Characterising hospital admission patterns and length of stay in the emergency department at Mater Dei hospital

Abstract

Through the development of policies, it is possible for healthcare professionals and resource planners to ensure that the rare healthcare resources are used optimally at an acceptable cost of care. The daily resource requirements are affected by a patients' Length of Stay (LOS) and the number of patients admitted. These may depend on many factors, such as those representing the characteristics of a patient like; gender, age and locality, or the changes in the weather. This study uses Coxian phase-type distribution (C-PHD) to obtain results and generate Phase-Type Survival (PTS) trees to analyse groups of patients which a ffect the admission and LOS distribution, through the use of covariates such as admission date, gender, age, district and source of admissions. PTS trees for the same distributions were carried out to find the effect that the weather might have on these, using covariates such as date, maximum temperature, minimum temperature, average temperature and maximum variability in temperature. This study uses a two year data set (2011-2012) of patients admitted to the Emergency Department at Mater Dei Hospital to generate the models and an independent one year data set (2013) of patients admitted to the Emergency Department at Mater Dei Hospital to evaluate. The PTS tree eff ectively clusters patients based on their LOS considering prognostic signifi cance of di fferent covariates, those related to patient characteristics and those related to weather. Characterising the covariates related to patient characteristics and weather both provided successful results in relation to LOS. Similarly, the PTS tree was used to effectively cluster patients based on the admission rate considering prognostic significance of different covariates, those related to patient characteristics and those related to weather. Characterising admissions to patient characteristic covariates provided the most successful prognostic significance.