Objective:Exploring the association rules of factors influencing high hospitalization costs for cancer patients, providing references for hospitals to optimize medical cost management measures.Methods:In the inpatient case information system of a tertiary general hospital, the medical record homepages of inpatients in the DRG groups of the oncology department in 2022 were obtained. The upper four scores of hospitalization costs was used as the threshold for patient grouping. Patients with hospitalization costs≥this threshold were the high-cost group, while other patients were control group; 12 factors, including age, gender, and admission condition, etc, were considered as potential influencing factors of high hospitalization costs. FP-Growth and Apriori algorithms were used to excavate the potential association rules between the influencing factors of high hospitalization costs. Logistic regression was used to analyze the independent influencing factors of high hospitalization costs.Results:A total of 5 512 hospitalized patients were included, including 1 378 patients in the high-cost group. Thirteen validated strong association rules for factors influencing high hospitalization costs were obtained, of which the rule antecedents included age (≥70 years), number of days in hospital (≥7 days), other diagnoses (≥5), surgery, planned readmission, use of antibiotics, admission (general/critical), living admission score (61~99), level of care (level 1/level 2), non-day ward, criticality during hospitalisation. Logistic regression results showed that all nine influencing factors except gender, use of antibiotics, and readmission plans were independent influences on high hospitalization costs ( P<0.05). Conclusions:The joint application of FP-Growth and Apriori algorithm could effectively explore the association rules of high hospitalization costs for oncology patients. The early warning information mainly included the number of hospitalization days, the number of other diagnoses, surgeries, and so on. It was suggested that medical institutions can reasonably control the high hospitalization costs through clinical pathway management, diagnosis and treatment process reengineering, admission risk assessment, and multidisciplinary collaborative diagnosis and treatment strategies.

Objective:To analyze the influencing factors of China′s DRG payment system reform(DRG reform) and its hierarchical relationship, for references for the in-depth promotion of China′s medical insurance payment reform.Methods:Relevant literature on DRG reform in China from databases such as CNKI, Wanfang Database, Pubmed, etc, were obtained. Content analysis method was used to extract the influencing factors of DRG reform. The correlation between each influencing factor was determined through expert discussion. An interpretive structural model(ISM) was constructed to analyze the hierarchical relationship of factors influencing DRG reform.Results:After analysis, the influencing factors(12) of DRG reform in China were included such as medical level, hospital management, and medical staff′s cognition and behavior. Among them, the local situation was the deep-level factor affecting DRG reform, 9 factors such as data quality assurance and policy design/implementation were the middle-level factors, and patients′ interests/needs and disease grouping were the surface-level factors.Conclusions:There were many influencing factors on the reform of China′s DRG payment system. It was suggested that relevant management departments in various regions should focus on the actual situation of the locality, take data quality and policy design and implementation as the key points of reform, formulate a scientific and reasonable DRG grouping scheme, safeguard the interests of patients, so as to promote the deepening of DRG reform.

Objective To explore the influence of the coupling and coordination of DRG and clinical pathways on Antibiotics Use Density.Methods The data of 7 clinical departments in a sample hospital from 2021 to 2022 were selected,the data were collated through descriptive statistics,and the coupling coordination degree model was used to measure the coordinated development level of DRG and clinical pathways.Taking the per-average drug cost and the average hospital stay of patients as the threshold variables,the threshold regression model was used to calculate the threshold value of Antibiotics Use Density at the level of coordinated development.Results There was a single threshold(F=21.98,P<0.05)of 6334.69 and a double threshold(F=160.53,15.99,P<0.1)of 6.74 and 8.20 for the average length of stay of patients.The statistical values between the level of coordinated development and Antibiotics Use Density in the two threshold variables were positive.Conclusion There is a nonlinear relationship between the level of coordinated development of DRG and clinical pathway with Antibiotics Use Density,and there is a positive promotion effect,and there are differences in the impact size in different threshold variable intervals.Medical institutions should pay attention to the nonlinear relationship,make good use of hospital informatization,do a good job in reasonable monitoring,and reasonably promote the management of antimicrobial drugs.

The design of the salary structure in public hospitals is an important part of the salary system reform.The salary structure is in a dynamic evolutionary process as the reform deepens,and at this stage,it is gradually shifting towards a development direction that emphasizes incentive-based salary performance.Continuous optimization of the salary structure is key to exploring the accurate measurement of doctors' value and the sustainable development of the hospital's public welfare attributes.Through literature research and comparative analysis,it presented a tabular presentation of the four incentive compensation models formed so far in the dynamic optimization of China's public hospitals after the new healthcare reform,and to conduct a comparative analysis of their respective advantages and disadvantages,in order to provide reference and information for the continuous optimization of the pay structure of public hospitals.

Objective To explore the influence of the coupling and coordination of DRG and clinical pathways on Antibiotics Use Density.Methods The data of 7 clinical departments in a sample hospital from 2021 to 2022 were selected,the data were collated through descriptive statistics,and the coupling coordination degree model was used to measure the coordinated development level of DRG and clinical pathways.Taking the per-average drug cost and the average hospital stay of patients as the threshold variables,the threshold regression model was used to calculate the threshold value of Antibiotics Use Density at the level of coordinated development.Results There was a single threshold(F=21.98,P<0.05)of 6334.69 and a double threshold(F=160.53,15.99,P<0.1)of 6.74 and 8.20 for the average length of stay of patients.The statistical values between the level of coordinated development and Antibiotics Use Density in the two threshold variables were positive.Conclusion There is a nonlinear relationship between the level of coordinated development of DRG and clinical pathway with Antibiotics Use Density,and there is a positive promotion effect,and there are differences in the impact size in different threshold variable intervals.Medical institutions should pay attention to the nonlinear relationship,make good use of hospital informatization,do a good job in reasonable monitoring,and reasonably promote the management of antimicrobial drugs.

