A Study on the Factors Related to the Readmission and Ambulatory Visit in an University Hospital: Using Patient Care Information DB / 대한의료정보학회지

To identify the factors related to the readmission and ambulatory visit we analyzed the data in discharge abstract DB(DADB) and outpatient database(OPDB) for 19,983 patients discharged in 1990 from an university hospital(S Hospital) in Seoul. The target patients were limited to those who didn' t have previous episode of discharge in that hospital. Readmission data for 10 years(1990-1999) and ambulatory visit data for 5 years(1995-1999) were analyzed by using x2 test and multiple logistic regression analysis. The main results of this study is as follows. 1) As the number of readmission was increased, readmission rate(RR) was also increased while the average length of stay(ALOS) was decreased. 2) RR was higher in male, transferred from other health care facilities, with consultation, biopsy, ICU care episode during hospitalization. 3) In logistic regression, RR of patients living close to S Hospital hospital was higher than the others wh?n other variables were adjusted. 4) RR of the patients with the diagnosis(Dx) of cancer or cancer related condition was the highest(47.6%), and the consistency rate (CR) of principal Dx group with that of previous admission was also the highest in cancer patients. As the number of readmission was increased the CR of Dx group was also increased. 5) 23.4%(4866) of the target patients had episode of visiting outpatient dispensary(OPD) for between 1995-1999 and the average number of visit was 13.6 times. Patients with the Dx of heart disease showed the highest proportion in ambulatory visit.

Application of Survival Analysis to Data from Discharge Abstract of Medical Record Department: Focused on Readniission / 대한의료정보학회지

Abundant data on patients have been accumulated in hospital since the introduction of the computerized system. Now data mining is required for the survival and growth of hospital. Cases of 19,558 patients were analyzed to investigate factors influencing readmission and repeated admissions, and to estimate probability of readmission with considering covariate effects. Techniques of Kaplan-Meier method, Cox proportional hazards model, and WLW method were applied to the analysis. The conclusions are as follows. The severity of disease, congenital defect and chronicity of disease are influencing readmission or repeated admissions of a patient. Patient s characteristics, such as gender, distance from residence and type of discharge are also related to them. The probability of readmission can be estimated for a patient with variety of conditions for certain period of time. It is suggestive that survival analysis is a good methodology for data mining works on computerized data in hospital. If death certificate data are connected with patients' data, we will be able to get a good data source to medical studies.