1.The Spatial Differences and Dynamic Evolution of China's Healthcare Service Efficiency from 2012 to 2021
Sha-Sha SONG ; Lina SHAO ; Zhonghua SUO ; Jing WU ; Ying LANG
Chinese Health Economics 2024;43(9):70-74,96
Objective:To study the longitudinal trends and spatial clustering characteristics of healthcare service efficiency in China and in North,Northeast,East,Central,South,Southwest,and Northwest China.Methods:The Malmquist index model is used to measure China's healthcare service efficiency from 2012 to 2021,the Dagum Gini coefficient as well as the decomposition method are used to measure the magnitude and source of regional gaps in healthcare service efficiency,and the Kernel density estimation is used to study the longitudinal trend of change and spatial agglomeration characteristics of China's healthcare service efficiency.Results:China's overall healthcare service efficiency is growing,and the inter-regional gap is gradually narrowing,characterized by a concentration trend;the gap in the level of healthcare service efficiency between regions did not widen during the period under examination,but it was found that the gap within some regions was still significant.Conclusion:The national health service efficiency is growing slightly,and the regional gap is generally decreasing,but the Gini coefficient shows that the inter-regional contribution is still the main source of the gap.National health service efficiency is generally concentrated,but some regions are less efficient,with significant internal disparities.
2.Prognosis of different hemodynamic classifications in patients with pulmonary hypertension due to left heart disease
Yuan TANG ; Yanping SHI ; Lu CHEN ; Yifang SUO ; Shengen LIAO ; Cheang LOKFAI ; Yanli ZHOU ; Rongrong GAO ; Jing SHI ; Wei SUN ; Hao ZHANG ; Yanhui SHENG ; Rong YANG ; Xiangqing KONG ; Xinli LI ; Haifeng ZHANG
Chinese Journal of Cardiology 2024;52(10):1177-1185
Objective:To compare the prognostic values of different classification by using transpulmonary pressure gradient (TPG), diastolic pressure gradient (DPG) and pulmonary vascular resistance (PVR) in patients with pulmonary hypertension due to left heart disease (PH-LHD), and investigated hemodynamic and clinical factors associated with mortality in patients with PH-LHD.Methods:This was a single-center prospective cohort study. In-hospital patients diagnosed with PH-LHD via right heart catheterization at the Department of Cardiology, the First Affiliated Hospital of Nanjing Medical University, from September 2013 to December 2019 were enrolled. Patients were divided according to TPG (cutoff value 12 mmHg; 1 mmHg=0.133 kPa), DPG (cutoff value 7 mmHg), PVR (cutoff value 3 Wood Units), and the combination of TPG and PVR. Baseline characteristic was recorded. All patients were followed up until the occurrence of endpoint event, defined as all-cause death that occurred during the follow-up period, or until April 18, 2022. Receiver operating characteristic curves were used to compare the predictive value of 3 classification methods for all-cause death in PH-LHD patients. The optimal cutoff values were calculated using Jorden index. Survival analysis was performed using Kaplan-Meier analysis, and log-rank test was used to compare the predictive efficacy of classification methods based on optimal cutoff values or guidance-recommended thresholds for the survival of PH-LHD patients. Variables showing statistical significance in the univariate analysis were incorporated into multivariate Cox regression model to analyze the independent risk factors for all-cause mortality.Results:A total of 243 patients were enrolled, aged (54.9±12.7) years old, including 169 (69.5%) males. During a median follow-up of 57 months, there were 101 (41.6%) deaths occurred. Grouping results were as follows: (1) TPG: TPG≤12 mmHg group 115 patients, TPG>12 mmHg group 128 patients; (2) DPG: DPG<7 mmHg group 193 patients, DPG≥7 mmHg group 50 patients; (3) PVR: PVR≤3 Wood Units group 108 patients, PVR>3 Wood Units group 135 patients; (4) TPG and PVR: TPG≤12 mmHg and PVR≤3 Wood Units group 89 patients, TPG>12 mmHg and PVR>3 Wood Units group 109 patients. PVR ( AUC=0. 698,95% CI:0.631-0.766) had better predictive value for all-cause mortality than TPG ( AUC=0.596, 95% CI: 0.523-0.669) and DPG ( AUC=0.526, 95% CI: 0.452-0.601) (all P<0.05). The optimal cutoff values for TPG, DPG, and PVR were13.9 mmHg, 2.8 mmHg, and 3.8 Wood Units, respectively. Kaplan-Meier analysis based on the optimal cutoff values or guidance-recommended thresholds showed that PVR and TPG were the predictors of survival ( P<0.05), while DPG did not showed significance ( P>0.05). Multivariate Cox regression analysis showed that age, PVR and log 2N-terminal pro-B-type natriuretic peptide were independent risk factors for all-cause mortality in PH-LHD patients (all P<0.05). Conclusion:Classification according to PVR was most valuable in predicting all-cause death in PH-LHD patients, while TPG showed moderate predictive ability and DPG had no predictive value.
