Analysis of Length of Stay (LOS) Data from the Medical Records of Tertiary Care Hospital in Saudi Arabia for Five Diagnosis Related Groups: Application of Cox Prediction Model

Background: One of\r\nthe main objectives of hospital managements is to control the length of stay\r\n(LOS). Successful control of LOS of inpatients will result in reduction in the\r\ncost of care, decrease in nosocomial infections, medication side effects, and better\r\nmanagement of the limited number of available patients’ beds. The length of\r\nstay (LOS) is an important indicator of the efficiency of hospital management\r\nby improving the quality of treatment, and increased hospital profit with more\r\nefficient bed management. The purpose of this study was to model the distribution\r\nof LOS as a function of patient’s age, and the Diagnosis Related Groups (DRG),\r\nbased on electronic medical records of a large tertiary care hospital. Materials\r\nand Methods: Information related to the research subjects were retrieved\r\nfrom a database of patients admitted to King Faisal Specialist Hospital and\r\nResearch Center hospital in Riyadh, Saudi Arabia between January 2014 and\r\nDecember 2016. Subjects’ confidential information was masked from the\r\ninvestigators. The data analyses were reported visually, descriptively, and\r\nanalytically using Cox proportional hazard regression model to predict the risk\r\nof long-stay when patients’ age and the DRG are considered as antecedent risk\r\nfactors. Results: Predicting the risk of long stay depends significantly\r\non the age at admission, and the DRG to which a patient belongs to. We\r\ndemonstrated the validity of the Cox regression model for the available data as\r\nthe proportionality assumption is shown to be satisfied. Two examples were\r\npresented to demonstrate the utility of the Cox model in this regard.