Hospitals in many countries face the need for balancing different categories of expenditures to achieve multiple goals within a limited budget. This study established a two-stage fuzzy linear programming (FLP) estimation model to explore the optimal allocation decision-making of expenditure budget under the multi-objective constraints. Taking all urban public hospitals in Henan province of China as a sample, the optimal allocation decision-making of total expenditure budget was tested with the human resources expenditures (HE) as the dependent variable. And the outcome was compared with the actual expenditure data of these hospitals between 2010 and 2016. The study found that when the HE achieves the maximum and minimum feasible scale, the expenditure scales of the budget allocation categories including pharmaceutical expenditures, medical supplies expenditures, and other expenditures were all within a reasonable range. Among them, the observed promoting space for HE was 3.78 billion yuan. The results show that the FLP method can help urban public hospitals to make better total expenditure budget allocation decisions, which can maintain their reasonable expenditure structure under the hospitals’ development goals and the government’s regulatory requirements.
Self-rated health among old adults (SHOA) indicates individuals' subjective assessments and evaluations of their overall health based on objective physical circumstances. The purpose of this study was to analyze the current state and influencing factors of the subjective perception-based self-rated health (SH) by qualifying selected older adults with similar objective physical conditions, as well as to explore the equality and changing trends of SHOA based on influencing factors.This study designed a cross-sectional study, conducted in three provinces in east, central and west China, and included 1,153 older adults (> = 60 years) with intact physical condition (IPC). The current state of SHOA and its influencing factors were analyzed using mean comparisons and Logistic regression (LR) models. The equality level and trend of SHOA's effect on health literacy, health habits, and access to health care were determined using the Lorenz curve, Gini coefficient, and Vector Autoregression (VAR) model.The mean SHOA with IPC was 74.37 ± 13.22. Findings from LR modeling indicated that SHOA with IPC was mainly influenced by age and communication methods (P < 0.05). It was also observed that the total Gini coefficient of the allocation of SHOA with IPC based on communication methods was equal to 0.0188, and the VAR results showed that the total effect of change in SHOA on health literacy among older adults was negative and its duration of the effect exceeded 50.The SHOA with IPC was shown to be better and was primarily influenced by age and communication methods. The observed effect of SHOA on health literacy was negative and lasting. To improve SHOA with IPC even further, policymakers could consider promoting the use of modern and convenient communication methods (such as smartphones) through training and purchasing subsidies, as well as focusing on increasing sustained attention and promoting health literacy and behavior among older adults with improved SH.
The level of outpatient satisfaction plays a significant role in improving the quality and utilization of healthcare services. Patient satisfaction gives providers insights into various aspects of services including the effectiveness of care and level of empathy. This study aimed to evaluate the level of patient satisfaction in the outpatient department and to explore its influencing factors in large hospitals (accommodating over 1000 beds) of Henan province, China.We analyzed data from Henan Large Hospitals Patient Satisfaction Survey conducted in the year 2018 and included 630 outpatients. Structural Equation Model (SEM) was used to explore the relationship among evaluation indicators of outpatient satisfaction levels. We used Dynamic Matter-Element Analysis (DMA) to evaluate the status of outpatient satisfaction. Binary Logistic Regression (BLR) was adopted to estimate the impact of personal characteristics towards outpatient satisfaction.The overall score for outpatient satisfaction in large hospitals was 66.28±14.73. The mean outpatient satisfaction scores for normal-large, medium-large, and extra-large hospitals were 63.33±12.12, 70.11±16.10, 65.41±14.67, respectively, and were significantly different (F = 11.953, P < 0.001). Waiting time, doctor-patient communication, professional services, and accessibility for treatment information were shown to have directly positive correlations with outpatient satisfaction (r = 0.42, 0.47, 0.55, 0.46, all P < 0.05). Results from BLR analysis revealed that patients' age and frequency of hospital visits were the main characteristics influencing outpatient satisfaction (P < 0.05).The outpatient satisfaction of large hospitals is moderately low. Hospital managers could shorten the waiting time for outpatients and improve the access to treatment information to improve the satisfaction of outpatients. It is also necessary to enhance service provision for outpatients under the age of 18 as well as the first-time patients.
