Objectives To investigate the personnel allocation and workloads of the medical residents across the subspecialties of the Department of Internal Medicine at a tertiary hospital. Methods A cross-sectional survey was performed to investigate personnel allocation and workload. The resulting data were compared with the ministerial standard that regulates the training of medical residents. Results Aside from the subspecialty of Rheumatology, medical residents accounted for 40% to 70% of the total staff physicians. The faculty physicians accounted for only 20% to 50% of the total. When the non-faculty residents were not taken into account, each individual faculty physician took charge of between 5.3 to 15.5 beds across all the subspecialties. When only the non-faculty residents were accounted for, each individual resident took charge of 1.7 to 9.4 beds, 1.3 to 5.7 bed-days per day, and 5.8 to 17.3 patients per month. When both were accounted for, each physician was responsible for 1.3 to 5.9 beds, 1 to 3.6 bed-days per day, and 4.2 to 10.7 patients per month. In comparison with the ministerial standards, medical residents have managed more patients per month in the subspecialties of Nephrology, Respiratory Diseases, Digestive Diseases, Neurology and Infection.Fewer patients were managed in the subspecialty of Endocrinology. Conclusion The medical resident allocation is balanced across the subspecialties of the Department of Internal Medicine, although it is less stable. The total number of physicians is smaller than required, and physicians generally bear an overload of work. The number of patients managed by each individual resident is more than the requirement set by the ministerial standards, and has significant variations across subspecialties. Medical residents need to be allocated in accordance with the corresponding workloads.
ObjectiveTo investigate the demand of pediatric health service, the current situation of resource allocation and the equity of health service, so as to provide guidelines for optimizing the resource allocation of pediatric health service in Sichuan province.MethodsA questionnaire of all healthcare institutions with legal capability from a total of 183 prefectures in Sichuan province was performed in 2015. We described the demand of pediatric health service by two-week hospital visit rate, the proportion of no-visit rate within two-weeks, hospital admission rate, and the patient required hospitalization rate. We assessed current situation of resource allocation, equity and accessibility by analyzing Gini coefficient, Lorenz curve and thermodynamic diagram.ResultsThe demand of pediatric health service in Sichuan province was huge and the current resource allocation can be shown a " inverted triangle” form. According to population distribution, the Gini coefficients of physician, bed and equipment were 0.47, 0.40 and 0.49, respectively, which represented inequality in resource allocation. By location, the Gini coefficients of physician, bed and equipment were 0.82, 0.77 and 0.81, respectively, which indicated an absolutely unfair situation.ConclusionsThe health resources of pediatric in Sichuan province are limited, the distribution is unbalanced, and the supply of pediatric healthcare is not compatible with demand. The lack of resources and waste coexist simultaneously. Furthermore, the fairness of distribution in terms of geographical areas is far less than that in terms of population. The accessibility of superior health resources is low.
ObjectiveTo evaluate the equity of health care resource allocation in Shanghai and the changing trends from 1995 to 2018.MethodsBeing based on the Gini coefficient and the Theil index, the equity of health care resource allocation in Shanghai from 1995 to 2018 was comprehensively evaluated from the perspective of "demographic equity" and "geographic equity", and the Mann-Kendall non-parametric test was used to predict the trends of changes.ResultsThe Gini coefficient of the distribution of medical and health resources by population in Shanghai from 1995 to 2018 was 0.225 9 to 0.411 9, and the configuration was in a normal or optimal state with an increasing trend. The Gini coefficient distributed by geographic area was 0.892 4 to 0.979 3, which was in a disadvantaged state and a decreasing trend. The overall Theil index ranged from 0.010 9 to 0.058 1, which was a more equitable configuration, but with a decreasing trend. In addition, both the Gini coefficient and the Theil index showed that equity improvements were mainly influenced by the number of health facilities and beds, with health facilities contributing the most to equity, while the disparity in health technician staffs was the main reason for the decline in equity. Inequities in the allocation of health facilities and the number of beds originated mainly within regions, while inequities in the allocation of health technicians originated mainly between regions.ConclusionsThe allocation of health care resources in Shanghai is more equitable and the equity has been on the rise in recent years. However, at the present stage, there is still a contradiction between equitable allocation by population and inequitable allocation by geographic area, and in the future, there is a contradiction between the tendency of inequitable allocation by population and the tendency of equitable allocation by geographic area. Optimizing the allocation of health technicians is the key to improving equity, and addressing regional differences in allocation is an effective way to optimize the allocation of health technicians.
With the rapid development of artificial intelligence (AI) technology, its application in hospital management is gradually becoming an important means to improve operational efficiency and the quality of patient health care. This article systematically explores the multidimensional applications of AI in hospital management, including medical services, administration, patient engagement and experience. Through in-depth analysis, the paper evaluates the potential of AI in these areas, especially the significant effect in improving operational efficiency and optimising patient healthcare services. However, the application of AI also faces many challenges, such as data privacy issues, algorithmic bias, operational management, and economic factors. This article not only identifies these challenges, but also provides specific inspiration and recommendations for hospital management in China, emphasises the importance of adaptability and continuous learning, and calls on hospital administrators to actively embrace change in order to achieve both improved patient health outcomes and operational efficiency.