Simulation modeling for predicting hospital admissions and bed utilisation

Abstract

Demographic research of world population shows that societies are ageing. The ongoing changes in the population structure will require appropriate quantitative and qualitative adjustments in health services to meet the needs of society. Simulation methods turn out to be helpful in these kinds of analyses. In this paper, the authors present a case study on using discrete event simulation (DES) to support decision-making in the field of hospital bed management in the light of demographic changes. The case study was elaborated for one of the Polish district hospitals. A discrete simulation model was built to simulate admissions to two hospital wards: pediatric and geriatric. A series of experiments was carried out as based on real data extracted from the hospital database and forecasted demographic trends elaborated by The Central Statistical Office (CSO). The influence of demographic changes on hospital admissions in the chosen age-gender cohorts was explored, examining different variants of hospital bed availability. The results of the experiments show that demographic trends significantly influence healthcare admission and bed utilisation. The reduction in the number of admissions to the pediatric ward by about 6% results in a change in average bed utilisation from 57.90% to 54.06%. With about 12% more admissions to the geriatric ward, the change is from 68.88% to 75.59%.

Hospitalization forecast to inform COVID-19 pandemic planning and resource allocation using discrete event simulation

“…Simulation models have been widely used in research articles focusing on hospital bed management (Ahmad et al, 2014;Clissold et al, 2015;Hajlasz & Mielczarek, 2020;He et al, 2019;Helbig et al, 2015;Holm et al, 2013;Khanna et al, 2016;Landa et al, 2017;Mallor & Azcarate, 2014;Monks et al, 2016;Oliveira et al, 2020;Qin et al, 2017;Seymour et al, 2015;Varney et al, 2019). These computer-based tools mimic real-world systems and processes and are particularly useful for predicting and analyzing patient flow and resource utilization in hospital settings.…”

“…By analyzing historical bed occupancy data and using a weekly time horizon, the authors propose policies to reduce congestion and improve the quality of care. Hajlasz and Mielczarek (2020) conducted a case study using discrete event simulation to predict hospital admissions and bed utilization in a Polish district hospital. The study found that a 6% reduction in admissions to the pediatric ward resulted in a decrease in average bed utilization from 57.90% to 54.06%, while a 12% increase in admissions to the geriatric ward led to an increase in bed utilization from 68.88% to 75.59%.…”