The Efficacy of Rule of Law Publicity Short Video Platforms in the Prevention of Medical Disputes Among Healthcare Professionals: A Propensity Score Analysis.

Background: Medical disputes are a recurrent and pressing issue in hospitals, posing significant challenges to the functioning of medical institutions. We aimed to investigate whether receiving rule of law publicity on short video platforms is relevant to preventing medical disputes among healthcare professionals. Methods: We collected the data from 37,978 medical professionals from 130 tertiary public hospitals. Participants were classified into two groups according to the presence of receiving rule of law publicity on short video platforms. A subgroup analysis was performed before and after propensity score analysis, and multiple logistic regression was used to identify risk factors for medical disputes. Results: Among all participants, 46.1% (17,506/37,978) experienced medical disputes. Before propensity score analysis, the prevalence of medical disputes among participants who received rule of law publicity on short video platforms was similar to that among participants who did not (P = 0.639). However, after propensity score analysis, participants who received the rule of law publicity on short video platforms did not show a benefit effect. These participants had a significantly higher rate of suffering from medical disputes than participants who did not receive publicity on this platform (P=0.020). Multiple logistic regression analysis confirmed that receiving the rule of law publicity through short video platforms (P=0.010) or MicroBlog (P = 0.016), and previously facing legal issues outside of medical work (P < 0.001) were risk factors for medical disputes; participating in legal training organized by hospitals (P=0.004) and the hospital rule of law being very good (P=0.045) were protective factors. Conclusion: Medical disputes are a common occurrence within the healthcare profession. However, using short video platforms to promote the rule of law is not an effective method to prevent disputes. Instead, healthcare professionals can benefit from participating in legal training and having a well-established rule of law within the hospital construct.

Prediction of Medical Disputes Between Health Care Workers and Patients in Terms of Hospital Legal Construction Using Machine Learning Techniques: Externally Validated Cross-Sectional Study.

BACKGROUND: Medical disputes are a global public health issue that is receiving increasing attention. However, studies investigating the relationship between hospital legal construction and medical disputes are scarce. The development of a multicenter model incorporating machine learning (ML) techniques for the individualized prediction of medical disputes would be beneficial for medical workers. OBJECTIVE: This study aimed to identify predictors related to medical disputes from the perspective of hospital legal construction and the use of ML techniques to build models for predicting the risk of medical disputes. METHODS: This study enrolled 38,053 medical workers from 130 tertiary hospitals in Hunan province, China. The participants were randomly divided into a training cohort (34,286/38,053, 90.1%) and an internal validation cohort (3767/38,053, 9.9%). Medical workers from 87 tertiary hospitals in Beijing were included in an external validation cohort (26,285/26,285, 100%). This study used logistic regression and 5 ML techniques: decision tree, random forest, support vector machine, gradient boosting decision tree (GBDT), and deep neural network. In total, 12 metrics, including discrimination and calibration, were used for performance evaluation. A scoring system was developed to select the optimal model. Shapley additive explanations was used to generate the importance coefficients for characteristics. To promote the clinical practice of our proposed optimal model, reclassification of patients was performed, and a web-based app for medical dispute prediction was created, which can be easily accessed by the public. RESULTS: Medical disputes occurred among 46.06% (17,527/38,053) of the medical workers in Hunan province, China. Among the 26 clinical characteristics, multivariate analysis demonstrated that 18 characteristics were significantly associated with medical disputes, and these characteristics were used for ML model development. Among the ML techniques, GBDT was identified as the optimal model, demonstrating the lowest Brier score (0.205), highest area under the receiver operating characteristic curve (0.738, 95% CI 0.722-0.754), and the largest discrimination slope (0.172) and Youden index (1.355). In addition, it achieved the highest metrics score (63 points), followed by deep neural network (46 points) and random forest (45 points), in the internal validation set. In the external validation set, GBDT still performed comparably, achieving the second highest metrics score (52 points). The high-risk group had more than twice the odds of experiencing medical disputes compared with the low-risk group. CONCLUSIONS: We established a prediction model to stratify medical workers into different risk groups for encountering medical disputes. Among the 5 ML models, GBDT demonstrated the optimal comprehensive performance and was used to construct the web-based app. Our proposed model can serve as a useful tool for identifying medical workers at high risk of medical disputes. We believe that preventive strategies should be implemented for the high-risk group.

