Opportunities for Simulation-Based Optimization in a Hospital Environment: Results and Perspectives From a Review of the Literature

Introduction

In healthcare management, one important problem is how to efficiently allocate resources with limited capacities. In health organizations, such as hospitals, it is necessary to observe the processes to understand if and where there are inefficiencies and, in this case, to delineate strategies to improve them. The appropriate allocation of resources within any hospital ward, positively affects the quality of the service (Van De Klundert et al., 2008). From the point of view of a central decision maker with a long planning horizon, many healthcare resources such as appointment slots, operation rooms, and hospital beds can be regarded as reusable resources. Operating rooms, in particular, consume a large amount of human, material and facility resources in many hospitals: about 40% of a hospital's total expenses can be attributed to the operating rooms (Denton et al., 2007). At the same time, however, operating rooms contribute to most of the revenues of many hospitals. Therefore, how to maintain a high quality of health care by trying to reduce the operating cost to the minimum becomes one of the most challenging problems for hospitals. In addition, it is of paramount importance to ensure the correct scheduling of times within the operating rooms (Van De Klundert et al., 2008).

Hospitals are beginning to understand that process optimization and reengineering can offer potentials for gaining a competitive advantage. Just as many companies successfully reduce costs and gain competitive advantage by redesigning their business processes, hospitals can redesign the way certain healthcare processes are performed to achieve efficiency and cost containment. Operating rooms are a vital component of the hospital context and, therefore, it is essential to improve the flow of patients, then optimizing their management in order to both provide timely treatments and maximize the use of available resources.

Simulation techniques include a set of tools whose general aim is to help decision-makers and researchers to reproduce an existing system and evaluate how alternative configurations of that system affect the operational performance (Lagergren, 1998). Redesigned scenarios, in turn, allow for better understanding the behavior of that system for a given set of conditions (Kelton, Sadowski, & Sturrock, 2008). This is why simulation is one of the most widely used operations research tools to identify potential areas of improvement, and as such, it has been applied in several different contexts, including healthcare (Brailsford, Harper, Patel, & Pitt, 2009). In this sector, simulation has been used by researchers with various aims, including hospital bed planning (Holm, Lurås, & Dahl, 2013), critical care (Mallor & Azcárate, 2011), emergency room settings (Abo-Hamad & Arisha, 2013) or patient flow analysis (Wang, Guinet, Belaidi, & Besombes, 2009). The recent coronavirus disease (commonly known as COVID-19) has represented a further push towards the usage of simulation, because of the unexpected increase in the number of patients to be treated with the same set of available resources (Tavakoli et al., 2022; Caro et al., 2021). As a second factor, modern information technologies, such as machine learning or artificial intelligence, or the tools included in the well-known Industry 4.0 paradigm (e.g., wireless sensor networks and Internet of Things), have undergone a significant expansion in their potential application fields, including healthcare (Sahoo et al., 2016). These tools can obviously help in collecting more data, elaborating them, and ultimately enabling the creation of even more precise and realistic simulation models, with further enhancement of their usage for process management.