Hospital discharge is a vulnerable transition for older adults who often leave with limited understanding of their health and care instructions. This study evaluated the implementation and outcomes the Patient-Oriented Discharge Summary (PODS), a one-page co-designed tool to support hospital-to-home transitions. Using a hybrid type II design, we combined a quasi-experimental pre-post study with an implementation evaluation in a Swiss acute care unit. Patients aged ≥50 years discharged home were allocated to control (n = 55) or intervention (PODS; n = 56). The primary outcome was perceived quality of care transition measured using the Care Transition Measure (CTM-15). Implementation outcomes were assessed through surveys, focus groups and interviews with healthcare professionals. PODS participants reported higher CTM-15 scores than controls (74.4 vs. 62.3, p < 0.001). Implementation findings showed that the PODS structured discharge teaching and supported dialogue but its blank, collaboratively completed format led to variable completeness and limited usefulness at home. Persistent barriers included workload, workflow integration, and uneven interprofessional engagement. PODS improved perceived quality of care transition, primarily through the relational and educational processes it structures rather than the written document alone. While valuable, PODS alone appears insufficient; combining structured tools with contextual and organizational supports may enhance effectiveness.Clinical Trial Registration: clinicaltrials.gov, identifier (NCT06123546).

IntroductionEarly identification of adolescents at risk for substance use is critical for timely intervention. However, standard screening tools like CRAFFT (Car, Relax, Alone, Forget, Family and Friends, Trouble) and S2BI (Screening to Brief Intervention) face significant barriers, including adolescent disclosure reluctance, limited clinic privacy, and administrative challenges with paper-based forms. Digital games offer a promising alternative by generating behavioral data that may serve as digital biomarkers of substance use risk through engaging, low-burden gameplay. The objective of this proof-of-concept study was to explore the utility of game log data collected during gameplay to predict substance use.MethodsWe analyzed game log data from 160 adolescents aged 11–14 years who played an HIV prevention game targeting high-risk behaviors including substance use, drawn from a larger randomized controlled trial (N = 333) conducted in schools. We extracted 240 gameplay-derived behavioral metrics reflecting executive function, decision-making, and inhibitory control—cognitive domains affected by substance use. Machine learning models (Support Vector Machine, Logistic Regression, Gradient Boosting, Neural Networks, Decision Tree, Random Forest) were trained to predict lifetime substance use and drug-refusal self-efficacy. A validation study with 36 newly recruited high school students tested associations with standardized measures of emotion regulation, impulsivity, and cognitive performance. Model performance was assessed using discrimination (e.g., AUC) and clinical utility (e.g., sensitivity) metrics.ResultsPredictive accuracy across all models was insufficient for clinical translation. For drug use prediction, AUC (SD) ranged from 0.458 (0.11) to 0.593 (0.07) (mean AUC <0.6 across all models), sensitivity ranged from 0 to 0.265 (0.18), and F-1 scores ranged from 0.398 (0.0) to 0.482 (0.08). For drug-refusal self-efficacy prediction, AUC ranged from 0.425 (0.10) to 0.592 (0.09), sensitivity ranged from 0.386 (0.14) to 0.785 (0.19), and F-1 scores ranged from 0.431 (0.06) to 0.598 (0.07). No model achieved the minimum threshold (AUC ≥0.7) suggested for clinical utility.DiscussionOur study elucidates the challenges associated with extracting behavioral markers from naturalistic digital environments. We discuss the potential of game-based digital biomarkers as scalable, low-burden tools for screening and monitoring, and the limitations of our study that can inform future studies seeking to understand the feasibility of using in-game data as digital biomarkers of substance use risk in adolescents.

King Saud Medical City (KSMC), Riyadh's largest public medical city, has undergone a profound spatial and institutional transformation between 2000 and 2025. Using multi-temporal remote sensing and geographic information system analysis, this study provides quantitative evidence of medical city densification in Saudi Arabia. The built-up footprint expanded by more than 19 hectares, and the Normalized Difference Built-up Index increased by +0.36, reflecting a shift from a low-rise, dispersed campus to a vertically integrated medical complex. This physical transformation coincided with major institutional growth: total bed capacity increased from approximately 450 to over 1,500, including 200 intensive care beds; the emergency department expanded to 102 beds, becoming one of the largest in the Kingdom; and annual surgical volume exceeded 20,000 major procedures. These changes occurred alongside rapid population growth in Riyadh and national health system reforms such as the Medical Cities Law, the Health Cluster Model, and Vision 2030. The findings demonstrate how spatial densification enabled operational expansion and accelerated the adoption of advanced medical infrastructure. The study offers a scalable spatial framework for guiding the future development of large medical facilities in rapidly urbanizing settings.