Association between hospital legal constructions and medical disputes: A multi-center analysis of 130 tertiary hospitals in Hunan Province, China.

Background: Medical disputes are common in hospitals and a major challenge for the operations of medical institutions. However, few studies have looked into the association between medical disputes and hospital legal constructions. The purpose of the study was to investigate the relationship between hospital legal constructions and medical disputes, and it also aimed to develop a nomogram to estimate the likelihood of medical disputes. Methods: Between July and September 2021, 2,716 administrators from 130 hospitals were enrolled for analysis. The study collected seventeen variables for examination. To establish a nomogram, administrators were randomly split into a training group (n = 1,358) and a validation group (n = 1,358) with a 50:50 ratio. The nomogram was developed using data from participants in the training group, and it was validated in the validation group. The nomogram contained significant variables that were linked to medical disputes and were identified by multivariate analysis. The nomogram's predictive performance was assessed utilizing discriminative and calibrating ability. A web calculator was developed to be conducive to model utility. Results: Medical disputes were observed in 41.53% (1,128/2,716) of participants. Five characteristics, including male gender, higher professional ranks, longer length of service, worse understanding of the hospital charters, and worse construction status of hospital rule of law, were significantly associated with more medical disputes based on the multivariate analysis. As a result, these variables were included in the nomogram development. The AUROC was 0.67 [95% confident interval (CI): 0.64-0.70] in the training group and 0.68 (95% CI: 0.66-0.71) in the validation group. The corresponding calibration slopes were 1.00 and 1.05, respectively, and intercepts were 0.00 and -0.06, respectively. Three risk groups were created among the participants: Those in the high-risk group experienced medical disputes 2.83 times more frequently than those in the low-risk group (P < 0.001). Conclusion: Medical dispute is prevailing among hospital administrators, and it can be reduced by the effective constructions of hospital rule of law. This study proposes a novel nomogram to estimate the likelihood of medical disputes specifically among administrators in tertiary hospitals, and a web calculator can be available at https://ymgarden.shinyapps.io/Predictionofmedicaldisputes/.

Highly electrocatalytic Cu2ZnSn(S1-xSex)4 counter electrodes for quantum-dot-sensitized solar cells.

Traditional Pt counter electrode in quantum-dot-sensitized solar cells suffers from a low electrocatalytic activity and instability due to irreversible surface adsorption of sulfur species incurred while regenerating polysulfide (S(n)(2-)/S(2-)) electrolytes. To overcome such constraints, chemically synthesized Cu(2)ZnSn(S(1-x)Se(x))(4) nanocrystals were evaluated as an alternative to Pt. The resulting chalcogenides exhibited remarkable electrocatalytic activities for reduction of polysulfide (S(n)(2-)) to sulfide (S(2-)), which were dictated by the ratios of S/Se. In this study, a quantum dot sensitized solar cell constructed with Cu(2)ZnSn(S(0.5)Se(0.5))(4) as a counter electrode showed the highest energy conversion efficiency of 3.01%, which was even higher than that using Pt (1.24%). The compositional variations in between Cu(2)ZnSnS(4) (x = 0) and Cu(2)ZnSnSe(4) (x = 1) revealed that the solar cell performances were closely related to a difference in electrocatalytic activities for polysulfide reduction governed by the S/Se ratios.

Fabrication of Porous TiO(2) Hollow Spheres and Their Application in Gas Sensing.

In this work, porous TiO(2) hollow spheres with an average diameter of 100 nm and shell thickness of 20 nm were synthesized by a facile hydrothermal method with NH(4)HCO(3) as the structure-directing agent, and the formation mechanism for this porous hollow structure was proved to be the Ostwald ripening process by tracking the morphology of the products at different reaction stages. The product was characterized by SEM, TEM, XRD and BET analyses, and the results show that the as-synthesized products are anatase phase with a high surface area up to 132.5 m(2)/g. Gas-sensing investigation reveals that the product possesses sensitive response to methanal gas at 200 degrees C due to its high surface area.