Search Results (12,334)

Background: Accurate preoperative prediction of length of hospital stay (LOS) after surgery for colorectal liver metastases (CRLMs) could improve patient counselling and resource planning, yet reliable risk tools are lacking. We aimed to develop an interpretable machine learning model predicting LOS following first-time liver-directed surgery for CRLMs. Methods: In this multicenter cohort study, we included patients who underwent first-time liver resection, ablation, or a combination for CRLMs at three Danish hepatobiliary centers between 2016 and 2023. Preoperative features from two national registries were used to train Elastic Net, Random Forest, HistGradientBoosting, and Explainable Boosting Machine (EBM) algorithms. Hyperparameters were optimized using five-fold cross-validation. Performance was evaluated on a 20% hold-out test sample using mean absolute error (MAE) with bootstrapped 95% confidence intervals (CIs). Results: Among 915 patients, median LOS was 4.0 days (interquartile range (IQR) 3.0–6.0). All four algorithms achieved comparable prediction error (MAE 3.0–3.1 days). The EBM (MAE 3.1 days, 95% CI 2.6–4.3) algorithm was selected for its inherent interpretability. Surgical approach was the strongest predictor, where percutaneous and laparoscopic approaches were associated with reductions of 1.9 and 1.2 days, respectively. Tumor burden, including number of lesions and largest lesion diameter, showed progressive non-linear associations with longer stays. Nonetheless, overall explained variance was low (R² ≤ 0.10), and calibration showed systematic underestimation of stays beyond five days. Conclusions: An inherently interpretable machine learning model matched the predictive performance of opaque algorithms for LOS after CRLM surgery, although overall predictive accuracy was modest and longer stays were underestimated. Explainability analysis identified surgical approach and tumor burden as the most influential predictors. External validation in healthcare systems with different discharge practices is warranted. Full article

The health system is now more complex, uncertain, interdependent, and dynamically interconnected than ever, making traditional planning decisions based on static, reductionist models increasingly impracticable. Systems analytics approaches such as system dynamics, agent-based modeling, and network analysis are often deployed in isolation and fail to capture cross-level interactions and emergent system behavior. This study proposes an integrated multi-layer systems analytics framework that combines these analytical paradigms within a unified architecture to support adaptive health systems planning under uncertainty. The proposed framework introduces an Adaptive Policy Intelligence Layer (APIL), which enables continuous feedback-driven policy adaptation through dynamic monitoring, scenario evaluation, and real-time adjustment mechanisms. The model is evaluated under multiple simulation scenarios, including baseline conditions, demand shocks, resource constraints, and digital transformation environments. The findings provide strong quantitative and analytical evidence of improved system performance and resilience. More specifically, the digital transformation scenario achieved the lowest mean system pressure (0.128) and the highest resilience index (0.887), while the demand shock scenario produced the highest peak system pressure (0.306). The results demonstrate enhanced system resilience, more efficient resource deployment, and superior policy responsiveness compared with traditional single-method approaches. The originality of this study lies in integrating multi-level systems analytics with adaptive policy intelligence into a unified, feedback-driven decision-support framework for resilient health systems governance. The study contributes to systems analytics literature by advancing a synergistic and adaptive modeling paradigm capable of supporting policymakers in navigating complex and unstable healthcare environments. Full article