3.Construction and effect evaluation of nursing management team professionalization model in an inter-national medical center of a provincial public tertiary hospital
Nannan ZHANG ; Hong LI ; Jing CHENG ; Shanshan ZUO ; Lina SUO ; Feifei YU ; Yifei KAN
Modern Hospital 2024;24(8):1238-1242
Objective To explore the professionalization model of nursing management team in an international medical center in a provincial public tertiary hospital.Methods Through literature research and Delphi method,a three-person nursing management team was established respectively in three nursing units:outpatient,first-ward,and second-ward of the center,and then trained professionally to define management boundaries and responsibilities.The training effect was verified by applying the professionalization management in the international medical center.The three nursing teams(nine nurses totally)were compared in terms of leadership,patient satisfaction,and nursing discipline construction before and after the training.Results Following the training,the three teams all exhibited a significant improvement in leadership as well as its dimensions(P<0.05),and pa-tient satisfaction(P<0.05).Additionally,care quality,scientific research capacity,and innovation ability were all elevated across the three groups.Conclusion The establishment of a nursing management team and performance of professional training can effectively promote the concept of professionalization management,improve the leadership of nurses,cultivate talent eche-lons,drive the overall development of disciplines and teams,and expand the connotation of nursing culture.For all these bene-fits,this model is suitable for promotion and application among clinical departments.
4.The Spatial Differences and Dynamic Evolution of China's Healthcare Service Efficiency from 2012 to 2021
Sha-Sha SONG ; Lina SHAO ; Zhonghua SUO ; Jing WU ; Ying LANG
Chinese Health Economics 2024;43(9):70-74,96
Objective:To study the longitudinal trends and spatial clustering characteristics of healthcare service efficiency in China and in North,Northeast,East,Central,South,Southwest,and Northwest China.Methods:The Malmquist index model is used to measure China's healthcare service efficiency from 2012 to 2021,the Dagum Gini coefficient as well as the decomposition method are used to measure the magnitude and source of regional gaps in healthcare service efficiency,and the Kernel density estimation is used to study the longitudinal trend of change and spatial agglomeration characteristics of China's healthcare service efficiency.Results:China's overall healthcare service efficiency is growing,and the inter-regional gap is gradually narrowing,characterized by a concentration trend;the gap in the level of healthcare service efficiency between regions did not widen during the period under examination,but it was found that the gap within some regions was still significant.Conclusion:The national health service efficiency is growing slightly,and the regional gap is generally decreasing,but the Gini coefficient shows that the inter-regional contribution is still the main source of the gap.National health service efficiency is generally concentrated,but some regions are less efficient,with significant internal disparities.
5.The Spatial Differences and Dynamic Evolution of China's Healthcare Service Efficiency from 2012 to 2021
Sha-Sha SONG ; Lina SHAO ; Zhonghua SUO ; Jing WU ; Ying LANG
Chinese Health Economics 2024;43(9):70-74,96
Objective:To study the longitudinal trends and spatial clustering characteristics of healthcare service efficiency in China and in North,Northeast,East,Central,South,Southwest,and Northwest China.Methods:The Malmquist index model is used to measure China's healthcare service efficiency from 2012 to 2021,the Dagum Gini coefficient as well as the decomposition method are used to measure the magnitude and source of regional gaps in healthcare service efficiency,and the Kernel density estimation is used to study the longitudinal trend of change and spatial agglomeration characteristics of China's healthcare service efficiency.Results:China's overall healthcare service efficiency is growing,and the inter-regional gap is gradually narrowing,characterized by a concentration trend;the gap in the level of healthcare service efficiency between regions did not widen during the period under examination,but it was found that the gap within some regions was still significant.Conclusion:The national health service efficiency is growing slightly,and the regional gap is generally decreasing,but the Gini coefficient shows that the inter-regional contribution is still the main source of the gap.National health service efficiency is generally concentrated,but some regions are less efficient,with significant internal disparities.
6.The Spatial Differences and Dynamic Evolution of China's Healthcare Service Efficiency from 2012 to 2021
Sha-Sha SONG ; Lina SHAO ; Zhonghua SUO ; Jing WU ; Ying LANG
Chinese Health Economics 2024;43(9):70-74,96
Objective:To study the longitudinal trends and spatial clustering characteristics of healthcare service efficiency in China and in North,Northeast,East,Central,South,Southwest,and Northwest China.Methods:The Malmquist index model is used to measure China's healthcare service efficiency from 2012 to 2021,the Dagum Gini coefficient as well as the decomposition method are used to measure the magnitude and source of regional gaps in healthcare service efficiency,and the Kernel density estimation is used to study the longitudinal trend of change and spatial agglomeration characteristics of China's healthcare service efficiency.Results:China's overall healthcare service efficiency is growing,and the inter-regional gap is gradually narrowing,characterized by a concentration trend;the gap in the level of healthcare service efficiency between regions did not widen during the period under examination,but it was found that the gap within some regions was still significant.Conclusion:The national health service efficiency is growing slightly,and the regional gap is generally decreasing,but the Gini coefficient shows that the inter-regional contribution is still the main source of the gap.National health service efficiency is generally concentrated,but some regions are less efficient,with significant internal disparities.
