The Use of Discrete Event Simulation in Hospital

International Conference on Advanced Logistics and Transport

The Use of Discrete Event Simulation in Hospital Supply Chain Management Amira Kammoun, Taicir Loukil

Wafik Hachicha

Unit of Logistic, Industrial and Quality Management (LOGIQ), Higher Institute of Industrial Management Sfax, Tunisia [email protected]

Laboratory of Mechanic, Modeling and Production (L2MP), Engineering School of Sfax, Sfax, Tunisia [email protected]

Abstract–A major challenge facing hospitals is to provide efficient medical services. Healthcare supply chain management (HSCM) using Discrete Event Simulation (DES) has received in literature considerable attention for more than two decades. Despite the widespread literature on this topic, efforts to review and analyze previous studies are very limited. For this reason, this paper proposes a new conceptual classification scheme to classify current developments of the use of the DES in Healthcare Management research. More than 30 papers published studies within 2003-2013 were classified and analyzed. This proposed conceptual classification scheme is based on the healthcare facilities application, the type of decision making (strategic, tactical and operational), and the application category. It could be useful for researchers as a starting point to facilitate further improvement in the HSCM field because it provides a faster access and understanding of the relevant literature. This paper offers a comprehensive reference work, which groups the publications into the application fields and gives brief summary on the background, application and methodology applied for each reviewed article. Keywords—discrete event simulation; healthcare management; classification schema; literature review

I.

INTRODUCTION

Over the years, healthcare requirements have grown and healthcare organizations have become larger, more complex and costly. The inherent uncertainty of these complicates decision-making processes. The application of computer models has been a very successful method to problem solving. According to many surveys [1] [2] and [3], the discrete-event simulation has been the most used technique in the healthcare management field. Discrete Event Simulation (DES) is considered like one of many different tools and methods used in the analysis and improvement of health-care systems. It is particularly well suited to tackling problems in healthcare where, resources are scarce and patients arrive at irregular times (for example, in accident and emergency (A&E) departments). Some of the applications of DES are therefore to forecast the impact of changes in patient flow, to examine resource needs (either in physical capacity of beds and equipment or in staffing), to manage patient scheduling and admissions or to investigate the complex relationships among the different model variables (for

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example, rate of arrivals or time spent in the system). DES therefore allows decision makers to effectively assess the efficiency of existing healthcare delivery systems, to improve system performance or design, and to plan new ones [4]. It can be observed that the publication rate of simulation papers relating to the health care sector has doubled within the last decade [5]. Numerous papers have been published in the literature combining organizational problems in healthcare systems and DES, as can be seen in the following review papers; J.B. Jun et al. surveys approximately a 30 years period and classifies models according to the objectives of the studies of which they are part. The review is in three parts: Scheduling and patient flow; Sizing and planning of beds, rooms, and staff; and, finally a discussion on future research areas [3]. D. Fone et al. [6], present a systematic review of the literature related to the use of simulation modeling in healthcare and covers almost the same period, 1980-1999, as J.B. Jun et al. This review aimed to assess the quality of published studies and to consider their influence on policy, rather than on operations. They divided the published work into five categories: hospital scheduling and organization, infection and communicable disease, costs of illness and economic evaluation, screening and, finally, miscellaneous. Another paper presented in which the authors provides an overview of discrete-event simulation modeling applications to health care clinics and integrated health care systems (e.g. hospitals, outpatient clinics, emergency departments, and pharmacies) over the past forty years. The paper builds upon the Jun et al. survey and provides new updates that have been reported since 1999 [7]. M. Thorwarth and A. Arisha presents in their paper a comprehensive review of process simulation applications in healthcare areas, which summarizes projects applied in health care facilities like hospitals, emergency departments, intensive care units, surgical procedures, outpatient clinics, and facilities allocated in the health care supply chain. The authors focused to the papers published from 1995 to 2008 [5]. The most recent reviews of DES papers published between 2000 and 2009, is presented by MM. Gunal and M. Pidd with an interest in performance modeling within hospitals. They classify papers according to the areas of application evident in

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the literature and focus in DES modeling of patient flow through hospital facilities. The authors highlighted the extent to which DES models are used for real decisions is rarely discussed and that stake-holders need to be convinced of the benefits [8]. It may be noted that there is an absence of recent paper that present a review of DES application in hospital supply chain management (HSCM) and provide a conceptual classification scheme (CCS) for reviewing articles. The hospital supply chain defined as "a set of processes that exchange of physical flows and financial information in order to ensure all the necessary conditions to provide a better service to the patient. These processes range from design to ensure product in care units through planning, supply management and transport of goods and patient from admission to leave." [9] In this general frame this work offers a selection of references which is interested in discrete event simulation modeling application in hospital supply chain and mapped onto a proposed conceptual classification scheme (CCS). For this aim, the current paper can be organized as follow. After introduction, section 2 presents proposed conceptual classification schema. Section 3 considered for articles classification. Section 4 focuses in the classification results. And we finish by the conclusion in section 5. II.

