Purpose: To identify the factors influencing inpatient satisfaction by fitting the optimal discriminant model. Patients and Methods: A cross-sectional survey of inpatient satisfaction was conducted with 3888 patients in 16 large public hospitals in Zhejiang Province. Independent variables were screened by single-factor analysis, and the importance of all variables was comprehensively evaluated. The relationship between patients’ overall satisfaction and influencing factors was established, the relative risk was evaluated by marginal benefit, and the optimal model was fitted using the receiver operating characteristic curve. Results: Patients’ overall satisfaction was 79.73%. The five most influential factors on inpatient satisfaction, in this order, were: patients’ right to know, timely nursing response, satisfaction with medical staff service, integrity of medical staff, and accuracy of diagnosis. The prediction accuracy of the random forest model was higher than that of the multiple logistic regression and naive Bayesian models. Conclusion: Inpatient satisfaction is related to healthcare quality, diagnosis, and treatment process. Rapid identification and active improvement of the factors affecting patient satisfaction can reduce public hospital operating costs and improve patient experiences and the efficiency of health resource allocation. Public hospitals should strengthen the exchange of medical information between doctors and patients, shorten waiting time, and improve the level of medical technology, service attitude, and transparency of information disclosure.

Objective: This study aims to develop risk prediction models for neck and shoulder musculoskeletal disorders among healthcare professionals.Methods: A stratified sampling method was employed to select employees from medical institutions in Nanning City, yielding 617 samples. The Boruta algorithm was used for feature selection, and various models, including Tree-Based Models, Single Hidden-Layer Neural Network Models (MLP), Elastic Net Models (ENet), and Support Vector Machines (SVM), were applied to predict the selected variables, utilizing SHAP algorithms for individual-level local explanations.Results: The SVM model excels in both Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) and exhibits more stable performance when generalizing to unseen data. The Random Forest model exhibited relatively high overall performance on the training set. The MLP model emerges as the most consistent and accurate in predicting shoulder musculoskeletal disorders, while the SVM model shows strong fitting capabilities during the training phase, with occupational factors identified as the main contributors to WMSDs.Conclusion: This study successfully constructs work-related musculoskeletal disorder risk prediction models for healthcare professionals, enabling a quantitative analysis of the impact of occupational factors. This advancement is beneficial for future economical and convenient work-related musculoskeletal disorder screening in healthcare professions.

The integrated county healthcare consortium (ICHC) is becoming an important measure to improve the capacity of primary-level medical services and to achieve grading diagnosis and treatment system in China. However, it is not clear whether health professionals are satisfied with this reform and what are the problems with it. This study aimed to understand the satisfaction of healthcare professionals to the ICHC in Zhejiang Province, China, and analyze the problems and improvement measures of the ICHC. A cross-sectional study was carried out in the 11 pilot counties (cities and districts) implementing the construction of the ICHC in Zhejiang in November 2019. Healthcare professionals from the leading county-level hospital, three township health centers (THCs) or community health centers (CHCs) in each ICHC were invited to participate in this survey. A total of 3531 healthcare professionals were included, 85.92% of the participants agreed that the integration of the Centers for Disease Control and Prevention (CDC) and other professional public health institutions into the construction of ICHC could actively promote basic public health work. The most severe problem was the lack of financial guarantee fund input (severity score: 2.92 ± 1.76). The most crucial measure to promote the construction of the ICHC was to increase government financial input and improve the security mechanism (importance score: 4.81 ± 0.47). The satisfaction of the healthcare professionals to the ICHC was 89.41%. The satisfaction of healthcare professionals from county-level hospitals was 2.37 (95% CI: 1.760–3.238) times higher than that of healthcare professionals from the township health centers (THCs) or community health centers (CHCs). The satisfaction of health professionals with a college degree or below was 3.215 (95% CI: 1.413–6.786) times higher than that of health professionals with a master’s degree or above. Zhejiang Province has taken adequate measures to promote the construction of the ICHC. However, there are still some problems. Appropriate and effective policies should be implemented to enhance policy coordination and promote competition among ICHCs, as well as to strengthen medical service quality management and improve performance appraisal scheme.