Background: Medication errors remain a major threat to patient safety in acute care settings worldwide and are associated with preventable morbidity, mortality, and increased healthcare costs. Nurses play a critical role in identifying, intercepting, and preventing medication-related harm. However, limited qualitative evidence has explored nurses’ perspectives on medication safety within the Saudi Arabian healthcare context. This study explored nurses’ experiences of medication safety, perceived systemic challenges, and strategies for error prevention in Saudi hospitals. Methods: A qualitative descriptive design was employed. Fourteen (n = 14) nurses from two major referral hospitals in Saudi Arabia participated in semi-structured face-to-face interviews. Interviews were audio-recorded, transcribed verbatim, and analyzed using Braun and Clarke’s six-phase thematic analysis framework. Results: Five overarching themes were identified: (1) Communication gaps; (2) Medication processes; (3) Technology and safety; (4) Workload and staffing; and (5) Staff competence. Participants described how communication failures, staffing pressures, workflow interruptions, and documentation ambiguities compromised medication safety. While barcode systems and EHRs were perceived as valuable safeguards, participants emphasized that their effectiveness depended on staff vigilance, adequate training, and supportive workplace cultures. Conclusions: Medication safety is a dynamic socio-technical process shaped by communication, competence, staffing capacity, and human interaction with technology. Improving safety requires integrated organizational strategies that combine workforce investment, structured communication practices, continuous professional education, and non-punitive incident reporting cultures. These findings provide practical insights for healthcare leaders seeking to strengthen medication safety systems in Saudi Arabia and comparable settings. Full article

Anxiety disorders pose an increasing challenge to the mental health of individuals, particularly in regions with limited healthcare access. This study investigated the potential of integrating a brain–computer interface for processing electroencephalography (EEG) data with deep learning models to accurately classify anxious and non-anxious states. In the first phase, a convolutional neural network (CNN) was developed and validated on the public GAMEEMO dataset, achieving a classification accuracy of 95.72%. In the second phase, we conducted a separate experimental validation with seven participants (aged 18–60 years) using a within-subjects design. The protocol comprised a custom Stroop test to elicit acute cognitive stress and anxiety-related arousal, followed by a guided 4–7–8 breathing exercise to induce relaxation. EEG data from this experiment were used to classify anxious versus non-anxious states with the same CNN architecture after domain adaptation. On this self-collected dataset, the CNN achieved an accuracy of 86.58%. These results demonstrate proof-of-concept transferability while highlighting the performance gap between controlled benchmark data and real-world, small-sample recordings. The deep learning model can subsequently be coupled with neurofeedback techniques to manage anxiety levels. Overall, the findings support the potential of the developed automated system for detecting stress-induced anxious states, with possible future integration into neurofeedback-based management systems. Full article

Endometrial hyperplasia (EH) is an estrogen-driven proliferative disorder with a measurable risk of progression to endometrial carcinoma. Although many guidelines have been issued over the years, clinical practice remains heterogeneous. In this paper, we aim to compare and summarize key recommendations and disagreements among major international guidelines for managing endometrial hyperplasia, focusing especially on conservative and fertility-sparing strategies. All guidelines align with some key principles: they all adopt the 2020 WHO classification, strongly prefer hysteroscopy-directed sampling, and recommend progestin therapy as the first-line treatment for non-atypical EH, favoring the levonorgestrel-releasing intrauterine system (LNG-IUS) over oral regimens. They designate total hysterectomy as definitive management for atypical hyperplasia/intraepithelial endometrial neoplasia (AEH/EIN) due to the substantial prevalence of concurrent carcinoma. Nevertheless, several key discrepancies appear, mainly concerning how long to continue progestin therapy and when to escalate treatment; and how intensively and for how long to conduct post-treatment surveillance. Variations in diagnostic and therapeutic protocols reflect evidence gaps and differences across healthcare settings. Future research should focus on harmonized outcomes, comparative studies of conservative strategies, and the integration of new pathology tools for personalized management. Full article