PROPOSED CONCEPTUAL CLASSIFICATION SCHEME

Two selection criteria are used to select and accept the articles. If the articles do not meet one of the two criteria, then they are excluded. First the articles are found via computerized search of the topic areas. The search is narrowed using the following terms: hospital supply chain, healthcare system, discrete event simulation, and simulation model. And second the research covers a period of eleven years; from 2003 to 2013. The main objective of this paper is to present a reference paper for the recent works treating the DES application in hospital supply chain management (HSCM). Three questions are proposed to classify the researches and some response can be proposed: 1) What are the different Healthcare application fields? • Emergency department, Hospital, Blood Supply chain, Surgery department, Outpatient Clinical, Impatient center, Pharmacy, Other unit. 2) What are the different levels of decisions in healthcare supply chain? And what are the major problems resolved by DES method? • Tactical (Deployment problem, Staff scheduling) • Operational (Waiting time problem, Length of stay problem, Appointment problem) • Strategic (Healthcare Performance analysis, Dimensioning of resources) 3) What are the different categories of application? • Design a new unit • Improvement

In the first time the papers are grouped in this section according to the problems founded in HSCM as Resources Planning, Waiting Time and Appointment, Performance Measure and Improvement. This classification was proposed according to the analysis of the different articles reviewed, which are published during the last eleven years. Then in section 3 all reviewed articles will be mapped onto the proposed conceptual classification scheme (CCS). A. Ressource Planning Health care planning is often used to determine the size and layout of health care facility. R. Akkerman and M. Knip show that the number of beds could be reduced in a cardiac surgery center if recovering patients are transferred once they no longer require the center's specialized care services [10]. A generic model was presented to generate an optimal number of beds in a unit, illustrating the balance between transfers, “refused” unscheduled admissions and unoccupied beds, i.e. the department must not overflow, nor remain too empty[11]. In other work proposed the simulation model was used as a tool for strategic capacity planning for an outpatient physical therapy clinic in Taipei, Taiwan. The authors constructed a discrete-event simulation model to study the dynamics of patient mixes with realistic treatment plans, and to estimate the practical capacity of the physical therapy room [12]. Some of the applications of DES therefore relate to managing patient admissions and staff scheduling, for example DES studies that compared the ‘individual surgeons’ strategy with the ‘pooled lists’ strategy for scheduling outpatient clinical appointments in surgical care [13]. M. Centeno et al. describe a model that combines Linear Programming (LP) with DES to reduce staffing costs in an ED. They define generic patient flows and service time distributions for nurses and doctors at each process [14]. Interarrival times of patients are estimated by time of day (TOD), and optimal resources and shift patterns are generated using LP for different demands. Also a DES model was discussed of an ED in Japan. Coverage includes A&E processes, plus surgery. Patients are grouped by type (ambulance, walk-ins) with assigned routes. Resources include clerks, treatment cubicles, medical staff, nurses and diagnostic rooms [15]. The modeled outcomes are “congestion factor” and total patient time under different scenarios (e.g. staffing, beds etc). B. Wainting Time and Appointment The discrete-event simulation was used of an emergency department of a large hospital in the southeast United States to show that significant process changes would be required to meet specified goals for patient length of stay [16]. And other authors describe how discrete-event simulation was used by the Cooper University Hospital to reduce patient length of stays in their emergency department. Their study determined that length of stay was a process related problem rather than resource dependent [17]. Another discrete-event simulation study of patient flow to reduce emergency department length of stay is presented [18]. This study discusses a DES model of an ED and “Medical Telemetry” unit (like a medical admissions unit) in Boston, US. The objective was to reduce ED patient time, including

