Search Results (435)

The healthcare needs of refugees and people seeking asylum are often broad and complex, with a higher burden of communicable diseases. There are limited data describing migrants’ experiences of accessing healthcare in Ireland. This cross-sectional study describes the experiences of migrants accessing healthcare services through an Irish Infectious Diseases clinic. Individuals attending the infectious diseases services in our hospital who had migrated to Ireland were included. Data were collected via a questionnaire, focusing on factors that may limit access to care, including communication, accessibility, cost, and stigmatisation. Seventy-six patients participated in this study. N = 20 (26%) of patients reported a commuting time of more than two hours to attend our clinic. N = 11 (15%) had experienced being unable to access healthcare in Ireland due to cost. Trust in healthcare providers was high (88%), and patient-reported satisfaction with communication was high (>90%). Persons living in direct provision services were more likely to report issues around privacy and less likely to have registered with a general practitioner. Accessibility and privacy were among the biggest challenges faced by migrants attending infectious diseases services at our centre, while communication and trust in healthcare providers were identified as areas of strength. Considering the burden of infectious diseases in migrant populations, and the challenges that certain migrant populations face in accessing healthcare, it is important to identify potential barriers to accessing care in order to ensure equitable, effective care. This study seeks to identify and describe the challenges that migrants face when accessing care through an Irish infectious diseases clinic. The results can help inform service provision and allocation of resources at a local level, while also identifying an area for further research regarding the barriers to accessing care faced by migrant communities in Ireland. Full article

Background: Intensive Care Unit (ICU) readmission and in-hospital mortality are critical indicators of patient outcomes following ICU discharge. Patients readmitted to the ICU often face worse prognosis, higher healthcare costs, and prolonged hospital stays. Identifying high-risk patients is essential for optimizing post-ICU care and resource allocation. Methods: This two-phase study included the following: (1) a retrospective analysis of ICU survivors in a mixed medical–surgical ICU to identify risk factors associated with ICU readmission and in-hospital mortality, and (2) a prospective validation of a newly developed predictive model: the Worse Outcome Score (WOScore). Data collected included demographics, ICU admission characteristics, severity scores (SAPS II, SAPS III, APACHE II, SOFA), interventions, complications and discharge parameters. Results: Among 1.190 ICU survivors, 126 (10.6%) were readmitted to the ICU, and 192 (16.1%) died in hospital after ICU discharge. Key risk factors for ICU readmission included Diabetes Mellitus, SAPS III on admission, and ICU-acquired infections (Ventilator-Associated Pneumonia (VAP) and Catheter-Related Bloodstream Infection, (CRBSI)). Predictors of in-hospital mortality were identified: medical admission, high SAPS III score, high lactate level on ICU admission, tracheostomy, reduced GCS at discharge, blood transfusion, CRBSI, and Acute Kidney Injury (AKI) during ICU stay. The WOScore, developed based on the results above, demonstrated strong predictive ability (AUC: 0.845 derivation, 0.886 validation). A cut-off of 20 distinguished high-risk patients (sensitivity: 88.1%, specificity: 73.0%). Conclusions: ICU readmission and in-hospital mortality are influenced by patient severity, underlying comorbidities, and ICU-related complications. The WOScore provides an effective, easy-to-use risk stratification tool that can guide clinicians in identifying high-risk patients at ICU discharge and guide post-ICU interventions, potentially improving patients’ outcomes and optimizing resource allocation. Further multi-center studies are necessary to validate the model in diverse healthcare settings. Full article

