Analysis of Influencing Factors of Patients with Prolonged Hospital Stay in a Certain Hospital in Beijing
Objectives This study aims to explore the composition and influencing factors of hospitalization costs for patients with extended length of stay in a hospital in Beijing,in order to provide a reference for shortening the average length of stay and rational utilization of medical resources.Methods A statistical description was conducted on the distribution characteristics of ultra long hospital stay days among 1,154 discharged patients from a tertiary hospital from January 1 to December 31,2022.Logistic regression models were used to analyze the influencing factors.Results 1,154 patients were discharged due to prolonged hospital stay,accounting for 3.29％of the total.The average age of patients with prolonged hospital stay was 58.69±8.79 years old,ranging from 1 year old to 98 years old.Among patients with long hospital stays,neurosurgery and hematology had the highest proportion,accounting for 16.48％and 12.99％respectively.The cumulative proportion of departments in the top 10 long hospital stays was 77.03％.In the distribution of diseases,injuries,poisoning,and certain other consequences of external factors,as well as musculoskeletal and connective tissue diseases,accounted for the highest proportions at 24.09％and 17.16％,respectively.Regression analysis showed that the factors that affect prolonged hospitalization ware in order of severity,age,gender,disease outcome,and surgical grade.At the same time,the standardized partial regression coefficient showed that critical illness had the greatest impact on excessive hospital stays and the data were statistically significant(P＜0.05).Conclusions Excessive length of hospital stay can prolong the average length of hospital stay,resulting in wastage of medical resources and a shortage of social resources.Under the premise of strengthening two-way referral and fully implementing the core system,hospitals should effectively control the length of stay and the number of patients and improve the efficiency of medical operations.
Excessive length of hospital stayInfluencing factorsLogistic regression modelCountermeasure
NETL
NSTL
万方数据