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waits for beds. Patients are categorized by arrival time, type (walk-ins, ambulance, direct) and urgency. Processes/ resources were diagnostics, staff (doctors, nurses, and healthcare assistants), patient transport, cleaning, rooms, beds, other hospital transfers (in telemetry unit). Outputs were patient time by process and total, queue length by process, and utilization of staff, rooms and beds. Martin E. et al. present additional patient flow simulation studies that seek to increase patient throughput and decrease patient length of stay [19]. The discrete event simulation was used to analyse the waiting lists for surgery in Nova Scotia, Canada [20]. The authors developed a model for capacity planning decisions and analysed performance measures for enhancement as well as embellishment. R. B. Van der Meer et al. covers the phases an elective patient passes through, though only for a single specialty, orthopedics. The objective in this study was to reduce elective patients’ waiting times [21]. Although their model is very detailed and specific to the hospital studied, the use of DES is suggested as a good communication tool between the stakeholders and modelers. T. R. Rohleder et al. use discrete event simulation modeling in order to study and improve patient flow at an outpatient orthopedic clinic in Calgary (Canada). The improvements are a reduction in waiting time and congestion in the clinic, thus lowering patient dissatisfaction and improving staff morale [22]. The main purpose of the paper of E. Hamidreza et al. is to introduce a new framework to more efficiently investigate the patient flow of the Emergency Department (ED) of a governmental hospital in Tehran, Iran, in order to find out improving scenarios for reducing waiting times of patients [23]. Also a DES study of an ED in Moncton (Canada) was described to reduce patient waiting times and to improve overall service delivery and system throughput. The alternative with one physician and nurse added gave the best improvement level for waiting time [24]. S. Mahapatra et al. discuss a DES model of an American ED. The objective was to reduce patient time using a fast-track centre [25]. C. Performance Measure and Improvement Discrete event simulation is widely used to model healthcare systems with a view to their improvement. Most applications focus on discrete aspects of healthcare, such as accident and emergency rooms or outpatient clinics. However, despite this success with simulation at an operational level, there are no reported uses of discrete event simulation for the development and improvement of health policy. MM. Gunal and M. Pidd describe the development of such a policyoriented model, aimed at improving performance assessment in the UK National Health Service [26]. Similarly, another paper seeks to optimize the management and financial performance of ambulatory care clinics used for teaching medical students. Here they use discrete-event simulation to determine that a teaching ambulatory care clinic runs optimally (where optimality is defined as the policy that minimizes patient flow time and wait time while maximizing revenue) when the trainee-to-preceptor ratio is between 3 and 7 to 1 [27].

A DES model was described at an ED in the US which required a new build to cope with extra demand. All A&E processes, clinical decision and admissions units are modeled [28]. An Ambulance and Emergency DES model was described in England to understand the drivers of patient time (average and variability). Scope included the Medical Admissions Unit and diagnostics; A&E doctors and nurses were modeled, TOD was modeled, process times were generated through observation, plus computerized data. Validation was through demonstration to key experts and comparison with Key Performance Indicators (KPI’s). Scenarios included adding cubicles or staff, and improved admission processes by using discrete-event simulation. In this paper the focus is on logistical operations, especially on support service logistics, which is, in many cases, considered as secondary operation in hospital environment [29]. Another attempt to imply the patient and staff satisfaction is undertaken at a Medical Assessment Unit (MAU) of a general hospital within the UK [30]. In the first step the simulation model is used to eliminate bottlenecks, while the second step considers the patient and staff satisfaction by setting a metric scale in the goal programming method. D. Sundaramoorthi et al. developed a simulation approach to evaluate nurse–patient assignments. They used real data sets from a hospital in Texas to demonstrate their results [31]. A simulation model was proposed of a real sterilization service. The aims to study the impact of smoothing the arrival of medical devices at the sterilization service on the performance of the system. The authors show that modifying this arrival process significantly improves the performance of the system [32]. In order to use the scarce resource of blood more efficiently, a DES was used as a tool for increasing efficiency in blood supply chains. The overall aim of the research presented is to improve blood supply chain management in order to use the scarce resource of blood more efficiently [33]. C. Kamp et al. study case scenarios regarding the availability of blood products in the event of an influenza epidemic or comparable pandemic event [34]. T. Ruohonen et al. present a case study where hospital’s material logistics is evaluated, analyzed and improved by using discrete-event simulation. In this paper the focus is on logistical operations, especially on support service logistics, which is, in many cases, considered as secondary operation in hospital environment [35]. A simulation model of the central sterilization department (CSD) of a large surgical suite illustrates how simulation can be used to answer important system design and operation questions [36]. The article mentioned how simulation can be used to help managers make good decisions in CSD design, staffing, and equipment specification. A generic model of a sterilization service was proposed. The model can be used to improve the performance of a specific sterilization service and/or to dimension its resources [37]. Another paper presents a discrete event simulation study of the hospital pharmacy for outpatients at two London Hospitals [38]. M. Gourgand et al. studied several organization scenarios of stocks and restocking of the

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pharmacy area [39]. This organization should make it possible to provide pharmaceutical products under optimum conditions of quality and safety. L.Y. Meng and T. Spedding described a case study of a DES model of an Accident and Emergency Unit in a hospital in the UK. The scenario analysis illustrates that significant reductions in the waiting time of patients can be obtained by a reduction of waiting time for the consultant as well as an increase in the number of trolley beds [40]. In other work the simulation was integrated with optimization to design a decision support tool for the operation of an ED at governmental hospital in Kuwait [41]. The study of Kh. Belkadi and A. Tanguy focuses on the functional exploration of the ophthalmology service and mainly on the simulation of units for consultation, examination and care. The objective is to increase its quality of service [42]. III.