Abstract Background Meeting the demands of older adults for health promotion services (DOAHPS) is essential for maintaining their health and enhancing their quality of life. The purpose of this study was to construct a model for evaluating DOAHPS to quantitatively evaluate the current state and equity level of DOAHPS in China, as well as to explore the main factors affecting DOAHPS’ current state and equity level. Methods This study analyzed the DOAHPS data from the "Survey on Chinese Residents' Health Service Demands in the New Era", which included 1542 older adults aged 65 and older. Relationships between evaluation indicators of DOAHPS were explored using Structural Equation Modeling (SEM). The Weighted TOPSIS method and Logistic regression (LR) were used to analyze the current state and factors impacting DOAHPS. The equity level of DOAHPS’ allocation among different older adult groups and its influencing factors were determined using the Rank Sum Ratio (RSR) method and T Theil index. Results The evaluation score for DOAHPS was 42.57 ± 1.51. Health status, health literacy and behavior were positively correlated with DOAHPS ( r = 0.40, 0.38; P < 0.05). The LR results revealed that the most significant determinants of DOAHPS were sex, residence, education level and pre-retirement occupation (all P < 0.05). The number of older adults with very poor, poor, general, high and very high level health promotion service demands accounted for 2.27%, 28.60%, 53.05%, 15.43% and 0.65%, respectively. The total T Theil index of DOAHPS was 2.7433*10 –4 , and the intra-group difference contribution rate exceeded 72%. Conclusions Compared to the maximum level, the total DOAHPS level was found to be moderate, although the demands of urban seniors with higher levels of education may be substantially greater. The observed inequities in the allocation of DOAHPS were primarily related to differences in education level and pre-retirement occupation within group. To better address health promotion services for older adults, policymakers could target older males with low education who reside in rural regions.
The purpose of this study is to evaluate practices and effects of provider payment system reform of Henan province, China, which is composed of case-based payment for inpatients.Data for the study were obtained from the initial pilot hospital, the People's Hospital of Yiyang County, from 2010 to 2013.Compared with the previous payment intervention in 2011, the hospital built a new case-based payment system for inpatients progressively, which replaced the original fee for service payment.As of 2013, the new payment included 188 diseases, and covered more than 72% inpatients and hospitalization expenditure.The proportion of drug use and antibiotic usage continue to decrease, and the singular disease prices decided by negotiate would affect average hospitalization expenditure and so on.These findings demonstrated both significant achievements and further efforts to be made to strengthen China's rural medical service payment reform and enhance their effect.
AbstractBackground: The coupling coordination of population, medical care and public health system is crucial for advancing health care development and improving health governance efficiency. The dramatic shifts in global population structures are reshaping health demands, placing unprecedented challenges on health care systems. This study investigates the population, medical care system and public health system in China from 2011 to 2021, revealing their spatiotemporal dynamic evolution. The findings are expected to provide valuable insights for promoting the coordinated development of population and health care system and improve health governance. Methods: The data for this study were sourced from the China Statistical Yearbook and the China Health Statistics Yearbook. On the basis of relevant data, an evaluation model was developed to assess the coupling coordination degree of the population health multisystem. A quantile regression model was adopted to explore the influencing factors. Results: From 2010 to 2021, China experienced significant improvements across the three major systems. The evaluation index for the medical care system increased from 0.109 to 0.782, whilst the population and public health indices rose from 0.195 and 0.008 to 0.702 and 0.842, respectively. This development can be categorised into two phases: ‘rapid growth’ (2010–2018) and ‘fluctuating growth’ (2019–2021). The coupling coordination degree amongst the three systems steadily increased from 0.155 in 2010 to 0.668 in 2021, with an average annual growth rate of 14.2%. This phenomenon marked a shift from a state of severe dysfunction to one of primary coordination. However, significant regional disparities persisted, with varying rates of development and coordination. The eastern regions consistently led, followed by the western and central regions. This study identified multiple factors influencing the coupling coordination of the population–health care multisystem, including population structure, economic income, medical service efficiency, public health resources and health management service. Notably, health management emerged as a crucial factor at Q1 (b=2.283, P=0.002), whilst population structure was the most significant factor at Q3 (b=3.439, P<0.001). Conclusion: Structural adjustments for enhancing coupling coordination can effectively boost overall health sector efficiency and address reform challenges. Development strategies involve two pathways: economy- and policy-driven pathways. Economically disadvantaged areas should focus on policy-driven efforts to enhance coupling coordination, whereas economically developed areas should leverage economic momentum to accelerate the development of integrated health services to meet people’s needs throughout their life cycle. Additionally, considering population and regional characteristics is crucial, emphasising the need for health care systems tailored to the unique structural features and developmental strengths of each region.
