in Galveston, Texas. Standardized SNA was conducted to collect information on financial needs, psychosocial well-being, and other chronic health conditions. Based on their identified needs, participants were referred to non-medical existing community resources. A series of in-depth interviews were conducted with a subset of participants. A team member independently categorized these SNA narratives and aggregated them into two overarching groups: medical and social needs. Fifty-nine participants (with a mean age of 53 years and equal representation of men and women) completed an SNA. Most (71%) did not have health insurance. Among 12 potential social needs surveyed, the most frequently requested resources were occupational therapy (78%), utility assistance (73%), and food pantry services (71%). SNA provided data with the potential to address barriers that may hinder participation, retention, and outcomes in diabetes self-management. SNA findings may serve as tertiary prevention to mitigate diabetes-related complications and disparities. Full article

Many hospitals still lack digital traceability in hygiene and cleaning management, leading to operational inefficiencies and inconsistent quality control. This study aims to establish cleaning and hygiene processes in healthcare services that are planned in accordance with standards, as well as to enhance the traceability and sustainability of these processes through digitalization. This study proposes a Hyperledger Fabric-based blockchain architecture to establish a reliable and transparent quality assurance system in process management. The proposed Quality Assurance Model utilizes digital technologies and IoT-based RFID devices to ensure the transparent and reliable monitoring of cleaning processes. Operational data related to cleaning processes are automatically recorded and secured using a decentralized blockchain infrastructure. The permissioned nature of Hyperledger Fabric provides a more secure solution compared to traditional data management systems in the healthcare sector while preserving data privacy. Additionally, the execute–order–validate mechanism supports effective data sharing among stakeholders, and consensus algorithms along with chaincode rules enhance the reliability of processes. A working prototype was implemented and validated using Hyperledger Caliper under resource-constrained cloud environments, confirming the system’s feasibility through over 100 TPS throughput and zero transaction failures. Through the proposed system, cleaning/hygiene processes in patient rooms are conducted securely, contributing to the improvement of quality standards in healthcare services. Full article

Background/Objectives: The RTK-RAS signaling cascade is a central axis in colorectal cancer (CRC) pathogenesis, governing cellular proliferation, survival, and therapeutic resistance. Somatic alterations in key pathway genes—including KRAS, NRAS, BRAF, and EGFR—are pivotal to clinical decision-making in precision oncology. However, the integration of these genomic events with clinical and demographic data remains hindered by fragmented resources and a lack of accessible analytical frameworks. To address this challenge, we developed AI-HOPE-RTK-RAS, a domain-specialized conversational artificial intelligence (AI) system designed to enable natural language-based, integrative analysis of RTK-RAS pathway alterations in CRC. Methods: AI-HOPE-RTK-RAS employs a modular architecture combining large language models (LLMs), a natural language-to-code translation engine, and a backend analytics pipeline operating on harmonized multi-dimensional datasets from cBioPortal. Unlike general-purpose AI platforms, this system is purpose-built for real-time exploration of RTK-RAS biology within CRC cohorts. The platform supports mutation frequency profiling, odds ratio testing, survival modeling, and stratified analyses across clinical, genomic, and demographic parameters. Validation included reproduction of known mutation trends and exploratory evaluation of co-alterations, therapy response, and ancestry-specific mutation patterns. Results: AI-HOPE-RTK-RAS enabled rapid, dialogue-driven interrogation of CRC datasets, confirming established patterns and revealing novel associations with translational relevance. Among early-onset CRC (EOCRC) patients, the prevalence of RTK-RAS alterations was significantly lower compared to late-onset disease (67.97% vs. 79.9%; OR = 0.534, p = 0.014), suggesting the involvement of alternative oncogenic drivers. In KRAS-mutant patients receiving Bevacizumab, early-stage disease (Stages I–III) was associated with superior overall survival relative to Stage IV (p = 0.0004). In contrast, BRAF-mutant tumors with microsatellite-stable (MSS) status displayed poorer prognosis despite higher chemotherapy exposure (OR = 7.226, p < 0.001; p = 0.0000). Among EOCRC patients treated with FOLFOX, RTK-RAS alterations were linked to worse outcomes (p = 0.0262). The system also identified ancestry-enriched noncanonical mutations—including CBL, MAPK3, and NF1—with NF1 mutations significantly associated with improved prognosis (p = 1 × 10⁻⁵). Conclusions: AI-HOPE-RTK-RAS exemplifies a new class of conversational AI platforms tailored to precision oncology, enabling integrative, real-time analysis of clinically and biologically complex questions. Its ability to uncover both canonical and ancestry-specific patterns in RTK-RAS dysregulation—especially in EOCRC and populations with disproportionate health burdens—underscores its utility in advancing equitable, personalized cancer care. This work demonstrates the translational potential of domain-optimized AI tools to accelerate biomarker discovery, support therapeutic stratification, and democratize access to multi-omic analysis. Full article

