Risk Management and Healthcare Policy最新文献

Background: This study used data envelopment analysis (DEA), to assess relative efficiency of infection control in different clinical departments of the hospital for performance evaluation purposes.

Methods: All wards and departments from January to December 2022 were selected as decision units, and five input and two output indicators related to infection prevention and control were determined using DEA. Pure technical efficiency was evaluated using the Banker-Charnes-Cooper (BCC) model.

Results: In the study, the input-output indexes of the 27 clinical departments varied significantly. The average values of technical efficiency, pure technical efficiency, scale efficiency, and comprehensive benefit were 0.987, 0.995, 0.992, and 0.980, respectively. Among the 27 departments, 52% exhibited constant returns to scale, 44% showed increasing returns to scale, and 4% had decreasing returns to scale. In the context of DEA, 44% of the departments were classified as highly efficient, indicating that their input-output ratios had reached an optimal state. Meanwhile, 56% of the departments were identified as non-DEA efficient, suggesting that there was room for improvement in their input-output efficiency.

Conclusion: The improvement of input-output indexes of non-DEA effective clinical departments was defined by the BCC model. Use of DMUs could improve the efficiency of inventory control by optimizing the allocation of inventory control resources and refining inventory control measures.

Objective: To investigate the influencing factors affecting prognosis in patients undergoing drilling drainage surgery for hypertensive intracerebral hemorrhage (HICH) and to construct a nomogram predictive model.

Methods: Clinical data of 247 patients with HICH admitted to our hospital between October 2020 and February 2024 were retrospectively analyzed. Patients were divided into a modeling cohort (173 cases) and a validation cohort (74 cases). The modeling group was separated into a good prognosis group and a poor prognosis group based on postoperative prognosis.

Results: Among the 173 patients in the modeling cohort, 19 patients (10.98%) experienced poor prognosis. Multivariate logistic regression analysis showed that age, preoperative GCS score, diabetes history, systolic blood pressure, diastolic blood pressure, pulmonary infection and postoperative hematoma volume were the risk factors for the prognosis of drilling drainage surgery for patients with HICH (P<0.05). The AUC of the modeling group and validation group was 0.962 and 0.946, and the H-L test showed χ ²=7.105 and 7.246, with P<0.05 for both, indicating favorable consistency of the model. Decision curve analysis (DCA) showed high clinical utility of the nomogram model within the probability threshold range of 0.05 to 0.93.

Conclusion: Age, preoperative GCS score, history of diabetes, systolic blood pressure, diastolic blood pressure, pulmonary infection and postoperative hematoma volume are key prognostic factors affecting outcomes after drilling drainage surgery in HICH patients. The established nomogram model based on these variables accurately predicts the risk of poor postoperative prognosis and can serve as an effective clinical reference tool.

Background: Difficult intravenous (IV) access (DIVA) remains a significant challenge in healthcare, leading to treatment delays, patient discomfort, and adverse outcomes. Contributing factors include patient conditions (eg, obesity, dehydration, anatomical variations) and provider-related challenges (eg, inadequate training, improper technique). Addressing DIVA requires a structured, data-driven approach.

Purpose: This study examines the root causes of IV access complications, their prevalence, and distribution across healthcare settings. It evaluates the impact of an escalation pathway and data-driven strategies to improve IV success rates, provider training, and process standardization.

Patients and methods: A retrospective analysis was conducted on 311 DIVA patients at Sheikh Shakhbout Medical City (SSMC), Abu Dhabi, UAE, over seven months (June-December 2023). Data were obtained from electronic medical records (EMR) and Intensive Care Outreach Nurse (ICON) consultation forms. A novel IV escalation pathway and documentation system facilitated data collection on patient demographics, IV access reasons, and primary diagnoses.

Results: Among ICON-assisted patients, 74.6% had a known DIVA history, with cancer and renal disease being common conditions. ICONs achieved a first-attempt success rate of 68.8%, underscoring the need for improved floor nurse training. The most frequent IV access indications were medication administration, antibiotics, laboratory tests, and analgesia. Additionally, 57.3% of ICON consultations occurred outside standard hours, highlighting resource allocation challenges.

Conclusion: Structured escalation pathways, advanced nurse training, and data-driven decision-making are critical in addressing DIVA. Recommendations include a dedicated IV access team, ultrasound-guided cannulation, and predictive analytics to identify high-risk patients, ultimately enhancing patient outcomes and healthcare efficiency.

Objective: Venous thromboembolism (VTE) is a significant concern in critically ill patients. However, the incidence and risk factors of VTE in high-altitude regions like Tibet remain unclear. This study aimed to assess the effectiveness and safety of standardized anticoagulation therapy in preventing VTE among intensive care unit (ICU) patients in Tibet.

Methods: This prospective controlled study included 78 patients in the treatment group receiving low molecular weight heparin (LMWH) and 56 patients in the control group without standardized VTE prophylaxis. VTE incidence, risk factors, and safety outcomes were compared between the two groups. Patients were followed up for a minimum of one week after ICU discharge to assess VTE outcomes.

