Objective To analyze the changing trends and internal structural variations of the average inpatient medical expenses in public hospitals in Guangzhou,so as to provide data references for controlling the growth of medical expenses and op-timizing the structure of average inpatient medical expenses.Methods Data on the average inpatient medical expenses of patients in public hospitals in Guangzhou from 2012 to 2023 were collected.The gray correlation analysis was used to identify the main in-fluencing factors of the average inpatient medical expenses.The degree of structure structural variation method was employed to analyze the degree and direction of changes in various sub-expenses,as well as their structural contribution to the average inpa-tient medical expenses.Results The average inpatient medical expenses in public hospitals in Guangzhou showed a trend of in-creasing first and then decreasing from 2012 to 2023,with an average annual growth rate of 1.28％.Among the average inpatient medical expenses,drug expenses had the highest correlation degree(0.867),followed by hygiene material expenses(0.803),and these two items also had the largest structural contribution rates,accounting for 39.86％and 25.08％respectively.The structural changes of drug expenses,other expenses,bed charges,and treatment expenses were negative,while those of health materials,surgery,laboratory tests,inspections,nursing,and diagnosis expenses were positive.Conclusion The average medi-cal expenses in public hospitals have been effectively controlled.The cancellation of drug addition and centralized procurement have achieved remarkable results.However,the expense items reflecting the labor and technical value of medical staff have a rel-atively low impact on the total expenses.Therefore,it is necessary to further improve relevant policies and optimize the expense structure to better reflect the professional value and technical contribution of medical personnel.
average inpatient medical expensepublic hospitalgray correlation analysisstructural variation
维普
万方数据