Background/Objectives: Laboratory tests conducted in community settings are occasionally repeated within hours of presentation to pediatric emergency departments (PEDs). Reducing unnecessary repetitions can ease child discomfort and alleviate the healthcare burden without compromising the diagnostic process or quality of care. The aim of this study was to develop a decision tree (DT) model to guide physicians in minimizing unnecessary repeat blood tests in PEDs. The minimal decision tree (MDT) algorithm was selected for its interpretability and capacity to generate optimally pruned classification trees. Methods: Children aged 3 months to 18 years with community-based complete blood count (CBC), electrolyte (ELE), and C-reactive protein (CRP) measurements obtained between 2016 and 2023 were included. Repeat tests performed in the pediatric emergency department within 12 h were evaluated by comparing paired measurements, with tests considered justified when values transitioned from normal to abnormal ranges or changed by ≥20%. Additionally, sensitivity analyses were conducted for absolute change thresholds of 10% and 30% and for repeat intervals of 6, 18, and 24 h. Results: Among 7813 children visits in this study, 6044, 1941, and 2771 underwent repeated CBC, ELE, and CRP tests, respectively. The mean ages of patients undergoing CRP, ELE, and CBC testing were 6.33 ± 5.38, 7.91 ± 5.71, and 5.08 ± 5.28 years, respectively. The majority were of middle socio-economic class, with 66.61–71.24% living in urban areas. Pain was the predominant presented complaint (83.69–85.99%), and in most cases (83.69–85.99%), the examination was conducted by a pediatrician. The DT model was developed and evaluated on training and validation cohorts, and it demonstrated high accuracy in predicting the need for repeat CBC and ELE tests but not CRP. Performance of the DT model significantly exceeded that of the logistic regression model. Conclusions: The data-driven guide derived from the DT model provides clinicians with a practical, interpretable tool to minimize unnecessary repeat laboratory testing, thereby enhancing patient care and optimizing healthcare resource utilization. Full article

Background/Objective: A growing body of international research continues to show evidence of worsening youth mental health since the beginning of the COVID-19 global pandemic, yet very little research in this area has included young children under 6 years. Given the potential impact of early life stress during this critical period of development, it is crucial to better understand the effects on this age group. The objective of this study was to better understand the impact of the COVID-19 pandemic on the emotional health of very young children. Methods: This study utilized retrospective chart review of primary care records to compare the prevalence of markers of stress in two cohorts of children under the age of 6 years, comparing children presenting for care prior to the pandemic (1 April 2019–31 March 2020; control period) with those presenting for care during the first year of the pandemic (1 April 2020–31 March 2021; study period) in a large pediatric primary care clinic in Washington, DC, USA. Based on power calculations, charts of 200 patients from each cohort were reviewed and prevalence of stress markers were summarized using counts and percentages and compared between groups using chi-squared tests. Multivariable logistic regression models were also conducted for each domain adjusting for age, gender, and insurance type. Results: Overall, sleep difficulties were significantly more prevalent during the pandemic period compared to the control period (14% vs. 6.5%, p = 0.013). In addition, signs of stress presented differently across age groups. For example, during the pandemic period toddlers (13–35 months) were 13 times more likely (OR = 13, 95% CI [2.82, 60.4], p < 0.001) and preschool-aged children (36–71 months) were 18.5 times more likely (OR = 18.5, 95% CI [4.0, 86], p < 0.001) than infants to present with behavior problems, indicating substantially higher risk of externalizing symptoms in older children compared to infants. Toddlers were less likely than infants to present with mood changes (e.g., fussiness or crying) (OR = 0.15, 95% CI [0.03, 0.65], p = 0.011). In addition, toddlers (OR = 0.55, 95% CI [0.31, 0.97], p = 0.038) and preschool-aged children (OR = 0.15, 95% CI [0.06, 0.4], p < 0.001) were also less likely to present with feeding difficulties compared to infants. Conclusions: One of the very few studies of young children under 6 years (including infants) during the COVID-19 pandemic, this study found that even very young children experienced stress during the pandemic. Signs of emotional stress were identified in a primary care office during routine care, highlighting an important opportunity for early intervention and/or prevention, such as counseling and resources for caregivers, in settings where young children are already presenting for routine care. Full article