Background: Cardiovascular disease (CVD) remains the leading cause of mortality worldwide, with over 75% of deaths occurring in low- and middle-income countries, where conventional risk models often demonstrate poor calibration and limited generalizability. Objective: This study aimed to develop an interpretable, pharmacotherapy-informed machine learning model for cardiovascular risk prediction using national electronic health record (EHR) data from Jordan. Methods: A retrospective cohort study was conducted using approximately 600,000 individuals from the national Hakeem EHR system (2018–2022). Demographic, clinical, blood pressure, laboratory, and medication data were integrated to construct three datasets reflecting varying levels of feature completeness. Multiple machine learning models were benchmarked, followed by optimization, hybrid modeling, and probability calibration. Model interpretability was assessed using SHAP analysis. Results: The national cohort demonstrated a high cardiometabolic burden, with prevalence of hypertension (50.2%), hyperlipidemia (54.9%), and diabetes (47.9%). Antihypertensive and lipid-lowering therapies were more frequently used among CVD patients (56.9% and 49.6%, respectively). Treatment patterns were dominated by amlodipine (19.9%) and atorvastatin (74.4%). The final calibrated seed-bagged gradient boosting model achieved robust performance (ROC-AUC 0.844; PR-AUC 0.813) with consistent generalization across datasets. Key predictors included antihyperlipidemic therapy, systolic blood pressure variability, age, and sex. Conclusions: This study presents JoRisk, a calibrated and interpretable machine learning framework that integrates pharmacotherapy and clinical data for short-term cardiovascular risk prediction. The model demonstrates strong performance using routinely available EHR variables and offers a scalable decision-support tool for risk stratification in resource-constrained healthcare systems. Full article

This study assessed community process and empowerment in a Primary Healthcare Island Unit in the Azores to support the implementation of Epidemiological Surveillance of Nursing Diagnoses (ESND), focusing on three priority areas: tobacco use, drug dependence, and adolescent decision-making related to sexuality and life planning. Strengthening the visibility of nursing-sensitive phenomena requires integrating nursing diagnoses into epidemiological surveillance systems. A multimethod descriptive study was conducted between September and November 2025, combining document analysis, a community empowerment assessment, and a structured questionnaire. The total population included 328 nurses, with 172 participants (response rate: 52.4%) using a non-probabilistic sampling approach. Data were analyzed using descriptive statistics (frequencies, percentages, means, and standard deviations). Priority nursing foci were identified according to the ICNP^® 2019 classification: tobacco use, drug dependence, and decision-making process related to sexuality and life planning. Results showed that all three dimensions of the MAIEC were weak: community leadership was limited, particularly in knowledge indicators; participation was constrained by unclear organizational structures and insufficient communication; and coping capacity was insufficient due to limited training and experience. Empowerment assessment confirmed structural weaknesses in leadership, organizational support, and resource mobilization. Overall, the community process and empowerment profile indicate that the conditions required to sustain ESND are not yet sufficiently developed. Strengthening leadership, improving communication, and expanding training in ESND and ICNP^® documentation are essential to support nurse-centered surveillance and enhance the visibility of nursing contributions to population health. Full article

As healthcare data grows in volume and complexity, the use of association rule mining (ARM) and frequent itemset mining (FISM) in disease analysis holds great potential for data-driven decision-making, personalized treatment strategies, and disease prevention. This study introduces an extensible, interactive, self-developed visualization tool designed specifically for ARM and FISM, enabling the intuitive exploration of medical datasets. The tool incorporates an innovative preprocessing method that binarizes datasets from various scaling systems using a systematic multi-threshold evaluation, ensuring standardized analysis across diverse data sources. Its interactive design empowers users to dynamically explore relevant patterns individually, enhancing both the interpretability and usability of customized results. In addition, the tool integrates exploratory statistical assessments to support the interpretation and comparison of resulting association rules (ARs) and frequent itemsets (FISs). In this paper, we evaluate the tool using two pilot datasets: one on symptoms for long COVID and one on incorporating rare diseases (RDs) while also providing sample datasets for user testing. Full article

