Risk Management and Healthcare Policy最新文献

Purpose: To address the problems in medical electrical equipment risk management caused by the disconnection between unstructured medical electrical equipment standard documents and adverse event data, the lack of high-quality annotated data, and the reliance on manual combing for risk analysis.

Methods: This paper proposes a novel method for constructing a risk knowledge graph that integrates large language models and prompting engineering standards. Using adverse event data from early childhood incubators as a case study, it integrates multi-source standards to construct a three-layer risk knowledge system. It designs multi-angle prompting strategies involving entity relationships and employs a dual strategy of entity disambiguation and aggregation to achieve knowledge integration and standardization.

Results: The thought chain reasoning suggestion has the best performance (mean F1 score of 0.871). The constructed knowledge graph contains 24,106 nodes and 18,053 relationships, achieving a complete "fault-standard-measure" link. Based on this, a question-answering system for intelligent risk retrieval was developed.

Conclusion: This provides a low-cost, reusable knowledge graph construction path for the resource-constrained medical device field, promoting the transformation of risk management towards AI empowerment and assisting in intelligent supervision of adverse events related to medical devices.

Background: Needle stick injuries (NSIs) are among the most frequent and preventable risks to healthcare workers. Behavioral models like the Health Belief Model (HBM) offer valuable insight into preventive behaviors, including those related to NSI. However, no validated tools currently exist to assess NSI-related beliefs using the HBM.

Objective: To develop and validate the NSI-Prevention Beliefs Scale (NSI-PBS), a tool grounded in the Health Belief Model (HBM), to examine nurses' perception about NSI risk and prevention.

Methods: The study followed a structured scale development process, including literature review, expert validation, and a cross-sectional survey of 545 nurses was directed in July 2025. Psychometric evaluation involved exploratory and confirmatory factor analysis (EFA/CFA), as well as internal consistency testing, and measurement error assessment.

Results: EFA identified a six-factor solution aligned with HBM domains: "Susceptibility, Severity, Benefits, Barriers, Cues to Action, and Self-Efficacy". CFA confirmed good model fit (CFI =0.926, TLI =0.914, RMSEA =0.063, SRMR =0.058). All subscales demonstrated strong reliability (α =0.81-0.93; ω =0.82-0.93). The final version of this scale produced 25 items and showed robust psychometric properties, theoretical coherence, clear factorial structure, and low measurement error.

Conclusion: This study contributes to literature by adding a new psychometrically sound and theory-based instrument for assessing NSI prevention beliefs among nurses. The NSI-PBS can be used to measure NSI-Prevention Beliefs in clinical settings and in designing training, risk profiling, and safety interventions for nursing staff and other healthcare workers.

Background: Diabetic peripheral neuropathy (DPN) is highly prevalent among elderly patients with type 2 diabetes; however, existing models exhibit suboptimal performance and lack specificity. This study aims to develop and validate a machine learning-based model for early identification of DPN risk.

Methods: We retrospectively collected the data of 1450 elderly patients with type 2 diabetes using the electronic medical record system of the National Metabolic Management Center (MMC) at a tertiary hospital in Shanghai's Pudong New Area from March 2022 to March 2025. The dataset included general information, disease-related indicators, and laboratory results. We randomly divided the dataset into training and testing sets in a 7:3 ratio. After feature preprocessing and selection, four machine learning algorithms-logistic regression, naïve Bayes, random forest, and extreme gradient boosting (XGBoost)-were used to construct prediction models. Hyperparameter tuning was executed through grid search combined with 5-fold cross-validation, and model performance was evaluated using the Area Under the Receiver Operating Characteristic Curve (AUC), accuracy, precision, recall, F1-score, calibration curves, and Decision Curve Analysis (DCA). The SHapley Additive exPlanations (SHAP) analysis was applied for model interpretation.