Cybersecurity is increasingly recognized as a key enabler of Sustainable Development Goals (SDGs) and especially SDG 3 (Good Health and Well-being) as healthcare systems become more digitized. This study prioritizes cybersecurity control families from the NIST 800-53r5 framework using a structured framework combining the Analytic Hierarchy Process (AHP) and the Impact–Feasibility Matrix. From the impact–feasibility perspective, expert judgment reveals that while impact is the primary driver in selecting controls, feasibility—particularly budget and cost constraints—plays a decisive role in real-world implementation. A group of fifteen experts, including cybersecurity officers, health IT professionals, and public health advisors, has participated in structured surveys as per the methodological framework of this paper. Financial and budgetary limitations emerged as the top feasibility barrier, often determining whether high-impact controls are deployed or delayed. This underscores the need for strategic investments and phased implementation approaches, particularly in resource-constrained health systems. The results provide a practical roadmap for policymakers and healthcare administrators to allocate cybersecurity resources effectively, balancing technical necessity with economic feasibility to support resilient digital health infrastructures. Full article

Background: Hip fractures are common in the elderly and often require ICU admission post-surgery due to high ASA scores and comorbidities. Length of hospital stay after ICU is a crucial indicator affecting patient recovery, complication rates, and healthcare costs. This study aimed to develop and validate a machine learning-based model to predict the factors associated with extended hospital stay (>7 days from surgery to discharge) in hip fracture patients requiring postoperative ICU care. The findings could help clinicians optimize ICU bed utilization and improve patient management strategies. Methods: In this retrospective single-centre cohort study conducted in a tertiary ICU in Turkey (2017–2024), 366 ICU-admitted hip fracture patients were analysed. Conventional statistical analyses were performed using SPSS 29, including Mann–Whitney U and chi-squared tests. To identify independent predictors associated with extended hospital stay, Least Absolute Shrinkage and Selection Operator (LASSO) regression was applied for variable selection, followed by multivariate binary logistic regression analysis. In addition, machine learning models (binary logistic regression, random forest (RF), extreme gradient boosting (XGBoost) and decision tree (DT)) were trained to predict the likelihood of extended hospital stay, defined as the total number of days from the date of surgery until hospital discharge, including both ICU and subsequent ward stay. Model performance was evaluated using AUROC, F1 score, accuracy, precision, recall, and Brier score. SHAP (SHapley Additive exPlanations) values were used to interpret feature contributions in the XGBoost model. Results: The XGBoost model showed the best performance, except for precision. The XGBoost model gave an AUROC of 0.80, precision of 0.67, recall of 0.92, F1 score of 0.78, accuracy of 0.71 and Brier score of 0.18. According to SHAP analysis, time from fracture to surgery, hypoalbuminaemia and ASA score were the variables that most affected the length of stay of hospitalisation. Conclusions: The developed machine learning model successfully classified hip fracture patients into short and extended hospital stay groups following postoperative intensive care. This classification model has the potential to aid in patient flow management, resource allocation, and clinical decision support. External validation will further strengthen its applicability across different settings. Full article

Background and aim: Patient satisfaction is a critical indicator of healthcare quality, shaping treatment adherence, continuity of care, and the allocation of resources. The Short Assessment of Patient Satisfaction (SAPS) is a brief, reliable tool that is widely used internationally, but no validated Arabic version currently exists. Therefore, this study aimed to translate, culturally adapt, and validate the SAPS into Arabic for use in primary care clinics. Methods: We conducted a cross-sectional validation study at general practice clinics of a tertiary academic hospital in Riyadh, Saudi Arabia (June–August 2025). Consecutive Arabic-speaking patients aged 18–80 were recruited post-visit and completed a self-administered electronic survey including the Arabic Short Assessment of Patient Satisfaction (Ar-SAPS), PSQ-18, and PDRQ-9, as well as demographic and visit variables. Psychometric testing included internal consistency, test–retest reliability, construct validity, and factor analysis. Results: A total of 273 participants enrolled in our study. The Ar-SAPS demonstrated good reliability (Cronbach’s α = 0.789; McDonald’s ω = 0.882) and moderate test–retest stability (ICC = 0.634, p < 0.0001). Factor analysis supported a primarily unidimensional structure, with the first factor explaining 60.2% of variance. Most inter-item correlations were moderate to strong, except for item 6. Convergent validity was supported by significant correlations with the Arabic PDRQ-9 (r = 0.623, p < 0.001, CI [0.532, 0.713]) and PSQ-18 (r = 0.662, p < 0.001, CI [0.531, 0.793]), confirming consistency with established measures of patient satisfaction. Furthermore, it demonstrated excellent discriminative ability, with areas under the curve of 0.965 for overall satisfaction and 0.955 for willingness to recommend. Conclusion: The Ar-SAPS is valid and reliable for use to assess patient satisfaction. Full article

