Models for extracting information on patient pathways

Abstract

In this paper, we present a random effects approach to modelling of patient flow. Individual patient experience in care as represented by their pathways through the system is modelled. An application to the University College of London Hospital (UCLH) neonatal unit is presented. Using the multinomial logit random effects model, we demonstrate a methodology to extract useful information on patient pathways. This modelling technique is useful for identifying interesting pathways such as those resulting in high probabilities of death/survival, and those resulting in short or long length of stay. Patient-specific discharge probabilities may also be predicted as a function of the frailties; which are modelled as random effects. In the current climate of healthcare cost concerns these will assist healthcare managers in their task of allocating resources to different departments or units of healthcare institution. Two classes of models are presented; one based on patient pathways in which different random effects distribution assumptions are made and the other in which the random effects are regressed on patient characteristics. Intuitively, with the introduction of individual patient frailties, we can argue that both clinical and operational patient flows are being captured in this modelling framework.