7.The Spatial Differences and Dynamic Evolution of China's Healthcare Service Efficiency from 2012 to 2021
Sha-Sha SONG ; Lina SHAO ; Zhonghua SUO ; Jing WU ; Ying LANG
Chinese Health Economics 2024;43(9):70-74,96
Objective:To study the longitudinal trends and spatial clustering characteristics of healthcare service efficiency in China and in North,Northeast,East,Central,South,Southwest,and Northwest China.Methods:The Malmquist index model is used to measure China's healthcare service efficiency from 2012 to 2021,the Dagum Gini coefficient as well as the decomposition method are used to measure the magnitude and source of regional gaps in healthcare service efficiency,and the Kernel density estimation is used to study the longitudinal trend of change and spatial agglomeration characteristics of China's healthcare service efficiency.Results:China's overall healthcare service efficiency is growing,and the inter-regional gap is gradually narrowing,characterized by a concentration trend;the gap in the level of healthcare service efficiency between regions did not widen during the period under examination,but it was found that the gap within some regions was still significant.Conclusion:The national health service efficiency is growing slightly,and the regional gap is generally decreasing,but the Gini coefficient shows that the inter-regional contribution is still the main source of the gap.National health service efficiency is generally concentrated,but some regions are less efficient,with significant internal disparities.
8.The Spatial Differences and Dynamic Evolution of China's Healthcare Service Efficiency from 2012 to 2021
Sha-Sha SONG ; Lina SHAO ; Zhonghua SUO ; Jing WU ; Ying LANG
Chinese Health Economics 2024;43(9):70-74,96
Objective:To study the longitudinal trends and spatial clustering characteristics of healthcare service efficiency in China and in North,Northeast,East,Central,South,Southwest,and Northwest China.Methods:The Malmquist index model is used to measure China's healthcare service efficiency from 2012 to 2021,the Dagum Gini coefficient as well as the decomposition method are used to measure the magnitude and source of regional gaps in healthcare service efficiency,and the Kernel density estimation is used to study the longitudinal trend of change and spatial agglomeration characteristics of China's healthcare service efficiency.Results:China's overall healthcare service efficiency is growing,and the inter-regional gap is gradually narrowing,characterized by a concentration trend;the gap in the level of healthcare service efficiency between regions did not widen during the period under examination,but it was found that the gap within some regions was still significant.Conclusion:The national health service efficiency is growing slightly,and the regional gap is generally decreasing,but the Gini coefficient shows that the inter-regional contribution is still the main source of the gap.National health service efficiency is generally concentrated,but some regions are less efficient,with significant internal disparities.
9.The Spatial Differences and Dynamic Evolution of China's Healthcare Service Efficiency from 2012 to 2021
Sha-Sha SONG ; Lina SHAO ; Zhonghua SUO ; Jing WU ; Ying LANG
Chinese Health Economics 2024;43(9):70-74,96
Objective:To study the longitudinal trends and spatial clustering characteristics of healthcare service efficiency in China and in North,Northeast,East,Central,South,Southwest,and Northwest China.Methods:The Malmquist index model is used to measure China's healthcare service efficiency from 2012 to 2021,the Dagum Gini coefficient as well as the decomposition method are used to measure the magnitude and source of regional gaps in healthcare service efficiency,and the Kernel density estimation is used to study the longitudinal trend of change and spatial agglomeration characteristics of China's healthcare service efficiency.Results:China's overall healthcare service efficiency is growing,and the inter-regional gap is gradually narrowing,characterized by a concentration trend;the gap in the level of healthcare service efficiency between regions did not widen during the period under examination,but it was found that the gap within some regions was still significant.Conclusion:The national health service efficiency is growing slightly,and the regional gap is generally decreasing,but the Gini coefficient shows that the inter-regional contribution is still the main source of the gap.National health service efficiency is generally concentrated,but some regions are less efficient,with significant internal disparities.
10.The Spatial Differences and Dynamic Evolution of China's Healthcare Service Efficiency from 2012 to 2021
Sha-Sha SONG ; Lina SHAO ; Zhonghua SUO ; Jing WU ; Ying LANG
Chinese Health Economics 2024;43(9):70-74,96
Objective:To study the longitudinal trends and spatial clustering characteristics of healthcare service efficiency in China and in North,Northeast,East,Central,South,Southwest,and Northwest China.Methods:The Malmquist index model is used to measure China's healthcare service efficiency from 2012 to 2021,the Dagum Gini coefficient as well as the decomposition method are used to measure the magnitude and source of regional gaps in healthcare service efficiency,and the Kernel density estimation is used to study the longitudinal trend of change and spatial agglomeration characteristics of China's healthcare service efficiency.Results:China's overall healthcare service efficiency is growing,and the inter-regional gap is gradually narrowing,characterized by a concentration trend;the gap in the level of healthcare service efficiency between regions did not widen during the period under examination,but it was found that the gap within some regions was still significant.Conclusion:The national health service efficiency is growing slightly,and the regional gap is generally decreasing,but the Gini coefficient shows that the inter-regional contribution is still the main source of the gap.National health service efficiency is generally concentrated,but some regions are less efficient,with significant internal disparities.

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