Introduction: Traumatic brain injury (TBI) represents one of the leading health concerns worldwide, and it is associated with high morbidity, mortality, and substantial healthcare costs. This study aimed to assess inpatient cost determinant factors among TBI patients admitted to an Eastern European hospital, Cluj County Emergency Hospital, Romania, in 2022. Methods: A retrospective observational analysis was conducted on 90 TBI patients. Data on demographic factors, clinical variables, injury characteristics, and inpatient hospital costs were collected. Inpatient total cost differences considering categorical variables were analyzed using Wilcoxon and Kruskal–Wallis tests, and correlations of inpatient total costs with other continuous variables were analyzed using Spearman correlations. Results: Most patients were male (67.8%), urban residents (67.8%), retired (64.4%), and had a mild TBI (96.7%), and the main listed cause was falls (74.4%). The average inpatient cost was EUR 1115.79. There were no statistically significant differences for costs in TBI severity, PTA, or comorbidities. Inpatient costs were correlated with hospital length of stay (ρ = 0.979, 95% CI rho: 0.969 and 986, p < 0.001). While higher costs were seen in patients with PTA, more comorbidities, severe Marshall Scores, or return-to-work status, these differences were not statistically significant. Conclusions: Further research with larger, multicenter cohorts is needed to better understand the cost structure of TBI care and to inform policy decisions that are aimed at resource allocation and cost efficiency. Full article

Objectives: We conducted a systematic review of clinical trials and case reports analyzing the safety of the currently approved BRAF and MEK inhibitors in adults with cutaneous melanoma (CM), and a meta-analysis to estimate the pooled prevalence of treatment-related adverse events (TRAEs). Methods: We systematically searched six databases for studies published since 2009. The TRAE absolute frequencies reported in primary studies were aggregated using the Metaprop command in Stata 17, which calculates 95% confidence intervals (CIs) incorporating the Freeman–Tukey double arcsine transformation of proportions to stabilize variances within random-effect models. Methodological quality was assessed using the RoB 2 tool for randomized controlled trials (RCTs) and the ROBINS-I tool for non-randomized studies. Results: Twelve RCTs, thirteen prospective cohort studies (PCSs), and ten case reports were included. Meta-analysis was feasible for two regimens: vemurafenib 960 mg monotherapy and dabrafenib 150 mg twice daily plus trametinib 1–2 mg daily. The most common TRAEs during vemurafenib treatment were musculoskeletal and connective-tissue disorders (24%, 95% CI: 6–41%, p = 0.01), with arthralgia as the most prevalent (44%, 95% CI: 29–59%, p < 0.001), followed by rash (39%, 95% CI: 22–56%, p < 0.001). The most common TRAEs during dabrafenib plus trametinib were constitutional toxicities (classified in CTCAE as ‘General disorders and administration site conditions’; 25%, 95% CI: 14–37%, p < 0.001), with fatigue as the most prevalent (47%, 95% CI: 38–56%, p < 0.001), followed by pyrexia (40%, 95% CI: 26–54%, p < 0.001). Squamous cell carcinoma and keratoacanthoma were among the most frequent grade ≥ 3 cutaneous adverse events observed with vemurafenib therapy. Conclusions: Although additional large-scale studies are needed to corroborate these findings, each treatment has a distinct toxicity profile that should be considered when developing personalized risk-stratified treatment plans and in guiding healthcare resource allocation in melanoma care. Full article