Results: The prevalence of DPN was 42.9% (623/1450). Nine variables were identified as independent predictors: diabetes duration, HbA1c, sleep quality, Charlson Comorbidity Index, sugar-sweetened beverage intake, peripheral arterial disease, sedentary behavior, smoking, and hypertension. Among the models, XGBoost performed best with an AUC of 0.951, accuracy of 0.878, precision of 0.876, recall of 0.834, F1-score of 0.855, and Brier score of 0.087. SHAP analysis confirmed the dominant contribution of diabetes duration and HbA1c to model predictions.

Conclusion: The XGBoost-based risk prediction model exhibited robust predictive performance and clinical utility for DPN in elderly patients with type 2 diabetes, offering potential for early identification of high-risk individuals and guiding targeted clinical interventions.

Objective: This study aims to evaluate the implementation and outcomes of an internet + workshop model for standardized training in arteriovenous fistula (AVF) puncture among nurses.

Methods: A self-controlled design was employed and 81 hemodialysis nurses were selected from the Jilin Province. The training program was developed based on the 2023 publication "Best Evidence Summary for the Management of Vascular Access Puncture in Maintenance Hemodialysis Patients" incorporating the internet + workshop model. Participants underwent both theoretical and practical assessments before and after the training intervention. The Chinese version of the Jeffries Simulation Design Scale was utilized to assess training outcomes.

Results: Post-training, the nurses were evaluated using the Chinese version of the Jeffries Simulation Design Scale. The agreement rates ranged from 93.7% to 100% across five assessed dimensions. Comparative analysis demonstrated statistically significant improvements in both theoretical and practical assessment scores following the training (theoretical: t = -17.83, p < 0.01; practical: t = 116.08, p < 0.05).

Conclusion: The internet + workshop model constitutes an effective method for delivering standardized AVF puncture training to nursing staff, enhancing both theoretical knowledge and practical skills.

Respiratory infectious diseases (RIDs) remain a persistent public health challenge in Indonesia, a vast archipelago with complex healthcare delivery and marked regional inequities. The COVID-19 pandemic revealed critical weaknesses in the country's surveillance systems, diagnostic capacity, and outbreak response, underscoring the urgent need for stronger pandemic preparedness and a transition from a reactive, crisis-driven model to a proactive, prevention-focused health system. This study synthesizes existing literature, policy documents, and recent evaluation reports to assess Indonesia's health system performance in RID management and to identify evidence-based priorities for reform. The analysis is structured around five health system components: (1) surveillance and early warning, (2) diagnostic and laboratory capacity, (3) healthcare workforce, (4) public health infrastructure and primary care, and (5) governance and financing. Indonesia demonstrates important strengths, including a nationwide network of more than 10,000 Primary Health Centers (Puskesmas) and proven vaccination campaign capacity. However, significant gaps persist: during COVID-19, Early Warning Alert and Response System (EWARS) reporting completeness dropped from 75% to 53%, rural and remote areas remain underserved by diagnostics, and health workforce distribution continues to be inequitable Priority reforms include scaling up point-of-care diagnostics across all Puskesmas, integrating fragmented surveillance platforms through SatuSehat, empowering community health workers with digital tools, and ensuring sustainable financing for preparedness. Medium-term strategies focus on workforce redistribution and the establishment of regional health security centers, while long-term priorities emphasize predictive health intelligence, resilient supply chains, and nationwide facility upgrades. Building a resilient, prevention-oriented system will require sustained political commitment, innovative financing, and cross-sectoral collaboration, positioning Indonesia not only to strengthen domestic health security but also to serve as a regional leader in epidemic preparedness.

Purpose: This study aims to identify the key factors influencing hospital selection among urological patients in China. Despite policy efforts to promote tiered care through the Hierarchical Diagnosis and Treatment (HDT) system, many patients continue to bypass primary care. Understanding the behavioral and psychological drivers of this pattern is essential for improving patient guidance and optimizing healthcare resource allocation.

