Analysis on the Influencing Factors of Hospitalization for more than 30 Days for a Patient with Orthopedic Surgery
Objectives To analyze the influencing factors of more than 30 days in hospital for a patient with orthopedic surgery,and explore the measures to shorten the average days in hospital.Methods A retrospective study was conducted using Excel 16.0 and SPSS 26.0,a statistical description of case information of 745 orthopedic patients who underwent orthopaedic surgery with a discharge date of more than 30 days from January 1st,2018,to December 31st,2022 was performed,was made on the basis of a literature review and a questionnaire survey,chi-square test and Logistic regression analysis were used to study the influencing factors of orthopaedic surgery patients with over 30 days hospitalization.Results From 2018 to 2022,there were 25041 orthopedic patients,of whom 745(2.98％)were hospitalized for more than 30 days.The ratio of male to female was 1.48∶1,and the age of 41～60 was the most(39.2％),the second was 61～80 years old(35.4％).With the increase of years,the proportion of patients with over 30 days hospitalization showed a decreasing trend(Z=61.285,P＜0.001).The results of binary Logistic stepwise regression analysis showed that male,age,patients in county,emergency admission,transfer,waiting days 0 days and over3 days before operation,discharge diagnosis more than 3 ones,operation treatment fee more than 3000 yuan,tibia fracture,femoral fracture,calcaneal fracture,lumbar disc protrusion were the risk factors of over 30 days in hospital.Conclusions The hospital should strengthen the management of operation,shorten the waiting time before operation,strengthen the communication between doctors and patients,perfect the system and procedure of referral,optimize the diagnosis and treatment plan of diseases,and do the two-way referral well,the establishment of more than 30 days inpatient early warning and reporting system,effectively shorten the average length of stay.
Orthopedic surgery patientsOver 30 days in hospitalLogistic regressionInfluencing factors
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