Objectives: To determine the prescribing prevalence of epidermal growth factor receptor (EGFR)- and anaplastic lymphoma kinase (ALK)-positive non-small cell lung cancer (NSCLC) patients in Greece and examine patterns of first-line tyrosine kinase inhibitor (TKI) utilization and associated treatment costs using nationwide real-world data. Methods: A retrospective analysis of the national e-prescription database was performed, identifying patients initiating first-line treatment (FLT) for EGFR- or ALK-positive NSCLC between 1 January 2020 and 31 December 2022. Demographic characteristics, prescribing prevalence data, drug utilization patterns, total annual drug expenditures, and per patient treatment costs were assessed. All statistical analyses were performed using the statistical software SPSS-v.29. Results: Overall, 1188 EGFR-positive (mean age of 70.93 ± 11.6) and 246 (mean age of 64.26 ± 12.6) ALK-positive NSCLC patients initiated FLT during the three-year study period. EGFR mutations were slightly more common in females (53%), peaking in the 70–79 age group (35%). ALK mutations were also more common among females (52%), particularly within the 60–79 age group. In EGFR-positive patients, osimertinib usage markedly increased from 41% in 2020 to 63% in 2022, primarily displacing afatinib (from 32% to 22%) and erlotinib (from 24% to 14%), with gefitinib prescriptions falling below 2%. Among ALK-positive patients, crizotinib utilization declined significantly from 60% to 16%, whereas alectinib increased to 59% by 2022. Annual EGFR-related total drug expenditures remained stable (€11.5 million in 2020 vs. €11.9 million in 2022), driven primarily by increasing osimertinib usage. Similarly, ALK-related annual drug expenditures showed stability, with costs predominantly attributed to rising alectinib utilization. Conclusions: This nationwide analysis highlights the rapid adoption of second- and third-generation TKIs for EGFR- and ALK-positive NSCLC in Greece, reflecting evolving clinical practice patterns. Although the target patient populations are relatively small, the associated economic burden is considerable. To ensure long-term sustainability of the Greek healthcare system, policymakers should critically assess the cost-effectiveness of these innovative therapies and align resource allocation with value-based care principles. Full article

The Internet of Medical Things (IoMT) is a rapidly expanding network of medical devices, sensors, and software that exchange patient health data. While IoMT supports personalized care and operational efficiency, it also introduces significant privacy risks, especially when handling sensitive health information. Data Identification and Classification (DIC) are therefore critical for distinguishing which data attributes require stronger safeguards. Effective DIC contributes to privacy preservation, regulatory compliance, and more efficient data management. This study introduces SDAIPA (SDAIA-HIPAA), a standardized hybrid IoMT data classification framework that integrates principles from HIPAA and SDAIA with a dual risk perspective—uniqueness and harm potential—to systematically classify IoMT health data. The framework’s contribution lies in aligning regulatory guidance with a structured classification process, validated by domain experts, to provide a practical reference for sensitivity-aware IoMT data management. In practice, SDAIPA can assist healthcare providers in allocating encryption resources more effectively, ensuring stronger protection for high-risk attributes such as genomic or location data while minimizing overhead for lower-risk information. Policymakers may use the standardized IoMT data list as a reference point for refining privacy regulations and compliance requirements. Likewise, AI developers can leverage the framework to guide privacy-preserving training, selecting encryption parameters that balance security with performance. Collectively, these applications demonstrate how SDAIPA can support proportionate and regulation-aligned protection of health data in smart healthcare systems. Full article

