Modelling Hospital length of stay using convolutive mixtures distributions

TL;DR

This paper introduces a novel convolution-based statistical model for hospital length of stay, capturing skewness better than traditional distributions, with estimation methods and real data application.

Contribution

It proposes a new convolution model for LOS, combining patient recovery and hospital management factors, with two estimation procedures and real data validation.

Findings

Model effectively captures skewed LOS distribution.

Both estimation methods perform well on real data.

Provides a flexible approach for hospital stay analysis.

Abstract

Length of hospital stay (LOS) is an important indicator of the hospital activity and management of health care. The skewness in the distribution of LOS poses problems in statistical modelling because it fails to adequately follow the usual traditional distribution such as the log-normal distribution. The aim of this work is to model the variable LOS using the convolution of two distributions; a technique well known in the signal processing community. The specificity of that model is that the variable of interest is considered to be the resulting sum of two random variables with different distributions. One of the variables will feature the patient-related factors in terms their need to recover from their admission condition, while the other models the hospital management process such as the discharging process. Two estimation procedures are proposed. One is the classical maximum likelihood, while the other relates to the expectation maximisation algorithm. We will present some results obtained by applying this model to a set of real data from a group of hospitals in Victoria (Australia).