BackgroundPatient safety culture (PSC) is a critical component of healthcare quality, particularly in psychiatric settings where unique risks exist. In China, research on PSC within mental health institutions remains underdeveloped, and the factors influencing it are poorly understood.MethodsThis cross-sectional study utilized the Chinese version of the Hospital Survey on Patient Safety Culture (HSOPSC) to assess PSC among 2,524 mental health nurses from multiple Level-2 and Level-3 psychiatric hospitals in China (2021–2023). We specifically examined the impact of hospital level on PSC perceptions. Multivariate regression models were employed to identify predictors of PSC composites.ResultsThe overall positive response rate (PPR) across the 12 PSC dimensions was 62.4%, indicating a moderate culture. Strengths were “Teamwork Within Units” (PPR = 83.3%) and “Organizational Learning” (PPR = 82.8%), while critical areas for improvement were “Nonpunitive Response to Error” (PPR = 46.6%) and “Staffing” (PPR = 43.3%). Regression analyses revealed that hospital level was a significant predictor of PSC outcomes, alongside years of experience and daily overtime hours (P < 0.05).ConclusionsThis pioneering national study reveals that the PSC landscape in Chinese psychiatric hospitals is characterized by specific strengths but also critical weaknesses, significantly influenced by hospital level. The findings compel a move away from one-size-fits-all approaches. We recommend stratified interventions: foundational support for Level-2 hospitals and advanced quality initiatives for Level-3 hospitals, with universal prioritization of addressing staffing shortages and fostering a nonpunitive “Just Culture”.

The nursing ability for medical narrative is essential for patient-focused practice within the bio-psycho-social model of medicine. This study sought to identify influential factors affecting nursing competence in medical narrative skills among practicing nurses in healthcare settings to determine the personal, environmental, relational, and organizational elements that impact this essential competency using grounded theory and constructivist theoretical frameworks. A qualitative study design was employed with semi-structured interviews distributed to nurses with expertise in narrative and communication skills. Twenty nurses were selected through purposive sampling to assess: (1) their narrative medicine knowledge and practice; and (2) factors influencing their narrative competency for those with recognized communication expertise in nursing practice. The study used thematic content analysis for data interpretation. Both individual and contextual data were collected. Twenty nurses participated in this study. Four major categories of influencing factors emerged: personal factors including lack of narrative medicine knowledge, inadequate communication skills, non-standard behaviors, work dissatisfaction, low self-learning capability, and insufficient technical skills; environmental factors including family life situations, relief benefits, working atmosphere, and hospital humanistic culture; relational factors including patient and family interactions; and organizational factors including problematic training models, low emphasis on narrative skills, and ineffective medical processes. These factors demonstrated complex interrelationships affecting overall narrative competency in nursing practice. The present findings demonstrate that nursing competence in medical narrative is influenced by complex, interlocking factors at multiple levels. Such evidence can be used to support nursing policy and practice improvement through comprehensive strategies including narrative medicine education, incentive-based compensation systems, adequate staffing and supportive environments, primary nursing care models, and integrated organizational approaches for humanistic nursing practice quality.

Romania's social health insurance system is based on the Bismarck model and aims to provide universal healthcare access through compulsory contributions. While financing and institutional design have been widely studied, the ethical responsibilities of physicians, particularly social insurance physicians, remain under-explored. This study analyzes Romania's social health insurance system through an integrated institutional and ethical lens, focusing on social insurance physicians who operate at the interface of clinical assessment and social protection eligibility. A structured narrative review was conducted, synthesizing national legislation, international ethical frameworks (Declaration of Geneva, International Code of Medical Ethics), peer-reviewed literature (2010-2025), and comparative analyses with European health insurance models. The analysis identified systemic challenges, including underfunding, workforce migration, and demographic pressures, and examined their impact on professional ethics. Romania ensures broad formal coverage, yet persistent challenges-underfunding, regional disparities, informal payments, and physician shortages-create ethical tensions for social insurance physicians. Comparative analysis shows that other European countries (Germany, France, Netherlands) mitigate such pressures through stronger institutional safeguards, higher funding, and protected professional autonomy. This study contributes to health services research by conceptualizing ethical vulnerability in resource-constrained insurance systems and linking institutional structures to professional autonomy, fairness, and system resilience. Findings highlight the need to strengthen ethical frameworks, safeguard physician independence, and align institutional reforms with ethical governance to ensure transparent, equitable, and resilient social health insurance systems.