Deep learning (DL) continues to play a pivotal role in a wide range of intelligent systems, including autonomous machines, smart surveillance, industrial automation, and portable healthcare technologies. These applications often demand low-latency inference and efficient resource utilization, especially when deployed on embedded or edge devices with limited computational capacity. As DL models become increasingly complex, selecting the right inference framework is essential to meeting performance and deployment goals. In this work, we conduct a comprehensive comparison of five widely adopted inference frameworks: PyTorch, ONNX Runtime, TensorRT, Apache TVM, and JAX. All experiments are performed on the NVIDIA Jetson AGX Orin platform, a high-performance computing solution tailored for edge artificial intelligence workloads. The evaluation considers several key performance metrics, including inference accuracy, inference time, throughput, memory usage, and power consumption. Each framework is tested using a wide range of convolutional and transformer models and analyzed in terms of deployment complexity, runtime efficiency, and hardware utilization. Our results show that certain frameworks offer superior inference speed and throughput, while others provide advantages in flexibility, portability, or ease of integration. We also observe meaningful differences in how each framework manages system memory and power under various load conditions. This study offers practical insights into the trade-offs associated with deploying DL inference on resource-constrained hardware. Full article

Background: Deep surgical site infections (SSIs) represent a serious complication following abdominal trauma surgery; however, comprehensive risk factor analysis in large trauma populations remains limited. Although surgical site infections are recognized as preventable complications, little is known about the specific risk factors and clinical outcomes associated with deep SSIs in trauma patients at the national level. Methods: A retrospective cohort study analyzed data from the National Trauma Data Bank from 2020–2022, including 1,198,262 trauma patients with complete demographic, injury severity, and surgical procedure data. Deep SSI development, length of hospital stay, intensive care unit utilization, duration of mechanical ventilation, discharge disposition, and in-hospital mortality were assessed. Multivariate logistic regression was used to identify independent risk factors and quantify associations between patient characteristics and deep SSI occurrence. Results: Deep SSIs occurred in 601 patients (0.05%). Affected patients were younger (median 41 vs. 54 years, p < 0.001), predominantly male (73.7% vs. 61.8%, p < 0.001), and exhibited higher injury severity scores (median 17.0 vs. 5.0, p < 0.001). Major abdominal surgery was the strongest independent predictor (OR 3.08, 95% CI: 2.21–4.23, p < 0.001), followed by injury severity score (OR 1.05, 95% CI: 1.04–1.06, p < 0.001) and ICU length of stay (OR 1.04 per day, 95% CI: 1.03–1.05, p < 0.001). Patients with deep SSIs demonstrated dramatically increased hospital stays (89.5% vs. 4.5% exceeding 21 days, p < 0.001), reduced home discharge rates (28.5% vs. 48.9%, p < 0.001), and higher mortality (4.2% vs. 1.2%, p < 0.001). Conclusions: Major abdominal surgery and injury severity are primary risk factors for deep SSIs in trauma patients, with profound impacts on clinical outcomes and healthcare resource utilization. These findings highlight the importance of targeted prevention strategies for high-risk trauma patients undergoing major abdominal procedures and emphasize the significant burden that deep SSIs place on healthcare systems. Full article

Background/Objectives: Healthcare workers (HCW) have increased the risk of occupational stress injuries and adverse mental health outcomes, which were exacerbated during the COVID-19 pandemic. Understanding HCW psychological distress patterns and help-seeking behaviors can inform responsive resource development that may mitigate negative outcomes in future crises. This paper provides insights on monthly trends in HCW distress and support utilization at a large Canadian hospital over a 14-month period. Methods: As part of a hospital-wide wellness initiative during COVID-19, the STEADY program emailed monthly confidential wellness assessments to hospital staff from April 2020 to May 2021. The assessments included screens for burnout, anxiety, depression and posttraumatic stress, types of support accessed, and demographic information. Repeated cross-sectional data were summarized as monthly proportions and examined alongside longitudinal COVID-19 data. Results: A total of 2498 wellness assessments were submitted (M = ~168 monthly, range: 17–945). Overall, 67% of assessments had at least one positive screen for distress. Average positive screens were 44% for anxiety, 29% for depression, 31% for posttraumatic stress, and 53% for burnout. Despite high distress, most respondents used informal supports (e.g., family/friends), highlighting limited formal support use. Conclusions: HCWs experienced sustained high levels of psychological distress during the COVID-19 pandemic, with burnout remaining a predominant and persistent concern. The limited use of formal support services may indicate barriers to accessing these types of supports. Our findings underscore the need for accessible and acceptable mental health supports for HCW during prolonged crises. Full article