Background: Lung cancer causes more deaths than any other malignancy worldwide, accounting for 2.2 million new cases and 1.8 million deaths in 2020. Extracting structured clinical knowledge from unstructured French-language oncology records remains methodologically unresolved in Tunisian and Francophone healthcare systems, where validated natural language processing tools do not yet exist. This study examined the effectiveness of transformer-based named-entity recognition for automated clinical annotation of Tunisian lung cancer reports. Aim: The study aimed to (i) establish performance baselines for four transformer-based models on a publicly available thoracic radiology dataset, (ii) evaluate five models, including a French biomedical specialist, on a newly constructed Tunisian clinical corpus, and (iii) demonstrate prototype deployment feasibility for structured clinical decision support. Methods: An initial comparative study evaluated BERT, RoBERTa, BioClinicalBERT, and CamemBERT using the official RadGraph dataset partitions, which natively comprise a total of 600 annotated thoracic radiology reports distributed across a standardized 80/10/10 split. Subsequently, five models were evaluated on 200 manually annotated diagnostic reports from Mami Pneumo-Phthisiology Hospital, Tunis. For the Tunisian corpus, a five-fold cross-validation approach was implemented to ensure robust performance estimation, followed by final evaluation on a dedicated hold-out test set. All models were trained for a maximum of 10 epochs, with a learning rate of 5 × 10⁻⁵ and a batch size of 16. Results: Based on the initial comparative study on the RadGraph dataset, where RoBERTa was the top performer and achieved the highest F1-score of 0.873 (precision: 0.869, recall: 0.877), we evaluated its specialized biomedical variant, DR-BERT, on our Tunisian clinical dataset. DR-BERT demonstrated strong generalization on the hold-out test set with an F1-score of 0.824, outperforming the baseline RoBERTa (test F1: 0.791) and showing competitive performance relative to multilingual BERT (0.843 ± 0.005 in five-fold cross-validation). A prototype interface generated structured clinical summaries encompassing prior conditions, imaging modalities, and TNM staging. Conclusion: Language- and domain-adapted transformer models effectively extract structured clinical entities from French-language Tunisian lung cancer reports. DR-BERT’s superior generalization on unseen data confirms that biomedical pretraining in the target language is a key driver of robust performance in specialized French oncology text. This work establishes foundational infrastructure for NLP-driven oncology data management in Tunisia and comparable Francophone settings. Full article

Background: Public healthcare systems face persistent workforce retention challenges that undermine service continuity, organizational resilience, and public value creation. Although leadership is frequently identified as a relevant lever, the literature remains theoretically fragmented and often treats leadership effects as direct and context-free. Methods: This review adopts a PRISMA-guided systematic literature review as a theory-building strategy. Searches were conducted in Web of Science, Scopus, and PubMed using combinations of terms related to leadership, organizational commitment, job satisfaction, turnover intention, and retention in healthcare settings. The review identified 640 records, removed 372 duplicates, screened 268 titles and abstracts, assessed 90 full-text records for eligibility, and retained 30 peer-reviewed studies for configurative synthesis. The analysis combined thematic synthesis with configurative mapping to identify mechanisms, recurring patterns, and contextual contingencies. Results: The review shows three consistent patterns. First, leadership is linked to retention predominantly through organizational commitment, especially affective and normative commitment, rather than through direct effects. Second, institutional and organizational conditions—particularly red tape and working conditions—shape the strength of leadership–commitment relationships. Third, workforce heterogeneity, including generational differences, affects how leadership practices and organizational environments are interpreted, although these dynamics are rarely theorized explicitly in the literature. Conclusions: The article develops a governance-sensitive integrative framework in which people-centered leadership influences turnover intentions indirectly through organizational commitment, while red tape and working conditions operate as contextual moderators. By embedding leadership within Public Administration and governance theory, the review clarifies the literature’s main explanatory gap and provides a foundation for comparative empirical testing and for more sustainable workforce strategies in public healthcare systems. Full article