Objective: The continuous increase in health care costs and the growing demand for health services among residents make it an urgent priority to improve the operational efficiency of the health system in Chinese society. In this study, data on the operational efficiency of tertiary public hospitals in Guangxi were analyzed to identify issues in hospital management within the context of performance assessment, which thereby enhanced the social service capacity of hospitals.Methods: A comprehensive evaluation index system was constructed based on the “national monitoring indicators” of operational efficiency. The indicators were analyzed using data envelopment analysis with Banker, Charnes and Cooper (DEA-BCC) and Malmquist index models. The Tobit regression model was used to analyze the major factors affecting the efficiency of public hospitals.Results: Between 2019 and 2022, the pure technical efficiency (PTE) of 61 tertiary public hospitals in Guangxi remained at a relatively high level. The results of the Malmquist index showed a downward trend. The technical progress (TC) indicator became the main factor affecting the decline in the operational efficiency of hospitals. Tobit regression analysis revealed that plenty of factors exerted a significant impact on the operating efficiency of hospitals. These factors included the number of beds, the ratio of outpatient and inpatient patients relative to total patient numbers, the proportion of discharged patients undergoing surgery among total patients, business expenditures and total annual revenue.Conclusion: The scale of tertiary public hospitals in Guangxi is prominently unreasonable. It is necessary to raise the efficiency of resource utilization. The operation and management situation is not optimistic. Hospitals should accelerate the transformation of their development model, rationally allocate medical resources and shift from scale expansion to the improvement of quality and efficiency. Meanwhile, they should actively participate in establishing the hierarchical medical treatment system, controlling operating costs and reasonably increasing the proportion of personnel expenses to improve operational and management efficiency.

Objective: This research aimed to identify the fundamental and geographic characteristics of the primary healthcare personnel mobility in Nanning from 2000 to 2021 and clarify the determinants that affect their transition to non-primary healthcare institutions. Methods: Through utilizing the Primary Healthcare Personnel Database (PHPD) for 2000–2021, the study conducts descriptive statistical analysis on demographic, economic, and professional aspects of healthcare personnel mobility across healthcare reform phases. Geographic Information Systems (QGIS) were used to map mobility patterns, and R software was employed to calculate spatial autocorrelation (Moran’s I). Logistic regression identified factors that influenced the transition to non-primary institutions. Results: Primary healthcare personnel mobility is divided into four phases: initial (2000–2008), turning point (2009–2011), rapid development (2012–2020), and decline (2021). The rapid development stage saw increased mobility with no spatial clustering in inflow and outflow. From 2016 to 2020, primary healthcare worker mobility reached its peak, in which the most significant movement occurred between township health centers and other institutions. Aside from their transition to primary medical institutions, the primary movement of grassroots health personnel predominantly directs towards secondary general hospitals, tertiary general hospitals, and secondary specialized hospitals. Since 2012, the number and mobility distance of primary healthcare workers have become noticeably larger and remained at a higher level from 2016 to 2020. The main migration of primary healthcare personnel occurred in their districts (counties). Key transition factors include gender, education, ethnicity, professional category, general practice registration, and administrative division. Conclusions: This study provides evidence of the features of primary healthcare personnel mobility in the less developed western regions of China, in which Nanning was taken as a case study. It uncovers the factors that impact the flow of primary healthcare personnel to non-primary healthcare institutions. These findings are helpful to policy refinement and support the retention of primary healthcare workers.

Compensation plays a critical role in motivating staff and enhancing operational performance and human resource costs in hospitals. This study was aimed at investigating pay levels and the key factors influencing pay satisfaction in secondary and tertiary public hospitals in Guangxi. Questionnaires were distributed to 48 hospitals across 14 prefecture-level cities in Guangxi. Information on personal characteristics, salary levels, work situations and perceptions of current salary conditions was provided by 10,343 staff in secondary and tertiary hospitals. Five machine learning models were employed to identify the most significant influencing factors of salary satisfaction in Guangxi public hospitals. Overall, the actual total after-tax income in secondary public hospitals in Guangxi ranged from $466.55-$744, while the income of staff in municipal-level tertiary public hospitals ranged from $5,001 to $1,041.75 per month. Among the five models, the support vector machine (SVM) demonstrated the best performance in analyzing the influencing factors of compensation satisfaction. The most influential factors for total compensation satisfaction included the extent to which compensation reflected labor value, salary increases since 2017 compared to peer hospitals, total after-tax income and the difference in compensation between staff within and outside the establishment of hospitals. Satisfaction with salary growth aligned closely with the factors influencing overall compensation satisfaction. Satisfaction with pay equity was also influenced by the ability of salary gaps between different positions to reflect differential effort. A relatively low pay level in secondary hospitals in Guangxi was revealed. The factors influencing satisfaction with total pay, pay fairness and pay growth since 2017 varied. SVM outperformed other models in the analysis of the factors influencing pay satisfaction. To enhance pay satisfaction in secondary and tertiary hospitals in Guangxi, it is crucial to establish a salary distribution system aligned with the value of labor across different positions and tailored to the unique characteristics of each hospital.