Background: The emergency department (ED) often represents the entry point to care for patients that require urgent medical attention or have no alternative for medical treatment. This has implications on scope of practice and how quality of care is measured. A diverse array of methodologies has been developed to evaluate the quality of clinical care and broadly includes quality improvement (QI), quality assurance (QA), observational research (OR) and clinical quality registries (CQRs). Considering the overlap between QI, QA, OR and CQRs, we conceptualized a modular framework for TECOR to effectively and efficiently streamline clinical quality evaluations. Streamlining is both appropriate and justified as it reduces redundancy, enhances clarity and optimizes resource utilization, thereby allowing clinicians to focus on delivering high-quality patient care without being overwhelmed by excessive data and procedural complexities. The objective of this study is to describe the process for designing a modular framework for ED CQRs using TECOR as a case study. Methods: We performed a scoping audit of all quality projects performed in our ED over a 1-year period (1 January 2021 to 31 December 2021) as well as data mapping and categorical formulation of key themes from the TECOR dataset with clinical data sources. Both these processes then informed the design of TECOR. Results: For the audit of quality projects, we identified 29 projects. The quality evaluation methodologies for these projects included 12 QI projects, 5 CQRs and 12 OR projects. Data mapping identified that clinical information was fragmented across 11 distinct data sources. Through thematic analysis during data mapping, we identified three extraction techniques: self-extractable, manual entry and on request. Conclusions: The modular framework for TECOR aims to enable an efficient streamlined approach that caters to all aspects of clinical quality evaluation to enable higher throughput of clinician-led quality evaluations and improvements. TECOR is also an essential component in the development of a learning health system to drive evidence-based practice and the subject of future research. Full article

Background: Residency training is a formative and rigorous experience, with burnout rates reported at 76%. Formal peer support groups have shown improvement in burnout among healthcare workers with anxiety and depression. Objective: Implement a peer support program for emergency medicine (EM) residents and characterize utilization of metrics by demographics, burnout rates of participants, and overall session impact. Methods: An IRB-approved, longitudinal, prospective cohort study of 73 EM and EM/Pediatrics residents post-graduate year (PGY) 1–5 from July 2021–June 2022 was performed. Resident peer leaders were trained using a novel curriculum to lead peer support groups. Residents were invited to participate in biweekly sessions, with optional pre- and post-session surveys measuring demographics, burnout, themes discussed, and how they felt after sessions (Patients’ Global Impression of Change scale). Results: There were 134 attendances over 20 sessions, averaging 6 residents per session. Of 73 total residents, 37 (50%) participated at least once. All levels of training were represented, with half being female, 20% underrepresented in medicine, and 14% LGBTQ+. Overall burnout rates were unchanged for first-time attendances (49%, n = 18) vs. recurrent (50%, n = 11). Females had higher burnout at both baseline (60%, n = 15) and recurrent sessions (69%, n = 13). Following sessions, 94% of participants reported feeling immediately better and 100% of leaders felt prepared leading peer support sessions. Conclusions: This study demonstrates that residents utilize peer support, with many returning more than once. Despite stable burnout rates, 94% of participants felt immediately better after the session, suggesting that peer support is a valuable resource for residents actively experiencing burnout. Full article