Results: The incidence of VTE was significantly lower in the treatment group (35.9%) compared to the control group (42.9%, p<0.05). Risk factors for VTE included mechanical ventilation. The most common VTE type observed was deep vein thrombosis (DVT), with pulmonary embolism (PE) occurring less frequently. No significant bleeding events were observed in the treatment group.

Conclusion: Standardized LMWH prophylaxis effectively reduces VTE incidence in critically ill patients in Tibet without increasing bleeding risk. Regular risk assessment and appropriate prophylaxis should be implemented in high-altitude ICU settings.

Background and purpose: Concern over the low hepatitis immunization rates is growing. It is generally believed that misinformation and disinformation are the main barriers to the success of mass immunization campaigns. This study intends to investigate the matter of online misinformation and disinformation about hepatitis vaccination and its implications.

Material and methods: The researchers employed a cross-sectional research design vis-à-vis a web-based survey method to collect data. Data were collected from a nationally representative sample of 1931 internet users across Pakistan. Participation in the study was voluntary. Data collection continued for four months, from Mar 1, 2024, to Jun 30, 2024.

Results: The "results" of structural equation modeling show that misinformation and disinformation substantially structure risk perception regarding hepatitis, which, in turn, impacts problem recognition, involvement recognition, and constraint recognition. Besides, the outcomes uncovered that the chain of problem identification impacts situational motivation, which drives risk communication behaviors. Also, these risk communication behaviors are closely linked to individuals' motivation to receive the hepatitis vaccine.

Conclusion: This research concludes that managing misinformation and disinformation via strategically designed, valid, and reliable digital interventions improves the public's active response regarding willingness to get vaccinated against hepatitis. This study contributes to increasing public acceptance of the hepatitis vaccine by utilizing reliable and valid digital interventions to achieve a more active public health response.

Purpose: This study aims to compare the differences in medical insurance reimbursement for TB treatment in Guangxi and to analyze the effects of such variations, thereby contributing to the enhancement of TB care and control.

Patients and methods: A survey was conducted across 49 randomly selected TB-designated hospitals in Guangxi using structured questionnaires and patient records. Missing data were addressed via median imputation. Non-parametric test was used to analyse and compare the differences in treatment outcomes among hospitals of different levels and types, with a P value less than 0.05 as the test criterion. Logistic regression analysis was performed to evaluate the independent effects of medical insurance reimbursement, hospital level, hospital type and service ability on TB treatment outcomes.

Results: The Urban Employee Basic Medical Insurance provided significantly higher reimbursement floors, ceilings, and rates compared to the Urban Resident Basic Medical Insurance (URBMI). Tertiary hospitals offered higher reimbursement floors for inpatient care but lower reimbursement rates compared to secondary hospitals. Despite policy reimbursement rates for TB treatment consistently exceeding 60%, the actual reimbursement rates often fell short of these benchmarks, especially in specialist hospitals and secondary care facilities. URBMI reimbursement ceiling for pulmonary TB of inpatients was positively associated with treatment success. Additionally, a lower URBMI reimbursement floor for pulmonary TB of inpatients was linked to higher disease mortality rates. Areas exhibited lower treatment success rates and higher case fatality rates shared common socioeconomic characteristics, including smaller populations, lower per capita output values, depressed production values, and lower disposable incomes among the rural population.

Conclusion: This study underscores the importance of equitable medical insurance reimbursement policies, and targeted reforms, such as raising URBMI reimbursement ceilings and enforcing real-time monitoring of actual reimbursements, are critical to mitigate disparities in TB care.

Objective: This study examines the implementation of '6S' nursing management in sterilization and supply centers to evaluate its impact on improving surgical instrument management and overall operational quality.

Methods: The pre-management phase was from January to June 2019, during which the conventional management mode was applied in our hospital's sterilization and supply center, and 100 instrument packs were selected for quality inspection. The post-management phase was from July to December 2019, when the 6S management mode was implemented in the sterilization and supply center, with another 100 instrument packs selected for quality inspection. A total of 20 staff members were involved. The management quality (environmental management, packaging management, cleaning and sterilization, and instrument management), mastery of professional knowledge (theoretical knowledge, practical knowledge, and nursing skill knowledge), incidence of surgical instrument processing errors, quality of work (grooming, problem-solving, sense of responsibility, sense of initiative, and team communication), and work pass rate before and after 6S management were compared.

Results: After 6S management, the management quality scores were higher, the mastery of professional knowledge of personnel was raised, the quality of work was promoted, the overall incidence of errors in surgical instrument processing was reduced to 1%, and the qualification rates for cleaning, sterilization, and issuance were all increased to 100%, with significant differences compared to before the implementation of management measures (all P < 0.05).

Conclusion: The application of "6S" nursing management in the sterilization and supply center is an effective approach to addressing the current challenges of enhancing sterilization quality and management efficiency, significantly improving the quality of surgical instruments and work efficiency.