Objectives: This study aims to examine the impact of Diagnosis-Related Group (DRG) payment on medical costs, efficiency, and quality of healthcare services in public hospitals, providing policy recommendations for further health insurance payment reforms in China. Methods: Utilizing inpatient medical insurance settlement data from 2020 to 2023 in the selected city, we constructed a regression discontinuity design (RDD) and an interrupted time series (ITS) model to evaluate the causal effects of the DRG reform. The analysis includes 66,533 inpatient settlement records. Results: Following the reform, the average length of stay (LOS) decreased by 2 days (95% CI: −3.43 to −0.70, p < 0.01), total hospitalization expenditures dropped by 13% (95% CI: −0.26 to −0.00, p < 0.05), and expenditures from the medical insurance fund declined by 25% (95% CI: −0.39 to −0.12, p < 0.01). Additionally, examination and consultation fees were reduced by 23% (95% CI: −0.41 to −0.05, p < 0.05), although patients’ out-of-pocket burden increased by 8% (95% CI: 0.05 to 0.10, p < 0.01). In terms of healthcare quality, the 30-day readmission rate decreased by 1% (95% CI: −0.01 to −0.00, p < 0.01), and the mortality rate among low-risk patients declined by 4% (95% CI: −0.04 to −0.03, p < 0.01). We found no evidence of patient selection or denial of admission. Heterogeneity analysis revealed that the reduction in hospital stay was concentrated among enrollees under the Urban and Rural Resident Basic Medical Insurance and those treated in secondary hospitals. The policy’s effects peaked shortly after implementation but gradually attenuated over time. Conclusions: Our study offers hospital-level evidence indicating that the initial stage of DRG implementation achieved its preliminary goals of optimizing medical resource allocation and improving the efficiency of medical insurance fund utilization. However, the reform still faces several challenges. These findings may offer valuable references for developing countries pursuing reforms in primary healthcare and health insurance payment systems. Full article

Background/Objectives: Children with Autism Spectrum Disorder (ASD) who attend pediatric emergency services face challenges related to their sensory, cognitive, and behavioral characteristics. This study explored the perceptions of healthcare professionals and parents regarding the need to implement adaptations, particularly a sensory-adapted room, for children with ASD in pediatric emergency departments. Methods: A phenomenological qualitative study was conducted through semi-structured interviews (October–December 2024) until data saturation. Participants included healthcare professionals and parents of children diagnosed with ASD. Intentional coding and co-occurrence analysis were performed using Atlas.ti (version 25.0.1). The study was approved by the Research Ethics Committee (code: 204-458-1). Results: Eighteen informants participated (10 professionals and 8 parents). Professionals’ interviews revealed three themes and eight subthemes: Professional Training (approach strategies; training received; perceived needs), Hospital Environment (resource allocation; infrastructure; perceived needs during the emergency visit), and Emotional Aspects (emotional experience related to patient care; professionals’ personal perceptions). Parents’ interviews yielded four themes and ten subthemes: Professional Training (perceptions of staff training; demonstrated emotional competencies; socioemotional relationships during care), Hospital Environment (infrastructure; perceived needs during emergency visits), Emotional Aspects (families’ experiences; emotions during care), and ASD (diagnostic characteristics; children’s needs; sensory regulation). Conclusions: Pediatric emergency services should be adapted to better meet the needs of children with ASD. Both healthcare professionals and parents recognize the importance of such adaptations, particularly sensory-adapted spaces. The main barriers identified were a lack of professional training, inadequate hospital environments, and stress affecting both patients and provider. Priority measures include continuous ASD-specific training programs, improvements in sensory infrastructure, and more flexible clinical protocols, advancing toward a more inclusive and comprehensive model of care. Full article

Dystrophinopathies, including Duchenne and Becker muscular dystrophies (DMD and BMD), are inherited neuromuscular disorders frequently complicated by progressive cardiac involvement, ultimately leading to advanced heart failure. While heart transplantation and long-term left ventricular assist device (LVAD) therapy represent potential therapeutic options, their application in this population raises significant ethical challenges. This review explores the ethical implications surrounding the allocation of scarce medical resources, particularly in patients with limited life expectancy and multisystem disease, as in DMD. Decisions regarding eligibility for heart transplantation must balance individual benefit, considering the impact of excluding other potential recipients. LVAD therapy, although more accessible, still demands careful patient selection due to high perioperative risk and postoperative complications. The review emphasizes the need for transparent, multidisciplinary decision-making processes that respect patient autonomy while ensuring equitable and rational distribution of healthcare resources. Ultimately, while advanced therapies may be feasible in selected cases, particularly in BMD, ethical deliberation remains central to determining their appropriateness in the context of dystrophinopathies. Full article