Background: Sedentary behavior is a major modifiable risk factor for chronic metabolic disorders, particularly type 2 diabetes mellitus (T2DM). Despite recommendations promoting regular physical activity (PA), adherence remains low. DIA/01 is a multidisciplinary study designed to promote healthy lifestyles for the prevention and management of T2DM, supporting healthcare systems. Methods: A total of 123 adults with T2DM diagnosed will be enrolled at the Diabetes Center of the University Hospital of Perugia throughout 2025. Inclusion criteria are age 25–80 years, ability to walk independently, being inactive, and BMI 18.5–40 kg/m². Exclusion criteria include severe cardiovascular, central nervous system, or musculoskeletal diseases contraindicating PA. Participants will be randomized into three groups: (1) standard care (SC); (2) SC plus theoretical PA counseling (TCPA); and (3) SC plus TCPA plus a 3-month supervised mixed exercise program. The assessment, conducted at baseline and at 6 and 12 months, includes total weekly PA (WPA) time, using IPAQ-SF and actigraphy. Moreover, glycated hemoglobin, sedentary time (ST), functional capacity, body composition, cardiometabolic risk factors, dietary adherence, perceived barriers and willingness to initiate PA, readiness to change, health-related quality of life, and sleep quality will be studied. This study is registered in the Clinical Trials Registry on 13 May 2026, with the identifier NCT07583355. Conclusions: Participants in groups (2) and (3) are expected to show greater improvements in WPA, reductions in ST, and favorable changes in metabolic and functional outcomes compared with SC. This approach may support long-term engagement in regular PA and contribute to improving the clinical management of T2DM. Full article

Background: Advances in monitoring systems featuring wearable sensors, computer vision, and artificial intelligence (AI) have been increasingly used in sports science and rehabilitation practices as a means of movement pattern analysis, injury prevention, and training optimization. These technologies are becoming essential components of athlete-performance analysis and rehabilitation-monitoring systems designed to support biomechanical assessment, athlete development, and movement-quality evaluation. Athlete-performance analysis and rehabilitation monitoring increasingly rely on intelligent multimodal sensing systems capable of continuously evaluating movement quality, biomechanical patterns, training execution, and recovery progress. Human activity recognition (HAR) serves as a key enabling technology for these applications by providing automated assessment of human movement using wearable and vision-based sensing modalities. Therefore, the purpose of this study was to develop and evaluate an attention-based multimodal framework that integrates wearable inertial sensing and RGB video analysis for robust athlete-performance assessment and rehabilitation monitoring through accurate recognition of human movement patterns. Methods: Athlete-performance analysis and rehabilitation monitoring combining inertial sensor data and RGB-based visual information was introduced. Inertial signals were segmented with adaptive windowing, whereas silhouette refinement was performed to analyze motion structures from visual inputs in support of athlete-performance analysis and rehabilitation monitoring. Temporal, spatial, and motion features such as trajectory, orientation, and skeleton-based space-time representations were calculated from multimodal inputs. The proposed framework was designed to capture complex movement dynamics associated with rehabilitation exercises and sports-related motion patterns across heterogeneous sensing environments. Extracted features were then combined and optimized with a multimodal feature fusion approach, while the Ranger optimization algorithm was utilized during the process. An attention-based deep learning classifier was implemented to classify movement activities. Results: The results showed that the proposed framework reached accuracy scores of 88.40% and 87.96% on the VIDIMU dataset and the UTD-MHAD dataset respectively. Recognition performance across both inertial and vision-based modalities provided greater robustness than single-modality solutions. The integration of wearable sensing and computer vision modalities further improved the ability of the framework to analyze complex movement behaviors under varying execution conditions and environmental variations. Conclusion: The proposed multimodal framework provides a foundation for intelligent athlete-performance and rehabilitation-monitoring systems by integrating wearable sensing, computer vision, and attention-based artificial intelligence for robust movement analysis. The findings highlight its potential to support biomechanical assessment, movement-quality evaluation, training-performance monitoring, rehabilitation tracking, and injury-risk management in modern sports and healthcare environments. Full article