Patients and methods: Data were collected from a stratified random sample of 676 patients between June 10 and October 10, 2024 from tertiary, secondary, and community healthcare institutions in Jiangsu Province. We collected detailed information on demographic characteristics, clinical profiles, psychological states, health behaviors, and economic factors. Multinomial logistic regression was employed to identify independent predictors of hospital choice, while Sankey diagrams and multiple correspondence analysis (MCA) were used to visualize patient-level decision pathways and structural patterns.

Results: A substantial proportion of patients sought care at tertiary hospitals for conditions that could be effectively managed at lower levels, reflecting a disconnect between actual patient behavior and the goals of the HDT system. Hospital choice was shaped by clinical needs, emotional responses, and perceived institutional trust. Patients choosing tertiary hospitals were more likely to have undergone surgery, experienced severe pain, and reported negative emotional reactions. Secondary hospital users commonly had benign prostatic hyperplasia, moderate symptom burden, rural residence, and engaged in regular physical activity. Community healthcare facilities users typically presented with mild symptoms, shorter illness duration, mild anxiety, lower financial burden, and closer geographic proximity. Across all tiers, anxiety levels and trust perceptions emerged as key behavioral drivers contributing to the bypassing of primary care.

Conclusion: Despite policy efforts to promote primary care, many patients with manageable conditions continue to bypass lower-tier facilities. Building trust and providing triage support in primary care are essential for achieving the goals of hierarchical healthcare.

Background: Patients with coronary artery disease (CAD) and type 2 diabetes mellitus (T2DM) are at markedly increased risk of developing heart failure (HF), yet early identification of high-risk individuals remains challenging. The remnant cholesterol inflammatory index (RCII) has been proposed as a predictor of adverse cardiovascular outcomes, but its role in patients with CAD and T2DM has not been fully elucidated.

Methods: We retrospectively analyzed clinical data from patients treated at our center. Demographic characteristics, comorbidities, medication use, and laboratory parameters were collected. Key features were selected using the Boruta algorithm, and five machine learning models-logistic regression (Logistic), decision tree (DT), elastic net regression (ENet), LASSO regression, and naïve Bayes (NB)-were constructed. Discrimination was assessed by receiver operating characteristic (ROC) curves and area under the curve (AUC), calibration by calibration plots and Brier scores, and interpretability by SHAP analysis.

Results: Among 1181 enrolled patients, 73 developed HF. Median RCII levels were significantly higher in the HF group. Boruta feature selection identified 13 key predictors for model development. Logistic regression demonstrated the best performance, achieving AUCs of 0.88 in the training set and 0.85 in the testing set, with overall accuracy of 0.87 and F1-score of 0.79 in the testing cohort. SHAP analysis revealed that elevated RCII, poor nutritional status, and smoking were major contributors to HF occurrence, with RCII showing a positive association with HF risk.

Conclusion: RCII is a valuable predictor of HF in patients with CAD and T2DM. Higher RCII levels are closely linked to an increased risk of HF.

Background: Heart failure is characterized by high rates of hospitalization, substantial medical expenses and increased mortality, posing a serious threat to human health. Malnutrition is a common complication among patients with heart failure and is associated with increased risks of infection, rehospitalization and mortality, resulting in a considerable disease burden and financial strain on both patients and their families. Identifying patient-related influencing factors is the primary prerequisite for recognizing the risk of malnutrition.

Objective: This scoping review aims to systematically summarize the factors influencing malnutrition in patients with heart failure, based on evidence from both domestic and international studies.

Methods: Studies on malnutrition in heart failure were retrieved from PubMed, Web of Science, Cochrane Library, CINAHL, CNKI and Wanfang, from database inception to 25 May 2025. Two researchers independently screened titles and abstracts and extracted data according to predefined criteria. Findings were categorized and reported descriptively.

