Advanced Solutions for Healthcare Facility Management

Advanced Solutions for Healthcare Facility Management

Francesco Longo (DIMEG, University of Calabria, Cosenza, Italy), Letizia Nicoletti (DIMEG, University of Calabria, Cosenza, Italy), Alessandro Chiurco (DIMEG, University of Calabria, Cosenza, Italy) and Antonio Calogero (DIMEG, University of Calabria, Cosenza, Italy)
DOI: 10.4018/ijphim.2014070103
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Abstract

This paper presents a simulation study carried out within a private healthcare facility with the aim of understanding whether or not it is able to handle a greater flow of incoming patients as well as the related impact on the overall efficiency. As a result, the simulation outcomes have pointed out the need for an internal work re-organization that has been devised through Lean Management tools and methodologies. The simulation model has, then, been used to predict the intended changes effects as well as their feasibility. Particular attention has been paid on the care administration process, provided that research activities are still ongoing to investigate other processes in the patient value chain where there is still substantial room for improvement. The proposed research work is grounded on an in dept analysis of the main processes and activities taking place in the healthcare facility as a starting point for the simulation model development. Afterwards, simulation has been used for “as-is” analyses and, in combination with Lean Management approaches, for “what-if” studies whose results and findings are discussed.
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1. Introduction

Nowadays most of the healthcare facilities are suffering the pressure of higher service demand rates, experiencing – at the same time – a strong reduction in resources availability in contrast with the need to reduce healthcare costs while keeping even higher patients service levels (Jun and Jacobson, 1999; Jacbson et al. 2006). In addition, as a common practice in many hospitals and healthcare facilities worldwide, subject matter experts (head physicians) are also in charge of administrative issues and as a result less time is spent on patients’ care due to administrative burdens (Swisher et al. 2001).

In this framework, over the past few years, Modeling & Simulation (in particular discrete event simulation) has been increasingly used as a mitigation tool to overcome such difficulties. The possibility to recreate processes, activities, procedures and their inherent complexity, indeed, paves the way for new support tools in decision making and operational processes (Bruzzone et al., 2013).

As a matter of facts, a quick review of the existing literature in this area shows that, since 60’s, simulation has established as an effective tool for problem solving in the healthcare domain (England and Roberts, 1978). Moreover, over the past 40 years, the literature is plenty of surveys that testify how simulation has become increasingly pervasive in the healthcare domain. Meaningful examples can be found in England and Roberts (1978), Smith-Daniels et al. (1988), Lehaney and Hlupic (1995), Jun et al. (1999), Flagle (2002) as a proof that simulation is widely accepted as problem solving methodology and decision support tool in healthcare facilities. However, using simulation in healthcare management may entail a great deal of complexity due to the modeling effort required to capture and recreate the system behavior as well as the main roles involved in it (i.e. head physicians, professional nurses, healthcare workers, administrative personnel, etc.). In addition, the lack of computational capabilities (above all in the early years), the lack of funding, the excessive costs of simulation software and the difficulty in using it, are factors that could prevent simulation from being successfully applied. Aside from enabling factors when simulation is applied to healthcare facilities, interesting classification frameworks, based on the goals the simulation study is meant for (such as hospital scheduling and organization, communicable disease, screening, costs of illness and economic evaluation), can be found in Fone et al. (2003), Brailsford et al. (2009), Gunal and Pidd, (2010) and many others. Different examples of simulation applications in health care can be found in Holm et al. (2013), Weerawat et al. (2013), Wang et al. (2012), Bruzzone et al. (2011-a), Bruzzone et al. (2011-b), Diaz et al (2013, 2012-a, 2012-b), Brandeau et al. (2004), Winkler et al., (2011).

As pointed out by Gunal and Pidd (2010), the attempts to model entire hospitals or healthcare facilities are limited (mainly because of the complexity of such systems) while there are many applications that consider self-contained parts.

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