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