The recent COVID-19 pandemic outbreak has demonstrated all the limitations of modern healthcare information systems in preventing and controlling pandemics, especially following an unexpected event. Existing approaches often fail to integrate real-time data and adaptive learning mechanisms, leading to inefficient response strategies and resource allocation challenges. To address this gap, in this paper, we propose PROTECTION, an innovative data-centric process-modeling-managing-and-mining framework for pandemic control and prevention that is based on the new paradigm that we name Knowledge-, Decision- and Data-Intensive (KDDI) processes. PROTECTION adopts Business Process Model and Notation (BPMN) as a standardized approach to model and manage complex healthcare workflows, enhancing interoperability and formal process representation. PROTECTION introduces a structured methodology that integrates Big Data Analytics, Process Mining and Adaptive Learning Mechanisms to dynamically update healthcare processes in response to evolving pandemic conditions. The framework enables real-time process optimization, predictive analytics for outbreak detection, and automated decision support for healthcare. Through case studies and experimental validation, we demonstrate how PROTECTION can effectively deal with the complex domain of pandemic control and prevention. Full article

Emergency departments worldwide face challenges in managing fluctuating patient demand, which is often inadequately addressed by traditional forecasting methods due to the inherent nonlinearities of data. The purpose of this study is to propose a short-term prediction model for daily attendance in a private emergency healthcare unit in southern Brazil. The study employed seven years of historical data to compare the performance of ARIMA, Artificial Neural Networks (ANNs), and the chaotic logistic map model to forecast next-day arrivals in two specialties, general clinic and pediatric. The errors for the general practitioner and the pediatricians of the ARIMA, ANN, and logistic map models were, respectively, [0.31%, 2.54%, 2.17%] and [32.72%, 10.11%, 7.85%], measured by MAPE (mean absolute percentage error). The logistic map ranked second and first place, respectively, providing acceptable results in both cases. The main innovation is the successful application of a chaotic model, specifically the logistic map, exclusively for one-day prediction variables in the management of health and medical services. In particular, for the pediatrician, a most irregular time series, the logistic map provided the better outcome. For professionals, the study offers an accurate tool for optimizing the allocation of human and material resources and supporting daily strategic decisions. For scholars, it opens research avenues, addressing a gap in the body of knowledge on chaotic models that have not yet been extensively explored in healthcare service demand one-day forecasting. Full article

Background and Objective: Health services management faces increasing complexity, particularly in developing countries such as Brazil. Digital tools play a central role in optimizing health service operations, yet synthesized evidence on manager-focused applications remains limited. This study aimed to survey digital innovations for management within the Brazilian context. Methods: We systematically reviewed the complete proceedings of the Brazilian Symposium on Computing Applied to Health (SBCAS) from 2001 to 2024, identifying 26 studies that met eligibility criteria based on managerial relevance. Results: Applications identified predominantly addressed hospital management (e.g., resource scheduling and process optimization) and public health surveillance (e.g., disease prediction and monitoring), employing technologies such as machine learning and simulation. These tools primarily leveraged structured administrative data from national health information systems, reflecting existing data infrastructure capabilities. The reported implications suggest improvements in decision-making through optimized resource allocation (e.g., ICU beds and staffing), streamlined operational processes (e.g., bottleneck identification), enhanced planning and monitoring capabilities (e.g., endemic disease control and telemonitoring programs), and more timely, targeted public health surveillance (e.g., georeferenced analysis). Conclusions: The identified research aligns with global digital health trends but is also tailored to the complex realities of the healthcare system. Despite significant technical advancements, these digital solutions predominantly remain at the prototype stage, highlighting a gap between academic innovation and real-world deployment. Realizing the benefits of these tools will require a concerted effort to move beyond technical validation, focusing on implementation science, supportive policies, and strategic partnerships to integrate these solutions into managerial practice. Full article