ARTICLES CLASSIFICATION

Before showing the survey results according to the five categories mentioned in section II, the conceptual classification scheme (CCS), which is considered the main contribution of this survey, is defined and emphasized. The classification scheme, as shown in Table I, was set up during the analysis of the 33 articles, which was performed progressively until completion of the final version of this paper. Indeed, this survey uses this classification for classifying studies which use DES in Healthcare Management. TABLE I.

CONCEPTUAL CLASSIFICATION SCHEMA

Category

Healthcare application fields (HAF)

Long term decision (LTD) Mid-term decision (MTD)

Short term decision (STD)

Application category (AC)

Subcategory

Code

publication in order to determine the application of discrete event simulation in hospital supply chain during the last eleven years. TABLE II.

Ref.

HF A

LTD HPA

MTD DR

DP

SS

STD WT

LSP

AC AP

×

[28]

F1

[17], [19] [25]

F1

[27]

F8

×

[16], [18]

F1

×

[14]

F1

×

[15]

F1

×

[10]

F4

×

[29]

F1

×

[21]

F8

[11]

F6

[26]

F2

[33]

F3

[32]

F8

×

D

×

×

×

×

×

×

×

×

× ×

×

× ×

× ×

×

×

×

× ×

F1

Hospital

F2

[20]

F4

×

Blood Supply chain

F3

[24]

F1

Surgery department Outpatient Clinical Appointments Impatient center

F4

×

F5

[13]

F5

[39]

F7

Pharmacy

F7

Other unit

F8

[36]

F8

I

×

×

Emergency department

F6

INDEXING OF REVIEWED ARTICLE

× × ×

×

×

× ×

×

×

×

×

×

×

×

×

Healthcare Performance analysis

HPA

[40]

F1

Dimensioning of resources

DR

[41]

F1

Deployment problem

DP

Staff scheduling

SS

[30]

F8

Waiting time problem

WT

[31]

F2

Length of stay problem

LSP

Appointment problem

AP

[35] [42]

F8

×

×

Design new unit

D

[34]

F3

×

×

Improvement

I

[38]

F7

×

×

[22]

F5

×

[23]

F1

[12]

F5

[37]

F8

In this section, all reviewed articles are mapped onto the developed conceptual classification scheme CCS in Table II. The presence of a cross indicates that the article on the leftmost column of the row belongs to the group associated with the column as defined in the classification and listed in Table I. The papers are classified in Table II according to the year of

×

×

×

× ×

×

×

×

× ×

×

×

×

×

×

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IV.

CLASSIFICATION RESULTS

The reviewing articles presented are classified according to the developed conceptual classification scheme. This section presents a discussion on the survey findings about each group defined in the proposed classification scheme.

Fig 4. Distribution of articles according to the type of problems treated at Short terme

The figures 2,3 and 4 present the different problems treated in hospital supply chain and show that the most reviewed articles treated the problem of Performance Analysis (HPA) in healthcare management and Length of stay problem (LSP) keep the second place. Fig 1. Distribution of articles according to the type of healthcare application fields.

As shown in the figure 1, the largest number (39.4 %) of the articles is applicated DES mostly in Emergency Departement (F1) then in other hospital services.

Fig 5.

Distribution of articles according to the application categories.

As indicated in figure 5 the aim objective of the largest number of reviewed articles is to improve the existing healthcare system (87.9%). Based on the above literature analysis, it seems that the DES is most applied for improving the hospital supply chain and especially in the Emergency Department (ED). Fig 2. Distribution of articles according to the type of problems treated at Long terme

V.

CONCLUSION

In this paper we presented a selected works interested in discrete event simulation (DES) application in healthcare supply chain (HCSC). The aim objective is to present a reference paper for the recent works treating the DES application in hospital supply chain management from 2003 to 2013. This paper proposes a new conceptual classification scheme (CCS) to classify current developments of the use of the DES in Healthcare Management research.

Fig 3. Distribution of articles according to the type of problems treated at Mid-terme

According to the different analyzed papers we can summarize that the discrete event simulation (DES) is most applied to improving the healthcare supply chain and mostly for the emergency department, so new research needs to focus on improving the various logistics activities (sterilization, restoration…) in the hospital. In the future work we can take into account the different simulation techniques applied in healthcare supply chain (system dynamics (SD), discrete event simulation (DES), agent based modeling). The reviewed articles will be mapped onto the developed conceptual classification scheme.

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