The design of the salary structure in public hospitals is an important part of the salary system reform.The salary structure is in a dynamic evolutionary process as the reform deepens,and at this stage,it is gradually shifting towards a development direction that emphasizes incentive-based salary performance.Continuous optimization of the salary structure is key to exploring the accurate measurement of doctors' value and the sustainable development of the hospital's public welfare attributes.Through literature research and comparative analysis,it presented a tabular presentation of the four incentive compensation models formed so far in the dynamic optimization of China's public hospitals after the new healthcare reform,and to conduct a comparative analysis of their respective advantages and disadvantages,in order to provide reference and information for the continuous optimization of the pay structure of public hospitals.

Objective:Exploring the association rules of factors influencing high hospitalization costs for cancer patients, providing references for hospitals to optimize medical cost management measures.Methods:In the inpatient case information system of a tertiary general hospital, the medical record homepages of inpatients in the DRG groups of the oncology department in 2022 were obtained. The upper four scores of hospitalization costs was used as the threshold for patient grouping. Patients with hospitalization costs≥this threshold were the high-cost group, while other patients were control group; 12 factors, including age, gender, and admission condition, etc, were considered as potential influencing factors of high hospitalization costs. FP-Growth and Apriori algorithms were used to excavate the potential association rules between the influencing factors of high hospitalization costs. Logistic regression was used to analyze the independent influencing factors of high hospitalization costs.Results:A total of 5 512 hospitalized patients were included, including 1 378 patients in the high-cost group. Thirteen validated strong association rules for factors influencing high hospitalization costs were obtained, of which the rule antecedents included age (≥70 years), number of days in hospital (≥7 days), other diagnoses (≥5), surgery, planned readmission, use of antibiotics, admission (general/critical), living admission score (61~99), level of care (level 1/level 2), non-day ward, criticality during hospitalisation. Logistic regression results showed that all nine influencing factors except gender, use of antibiotics, and readmission plans were independent influences on high hospitalization costs ( P<0.05). Conclusions:The joint application of FP-Growth and Apriori algorithm could effectively explore the association rules of high hospitalization costs for oncology patients. The early warning information mainly included the number of hospitalization days, the number of other diagnoses, surgeries, and so on. It was suggested that medical institutions can reasonably control the high hospitalization costs through clinical pathway management, diagnosis and treatment process reengineering, admission risk assessment, and multidisciplinary collaborative diagnosis and treatment strategies.

Objective:To analyze the influencing factors of China′s DRG payment system reform(DRG reform) and its hierarchical relationship, for references for the in-depth promotion of China′s medical insurance payment reform.Methods:Relevant literature on DRG reform in China from databases such as CNKI, Wanfang Database, Pubmed, etc, were obtained. Content analysis method was used to extract the influencing factors of DRG reform. The correlation between each influencing factor was determined through expert discussion. An interpretive structural model(ISM) was constructed to analyze the hierarchical relationship of factors influencing DRG reform.Results:After analysis, the influencing factors(12) of DRG reform in China were included such as medical level, hospital management, and medical staff′s cognition and behavior. Among them, the local situation was the deep-level factor affecting DRG reform, 9 factors such as data quality assurance and policy design/implementation were the middle-level factors, and patients′ interests/needs and disease grouping were the surface-level factors.Conclusions:There were many influencing factors on the reform of China′s DRG payment system. It was suggested that relevant management departments in various regions should focus on the actual situation of the locality, take data quality and policy design and implementation as the key points of reform, formulate a scientific and reasonable DRG grouping scheme, safeguard the interests of patients, so as to promote the deepening of DRG reform.

DRG payment reform is an important means to control the unreasonable growth of medical expenses, improve the quality of medical services and achieve a win-win situation among three sides of hospitals, medical insurance and patients. This study adopted the Smith policy implementation process model to analyze the difficulties in the DRG policy implementation process from four aspects(idealized policies, policy implementation institutions, target groups, and policy environment), including the deviation between policy connotations and actual needs; the interest objectives of all parties were not completely aligned, the target group lacked a sense of identity, and the social impact and technological support needed to be improved. It was suggested that optimization should be carried out from four dimensions: policy supply coordination and precision, performance evaluation and personnel literacy, target group cognitive level and participation willingness, and policy implementation environment and atmosphere, in order to synergistically promote the effective implementation of DRG policies.

Polyolefin plastics are a group of polymers with C-C backbone that have been widely used in various areas of daily life. Due to their stable chemical properties and poor biodegradability, polyolefin plastic waste continues to accumulate worldwide, causing serious environmental pollution and ecological crises. In recent years, biological degradation of polyolefin plastics has attracted considerable attention. The abundant microbial resources in the nature offer the possibility of biodegradation of polyolefin plastic waste, and microorganisms capable of degrading polyolefin have been reported. This review summarizes the research progress on the biodegradation microbial resources and the biodegradation mechanisms of polyolefin plastics, presents the current challenges in the biodegradation of polyolefin plastics, and provides an outlook on future research directions.
Plastics/metabolism* ; Polymers/metabolism* ; Polyenes ; Biodegradation, Environmental