Purpose: Down syndrome is the most common chromosomal abnormality in newborns, often leading to developmental delays and congenital structural anomalies. This study employed multiple machine learning models to perform risk prediction and result exploration for first-trimester Down syndrome in East Asian populations, aiming to identify an optimal risk prediction model that will enhance future predictions of Down syndrome risk and improve the efficiency of the screening process.

Patients and methods: This study collected data from the Down syndrome screening database at Taipei Chang Gung Memorial Hospital from May 1, 2018, to February 29, 2024. The dataset included 3,812 cases available for analysis, comprising 165 high-risk cases and 3,647 low-risk cases. Fourteen features (including maternal age, nuchal translucency thickness, serum markers, etc.) were input into the twelve machine learning models, along with seven data-balancing algorithms, to explore the risk prediction outcomes. The performance of these models was thoroughly evaluated using AUC (Area Under the Curve), accuracy, precision, recall, and F1 scores.

Results: Among the twelve machine learning models, the highest recall of 0.84 for high-risk cases was achieved by LightGBM combined with the RUS (Random Undersampling) data balancing algorithm. The highest AUC of 0.939 was attained by the ANN and LSTM models when combined with the ROS (Random Oversampling) data balancing algorithm.

Conclusion: The proposed ANN machine learning model, based on deep neural networks and combined with the ROS data balancing method, achieved an impressive AUC of 0.939 for classifying first-trimester Down syndrome risk in the East Asian population. Notably, this model also achieved an outstanding classification accuracy of 0.97. These results demonstrate the potential of the proposed ANN machine learning model for the accurate prediction of first-trimester Down syndrome risk.

Background: Postoperative pulmonary complications (PPCs) in children are common. However, few models tailored specifically for children are available to identify risk factors for PPCs and enable preoperative interventions. This study aimed to identify independent risk factors for PPCs in children and establish a risk prediction model.

Methods: The clinical data of pediatric patients aged 0-6 years with an American Society of Anesthesiologists (ASA) physical status of I or II, and had undergone surgery with mechanical ventilation at Henan Provincial People's Hospital between January 2020 and December 2021 were retrospectively reviewed. Univariate and multivariate logistic regression analyses were employed to identify risk factors for PPCs. The corresponding nomogram prediction model was constructed based on the regression coefficients. The receiver operating characteristic curve and calibration curve were used respectively to evaluate the discriminant validity and calibration of the prediction model.

Results: Among 1545 patients included, 211 (13.4%) developed PPCs (156 of 1082 patients in the discovery cohort and 55 of 463 patients in the test cohort). In the multivariate logistic regression analysis, age (odds ratio [OR] 0.87, 95% confidence interval [CI] 0.79-0.96, P=0.007), mechanical ventilation time (OR 1.36, 95% CI 1.20-1.55, P<0.001), airway device (OR 1.67, 95% CI 1.04-2.68, P=0.033), ASA physical status (OR 1.96, 95% CI 1.34-2.88, P=0.001), and type of surgery (the total effect, P=0.004) were identified as the independent risk factors for PPCs in the discovery cohort. The prediction model showed good discrimination and calibration performance in both the discovery and test cohorts. The corresponding area under the curve was 0.762 (95% CI: 0.722, 0.803) and 0.818 (95% CI: 0.760, 0.875), respectively.

Conclusion: We identified age, ventilation device and duration, ASA physical status, and surgical site as independent risk factors for PPCs in children aged 0-6 years. The predictive model performed well and demonstrated a certain capability in predicting the risk of PPCs.

Objective: This cross-sectional study aimed to assess the public awareness, knowledge, and perception of congenital disabilities in Saudi Arabia, with a focus on identifying demographic factors that influence these perceptions.

Methods: A structured questionnaire was distributed to 1007 participants across various regions of Saudi Arabia. The questionnaire covered demographic information, knowledge of congenital disabilities, awareness of genetic and pharmacological risk factors, and engagement in preventive practices. Statistical analysis included descriptive statistics, correlation coefficients, and general linear modeling to understand the impact of demographic variables on awareness and preventive behaviors.

Results: The study showed moderate public awareness and knowledge about congenital disabilities, with 49.6% of respondents acknowledging awareness and only 8.3% demonstrating excellent understanding. Perceived risks associated with genetic and environmental factors were recognized by over half of the participants. The awareness did not consistently translate into engagement in preventive practices, which remained suboptimal across the population. Demographic factors such as age and having children significantly influenced both risk perception and engagement in preventive behaviors.

Conclusion: Despite moderate levels of awareness, there remains a significant gap in comprehensive knowledge and active engagement in preventive practices against congenital disabilities in Saudi Arabia. The findings suggest the need for targeted educational programs and public health initiatives to enhance understanding and proactive management of risk factors associated with congenital disabilities. These efforts should particularly focus on younger populations and those without children, where risk perception and engagement were lower.