Traditional medicine has been widely used to address relatively common illnesses. In this regard, Chinese government has been continually topping up its investments on public Traditional Chinese Medicine hospitals (PTHs) in recent years. This study aimed to assess the optimal scales and structure of the investments in Henan province by analyzing the contribution of Government Financial Investment (GFI) to the efficiency and revenue growth of PTHs as well as recommending proper investment strategies for implementation to policy-makers.This study was a panel data study, conducted in Henan Province, China. By collecting 143 PTHs' operational data from the year 2005 to 2017, Barro Economic Growth (BEG) model, Stochastic Frontier Analysis (SFA) and Vector Autoregressive (VAR) model were used to assess the efficiency and PTHs revenue.The study observed the positive contribution of GFI to PTHs' revenue growth (average MPG = 2.84), indicating that the GFI had not reached the required optimal level of "Barro Law". In order to maximize the input-output efficiency, the scales of GFI on Grade III, Grade II A, Grade II B PTHs need to be increased by - 5.96, 4.88 and 11.51%, respectively. The third year following the first investment may be a more essential period for conducting an effective GFI evaluation in Henan Province.GFI on PTHs usually has a long-term impact on PTHs. Governments can adjust its GFI policy so as to maximize the input-output efficiency.
Prediction of low Apgar score for vaginal deliveries following labor induction intervention is critical for improving neonatal health outcomes. We set out to investigate important attributes and train popular machine learning (ML) algorithms to correctly classify neonates with a low Apgar scores from an imbalanced learning perspective.We analyzed 7716 induced vaginal deliveries from the electronic birth registry of the Kilimanjaro Christian Medical Centre (KCMC). 733 (9.5%) of which constituted of low (< 7) Apgar score neonates. The 'extra-tree classifier' was used to assess features' importance. We used Area Under Curve (AUC), recall, precision, F-score, Matthews Correlation Coefficient (MCC), balanced accuracy (BA), bookmaker informedness (BM), and markedness (MK) to evaluate the performance of the selected six (6) machine learning classifiers. To address class imbalances, we examined three widely used resampling techniques: the Synthetic Minority Oversampling Technique (SMOTE) and Random Oversampling Examples (ROS) and Random undersampling techniques (RUS). We applied Decision Curve Analysis (DCA) to evaluate the net benefit of the selected classifiers.Birth weight, maternal age, and gestational age were found to be important predictors for the low Apgar score following induced vaginal delivery. SMOTE, ROS and and RUS techniques were more effective at improving "recalls" among other metrics in all the models under investigation. A slight improvement was observed in the F1 score, BA, and BM. DCA revealed potential benefits of applying Boosting method for predicting low Apgar scores among the tested models.There is an opportunity for more algorithms to be tested to come up with theoretical guidance on more effective rebalancing techniques suitable for this particular imbalanced ratio. Future research should prioritize a debate on which performance indicators to look up to when dealing with imbalanced or skewed data.
The goal of this study was to establish the most efficient boosting method in predicting neonatal low Apgar scores following labor induction intervention and to assess whether resampling strategies would improve the predictive performance of the selected boosting algorithms.A total of 7716 singleton births delivered from 2000 to 2015 were analyzed. Cesarean deliveries following labor induction, deliveries with abnormal presentation, and deliveries with missing Apgar score or delivery mode information were excluded. We examined the effect of resampling approaches or data preprocessing on predicting low Apgar scores, specifically the synthetic minority oversampling technique (SMOTE), borderline-SMOTE, and the random undersampling (RUS) technique. Sensitivity, specificity, precision, area under receiver operating curve (AUROC), F-score, positive predicted values (PPV), negative predicted values (NPV) and accuracy of the three (3) boosting-based ensemble methods were used to evaluate their discriminative ability. The ensemble learning models tested include adoptive boosting (AdaBoost), gradient boosting (GB) and extreme gradient boosting method (XGBoost).The prevalence of low (<7) Apgar scores was 9.5% (n = 733). The prediction models performed nearly similar in their baseline mode. Following the application of resampling techniques, borderline-SMOTE significantly improved the predictive performance of all the boosting-based ensemble methods under observation in terms of sensitivity, F1-score, AUROC and PPV.Policymakers, healthcare informaticians and neonatologists should consider implementing data preprocessing strategies when predicting a neonatal outcome with imbalanced data to enhance efficiency. The process may be more effective when borderline-SMOTE technique is deployed on the selected ensemble classifiers. However, future research may focus on testing additional resampling techniques, performing feature engineering, variable selection and optimizing further the ensemble learning hyperparameters.
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