Discrete Conditional Survival Models for trolley waiting times in Accident and Emergency

Health care managers have a huge responsibility to appropriately allocate the limited resources available. As a result focus is on intelligent patient management models using approaches from operational research and statistics. This paper wishes to consider one such approach for modeling an Accident and Emergency (A&E) department in a UK hospital. The main objective is to develop a model that can accurately predict which patients will experience a `trolley wait´ from the time the doctor decides they should be admitted to hospital until the time they are allocated a hospital bed. The Discrete Conditional Survival Model is developed using Nai¿ve Bayes Classification to predict the patient admission and a survival model represented by a lognormal distribution to model the trolley waiting time. Based on patient information available on arrival to hospital, the model can predict the patients likely to experience trolley waits and plan ahead to prevent such cases happening.