BackgroundType 2 diabetes mellitus (T2DM) has become a public health crisis in China, particularly in rural areas, leading to significant impairment of quality of life and premature death. However, little is known about the level of knowledge, attitude, and practice (KAP) required for diabetic patients for effective management and prevention of complications. This study aimed to assess the KAP level of patients with T2DM and identify the associated factors in Central China.MethodsA cross-sectional study was conducted using self-administered KAP questionnaires among patients with T2DM recruited from 12 township health centers. KAP levels were determined by calculating the scores, and the association between respondents' characteristics and KAP outcomes was evaluated using chi-square tests and Mann–Whitney U-tests, as appropriate.ResultsIn total, 259 diabetic patients completed the survey. Overall, despite adequate knowledge and positive attitudes toward T2DM, there were substantial gaps in practices. The respondents scored 13.09 out of 17points on the knowledge subscales, 3.88 out of 4 on the attitude subscales, and 4.96 out of 12 on the practice subscales. Educational attainment, health insurance type, diabetic complications, and current medical treatment pattern were significant predictors of knowledge. Educational attainment, health insurance type, diabetic complications, and comorbidities were significant predictors of attitudes. Educational attainment and marital status affected respondents' practices significantly.ConclusionsDespite adequate knowledge and positive attitudes toward T2DM, there were substantial gaps in diabetic practices. These findings highlight the urgent need for action from relevant health authorities and policymakers to improve diabetic practices among Chinese patients with T2DM in rural China.

Burnout among nurses remains a pervasive workforce issue in the United States, with limited data on allergy nursing. This study aimed to estimate the prevalence of burnout among U.S. allergy nurses and identify factors associated with burnout. A descriptive correlational study was conducted using the Mini Z survey and a demographic questionnaire. Responses were dichotomized into "with burnout" and "without burnout" based on validated thresholds. Descriptive statistics, Spearman's Rho correlations, and binary logistic regression were used to examine associations between burnout and workplace factors. Of 241 responses, 201 met inclusion criteria. Burnout prevalence was 34% (n = 69). Burnout was positively correlated with job stress and work area chaos, and negatively correlated with job satisfaction, workload control, documentation time satisfaction, and professional values alignment. In the final logistic regression model, higher work area chaos (OR = 2.66; 95% CI, 1.19-5.95; P < 0.05) and higher job stress (OR = 4.60; 95% CI, 2.14-9.87; P < 0.05) were associated with increased odds of burnout, while alignment of professional values was protective (OR=0.38; 95% CI, 0.18-0.83; P < 0.05). Burnout in allergy nursing was common and associated with factors such as work environment and nurse-leadership value-alignment. Interventions that reduce practice chaos, increase workload control, streamline documentation, and strengthen alignment between nurses' professional values and leadership may mitigate burnout. These findings can inform targeted organizational strategies, aid in the selection of existing tools, and guide the development of evidence-based interventions to reduce burnout in allergy nursing practice.

There is limited understanding regarding the factors that predict recreational therapy engagement among veterans in long-term care facilities. We aimed to develop and validate machine learning models to predict recreational therapy participation and identify key factors influencing engagement patterns among veterans in long-term care facilities. In this cross-sectional observational study, we used data from 57 veterans aged 18 years and above at the New York State Veterans Home at Oxford. Data were collected through a comprehensive self-administered survey capturing demographic characteristics, participation patterns, and activity preferences. Two binary outcome variables were constructed: high participation and any participation. Five machine learning algorithms (Random Forest, Decision Tree, Gradient Boosting, and Logistic Regression with L1 and L2 regularization) were systematically evaluated using Leave-One-Out Cross-Validation for high participation and 5-fold Stratified Cross-Validation for any participation. Feature selection was implemented using SelectKBest with f_classif scoring, and class imbalance was addressed through balanced weighting techniques. Random Forest emerged as the optimal algorithm for both prediction tasks, achieving F1-scores of 0.860 ± 0.347 for high participation prediction and balanced accuracy of 0.619 ± 0.081 for any participation prediction. Feature importance analysis revealed activity preference diversity (Gini importance: 0.293) and total preferences (0.254) as the primary predictors of high participation, while facility tenure (0.268) was the strongest predictor of any participation. Veterans with preference diversity >4.5 activities combined with satisfaction scores >3.84 achieved 100% observed probability of sustained high participation [n = 5; 95% exact binomial CI: (47.8%, 100%)], though this estimate should be interpreted cautiously given the small subgroup size. New residents (≤1.5 years) with limited preferences demonstrated the highest risk for non-participation. Group activities (Gini importance: 0.143) and spiritual activities (Gini importance: 0.100-0.101) emerged as significant predictors across both models. This research provides the first proof-of-concept demonstration of a machine learning approach for predicting recreational therapy engagement among veterans in long-term care facilities, establishing methodological feasibility and generating testable hypotheses for prospective multi-site validation. Activity preference diversity and facility tenure serve as primary determinants of participation, with a critical 1.5-year adaptation period identified for intervention targeting. These predictive models can be applied during admission or early in residency to identify veterans at risk of low participation, enabling recreational therapy staff to implement tailored, proactive engagement strategies before disengagement occurs.