Results: A total of 30 studies were included. The reported prevalence of malnutrition in heart failure varied by assessment tool. Based on the biopsychosocial model, nine categories of influencing factors were identified: demographic characteristics, disease characteristics, clinical physiological indicators, pharmacological treatment, emotional status, cognitive function, behavior, support system and living environment.

Conclusion: This scoping review provides the first comprehensive summary of malnutrition-related factors in patients with heart failure through the biopsychosocial model. The findings indicate that malnutrition in patients with heart failure is affected by both subjective and objective factors and is closely associated with disease progression. A comprehensive understanding of the influencing factors of malnutrition contributes to the development of more precise nutritional risk assessment tools and lays the foundation for a continuous management pathway of screening, assessment, intervention, and monitoring to facilitate early risk identification.

Background: Surgical safety is a global health priority, yet its consistent application in low- and middle-income countries remains a challenge due to systemic, cultural, and resource-related barriers. The World Health Organization's Surgical Safety Checklist (SSC) has been shown to reduce perioperative complications, but evidence from fragile health systems such as Somalia remains scarce.

Methods: This prospective closed-loop clinical audit was conducted at Dr. Sumait Hospital, a tertiary referral and teaching facility in Mogadishu, Somalia. A total of 160 surgical procedures were observed across two audit cycles. The first cycle established baseline compliance, while the second followed a structured intervention comprising targeted staff education sessions, strengthened leadership involvement, and placement of visual reminders and wall posters in operating theatres. Checklist adherence was assessed across the sign in, time out, and sign out phases using a standardized 25-item observation tool. Data were analyzed using the Wilcoxon signed-rank test, with significance set at p < 0.05.

Results: Overall checklist compliance increased significantly from 51.38% in the first cycle to 93.01% in the second (p < 0.001). Improvements were observed across all three SSC phases: sign in compliance rose from 54.62% to 88.19%, time out compliance from 50.60% to 96.94%, and sign out compliance from 47.29% to 95.01%. The most substantial gains were linked to improved team communication during the time out phase. However, checklist items requiring anticipatory planning, such as risk assessment for major blood loss, showed relatively lower improvements.

Conclusion: Context-sensitive, low-cost interventions-including focused education, leadership reinforcement, and visual prompts-can markedly improve adherence to the WHO Surgical Safety Checklist in resource-limited settings. These findings underscore the SSC's potential to strengthen surgical safety culture in Somalia and offer a practical model for similar fragile health systems aiming to reduce preventable perioperative harm.

Purpose: Globally, acute pediatric intoxication is a serious health concern with a significant burden. Differentiation between pharmaceutical and non-pharmaceutical poisoning is crucial for promoting early diagnosis and implementing effective preventive strategies.

Patients and methods: This three-year retrospective cohort study investigated 1328 exposed children, aiming to develop risk prediction nomograms to identify patients in need of pediatric intensive care unit (PICU) admission and those at risk of mortality.

Results: With a mean age of 8.21±6.64 years, a mortality rate of 1.7% and a PICU admission rate of 1.3%, more than 99% of infants and preschool children were exposed unintentionally, and intentional exposure was observed in about 88% of adolescents (p<0.001). Aluminum phosphide (AlP) was a leading cause of mortality and PICU admission. Non-pharmaceutical poisoning was associated with more severe clinical presentations and was exclusively linked to mortality. A predictive model for mortality with an overall accuracy of 99% underscores the role of receiving prehospital treatment in increasing the likelihood of mortality. Exposure to AlP contributed to PICU admission with a notably high odds ratio (50.596). Significant predictors of PICU need were rapid admission and leucocytosis. A model predicting PICU admissions, with a Nagelkerke pseudo-R² of 0.710, encompassed mutual factors contributing to mortality and PICU need, including age, sex, and blood pressure.

Conclusion: The obtained findings highlight critical differences in poisoning characteristics and outcomes across pediatric age groups and exposure types, emphasizing a need to implement preventive strategies through proper family education, increased social awareness, and the provision of psychological support for at-risk individuals.