Life cycle assessment is increasingly applied in healthcare, yet the healthcare-specific standardisation landscape and its relation to current practice remain unclear. This study maps existing frameworks and analyses their alignment with published healthcare LCA to identify standardisation gaps. Healthcare-specific standards and product category rules were identified through grey literature searches. Published healthcare LCA studies were quantitatively analysed and compared with the identified frameworks to assess methodological convergence and divergence. Six healthcare-specific frameworks were identified: five address medical products, one addresses services, and none cover organisational assessment. Product-level applications showed strong alignment in structural modelling elements including system boundaries and life cycle stages, while substantial heterogeneity persisted in functional unit definitions and impact assessment approaches. Service and organisational assessments showed broader variability in modelling approaches, functional units, and system boundary conceptualisations, indicating distinct modelling logics of healthcare delivery across assessment levels. Healthcare LCA practice is consistent with ISO-based principles but lacks a shared conceptual modelling logic for healthcare delivery systems. Rather than reflecting a single methodological paradigm, healthcare LCA combines product-, intervention-, pathway-, and organisational-oriented approaches. Standardisation efforts should therefore focus not only on harmonising calculation methods but also on developing healthcare-specific modelling conventions for products, services, and organisational structures. Full article

Artificial intelligence (AI) is increasingly applied to healthcare decision-making; however, many persistent patient safety risks arise from sociotechnical conditions such as communication breakdowns, coordination failures, and organisational culture rather than diagnostic or decision error alone. While simulation can engage these dimensions of care, AI-supported simulation remains limited by heterogeneity and a lack of explicit conceptual structure. This study presents a narrative and conceptual review of the healthcare simulation and AI literature to identify structural barriers to coherent AI reasoning about simulation. Drawing on this synthesis, we introduce Transformative Simulation (TfS) as an intentional framework that can be formalised as an ontology for AI-supported simulation focused on cultural and systems-level change. TfS structures simulation through explicit Simulation-Based Intentions, an aligned design–delivery–data–debrief process, and foundational considerations of purpose, perspective, power, preparation, and possibility. Framed in this way, TfS enables AI systems to interpret simulation artefacts in relation to declared intent, sociotechnical context, and ethical boundaries. We further describe an Intentionality–Simulation–Intelligence triad and a continuous learning loop that align human values, simulation structure, and AI reasoning. The findings of this review suggest that an important challenge in applying AI to healthcare simulation may be ontological as well as technical, and that explicit representation of intention and context is necessary to support coherent, context-sensitive, and system-aligned AI reasoning in healthcare. Full article

Severe Combined Immunodeficiency (SCID), Spinal Muscular Atrophy (SMA), and X-Linked Agammaglobulinemia (XLA) are rare but life-threatening genetic disorders in infants that can lead to severe infections, progressive neuromuscular degeneration, or severe immune dysfunction associated with significant morbidity and mortality if not diagnosed early. Advances in newborn screening (NBS) technologies have enabled pre-symptomatic detection of these conditions, allowing early initiation of life-saving interventions such as hematopoietic stem cell transplantation, gene therapy, and immunoglobulin replacement therapy. However, the absence of a standardized national clinical pathway linking screening, confirmatory testing, and specialist referral in Malaysia continues to contribute to delayed diagnosis and suboptimal patient outcomes. This review examines and synthesizes current evidence on the clinical pathways for early diagnosis and management of SCID, SMA, and XLA, with particular emphasis on diagnostic workflows, screening technologies, and healthcare system challenges within the Malaysian context. The review examines disease epidemiology, consequences of delayed diagnosis, and the role of expanded NBS under the Screening for Health, Intervention, Nurturing of Every Child (SHINE) program in improving early diagnosis and management. In addition, the paper outlines the current NBS landscape, the use of multiplex real-time polymerase chain reaction (PCR) assays for simultaneous detection of T-cell receptor excision circles (TREC), kappa-deleting recombination excision circles (KREC), and survival motor neuron 1 (SMN1) gene deletion of exon 7 from dried blood spot (DBS) samples. A structured diagnostic framework incorporating screening interpretation, confirmatory testing, and urgency-based referral pathways is also proposed. By addressing current operational barriers and coordinating laboratory referral systems, expanding NBS programs could significantly improve early diagnosis and long-term outcomes for infants affected by SCID, SMA, and XLA in Malaysia. Full article