Introduction: Visual impairment impacts millions of people around the world, with the vast majority of problems being treatable. Disadvantaged communities are unable to utilize the same resources to treat these problems due to a lack of knowledge or resources, in addition to the presence of barriers preventing access. The objective of this paper is to assess eye health awareness and evaluate the barriers for individuals from disadvantaged communities in order to inform future interventions and increase access to care. Methods: This is a pilot study utilizing an online anonymous questionnaire designed to assess the demographics, eye health awareness, and access to eye care of community-based patients. A comprehensive literature review was also conducted using PubMed, Scopus, and Google Scholar to evaluate barriers to eye care and methods to improve community health outcomes. The primary goal was to improve understanding of eye health awareness and access in order to inform future strategies that can help in improving eye health awareness and service availability. Results: The results indicated that 61.2% of respondents believed that eye exams are very important, and only 7.7% of participants believed that regular eye exams are not important. The majority of participants (75%) agree that regular eye exams help prevent serious eye conditions and 84.5% believe that eye health can affect quality of life. 35.6% of participants reported they had their eyes checked by a healthcare professional within the last year, while 21.2% reported never having an eye exam. Although the majority of participants found access to eye care services in their community somewhat or very easy, 8.6% and 9.5% of participants found access difficult and very difficult, respectively. Even though 45.6% of participants reported not facing any barriers regarding access to eye care, the cost of services, long waiting times, and lack of nearby eye care providers were often cited as barriers from the remainder of the participants. Moving forward, local interventions such as mobile eye clinics, public health workshops, and telehealth are viable options to obtain an understanding of the community’s health status in addition to creating opportunities to educate and provide health screenings. Conclusion: The results indicate that although there is awareness of the importance of eye health for the majority of participants, there is still a sizable minority who have insufficient understanding. Barriers to healthcare such as cost, waiting times, and proximity to providers are common problems that are preventing many from seeking eye care. Future interventions should be created to increase access and literacy amongst the community through telehealth, mobile eye clinics, and public health workshops. Additional efforts should be taken by healthcare stakeholders to enhance care delivery, implement policies, and improve awareness. Full article

Background/Objectives: The pediatric mental health crisis in the United States has reached unprecedented levels. Severe shortages in specialized health care professionals, particularly child and adolescent psychiatrists (CAPs), exacerbate the challenge of delivering timely and quality mental health care, especially in rural areas like Kansas. Innovative models such as Pediatric Mental Health Care Access (PMHCA) programs and School-Based Health Clinics (SBHCs) aim to integrate mental health expertise into primary care settings to address this gap. Methods: This paper examines an integrated care model to support SBHCs developed by the Kansas PMHCA. The Interprofessional Child-Centered Integrated Care Model (ICX2) was implemented within an SBHC in Haysville, KS. ICX2 utilizes biweekly collaborative team meetings (CTMs) via zoom involving primary care, psychology, child psychiatry, social work, and school resource coordinators to discuss patient cases and enhance the primary care management of pediatric mental health. This descriptive study analyzes data from January 2023 to June 2023, focusing on patient demographics, case characteristics discussed during CTMs, and recommendations made by the interprofessional team. Results: Findings illustrate the complex biopsychosocial needs of patients seen and define themes of case consultation and recommendations. Conclusions: Integrated care programs like ICX2 can be feasibly implemented through PMHCA programs and may be an efficient intervention to bridge resource gaps. Full article

Background/Objectives: The aims of this study were to re-evaluate the necessity routine post-biopsy chest X-rays by evaluating the incidence, timing, and clinical relevance of pneumothorax (PTX) following CT-guided lung biopsies, and to determine whether symptom-based monitoring can safely replace routine imaging. Methods: This retrospective, single-center study included 112 patients (mean age: 69.3 years; 55% male) who underwent CT-guided lung biopsy between 9 January 2020 and 16 April 2025. PTX occurrence was assessed both intraprocedurally and during follow-up within 7 days. The primary outcome was the development of PTX; secondary outcomes were chest drainage need and delayed PTX identification. Logistic regression analysis and descriptive statistics were used to identify predictors and associations. Results: Intra-procedural PTX occurred in 43.8%, of whom 10.7% required immediate drainage. Clinically silent PTX that did not require intervention occurred in 25.9%. Routine chest X-rays were obtained 4 h post-biopsy for all patients. Importantly, no asymptomatic patient required any intervention. These results suggest that routine 4 h imaging may be unnecessary in the absence of symptoms. Conclusions: PTX is frequent after CT-guided lung biopsy but is often asymptomatic and self-limiting. The absence of clinically significant findings among asymptomatic patients supports a shift toward symptom-based monitoring. Implementing this strategy may help reduce unnecessary post-biopsy imaging and optimize healthcare resource utilization without compromising patient safety. Full article