Hospital Event Simulation Model: Arrivals to Discharge

Abstract

Many Australian public hospitals operate under strict resource constraints. Arguably, this is manifested in higher incidence of ambulance ramping and patient flow congestion episodes, which has led to an increase in public complaints and, possibly, sub-optimal health outcomes for patients. Consequently, there is a well accepted need to make best use of all available information and domain knowledge to ensure that hospital resources and expertise are utilised more efficiently, for the benefit of patients. The latter is not a simple task since hospital operations involve complex interactions among many groups of health professionals utilising limited physical facilities and equipment. This is further complicated by the inherent variability of patient responses to treatments. Indeed, the stochastic nature of the demand process, as well as uncertainty in durations of medical treatments and patient recovery, lead to probabilistically distributed bed availability. Fortunately, in Australia, hospitals are ”data rich” in the sense that reliable records of patient journeys have been kept for many years. While older data may reflect procedures and priorities that are no longer in place, data from recent years may be regarded as quite robust, especially in cities that have not experienced major demographic changes. Thus there is an opportunity to apply modern tools of mathematical, statistical and simulation modelling to enhance our understanding of key processes that influence a hospital’s operations. The understanding so obtained can then be used to assist hospital staff in devising operational procedures that are likely to minimise disruption without adversely impacting the public service provided to the patient population. In this paper we outline the Hospital Event Simulation Model: Arrivals to Discharge (HESMAD) to describe the patterns of patient flows within the Flinders Medical Centre, an urban teaching hospital. The logical design of HESMAD was developed through extensive consultation with colleagues from the hospital. In particular, patients within HESMAD are not modelled as identical entities, rather, they are assigned different attribute values such as mode of arrival, triage category and division to reflect the typical profile of all patients. Patients go through a set of physical units and process modules that model various physical areas, processes, interactions and behaviours within the hospital to replicate a wide spectrum of patient journeys. Hospital and patient data from 2012 to 2013 were used to fit various probability distributions, for instance the waiting times for treatment or discharges. The model allows for a realistic representation of patient flows, at a level of resolution that was deemed appropriate by the hospitals data management experts. The model has been validated against historical data and through consultation with health care and hospital experts. Within space limitation we provide an outline and a brief discussion of HESMAD’s structure, features, capabilities, design decisions and development. In addition, we provide a brief case study demonstrating the potential applicability of HESMAD for ’what if’ analyses of hospital interventions. While all discussions are specific to the Flinders Medical Centre, the methodology used within HESMAD is generic enough to apply to other public hospitals in Australia.