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SIMULATION-BASED MODELS OF EMERGENCY DEPARTMENTS: REAL-TIME CONTROL, OPERATIONS PLANNING AND SCENARIO ANALYSIS

Sergey Zeltyn2, Yariv Marmor1, Avishai Mandelbaum1, Boaz Carmeli2, Ohad Greenshpan2, Yossi Mesika2, Segev Wasserkrug2, Pnina Vortman2, Dagan Schwartz3, Kobi Moskovitch3, Sara

Tzafrir3, Fuad Basis3, Avraham Shtub1, Tirza Lauterman1

1 Technion – Israel Institute of Technology, 2 IBM Haifa Research Labs, 3 Rambam Health Care Center

ABSTRACT

The Emergency Department (ED) of a modern hospital is a highly complex system that gives rise to numerous managerial challenges, spanning the full spectrum of operational, clinical and finan-cial perspectives. Since realistic ED models are intractable analytically, one resorts to simulation for an appropriate framework to address these challenges, which is what we do here. Specifically, we apply a general and flexible ED simulator to address several central wide-scope problems that arose in a large Israeli hospital. First, we demonstrate that our simulation model can support real-time control by inferring missing data of the current ED state, which then enables short-term pre-diction and operational planning (e.g. nurse staffing). To this end, we implement a novel simula-tion-based technique that utilizes the concept of offered-load. Then, using the same simulation-based approach, we evaluate the impact of RFID (Radio Frequency Identification) technology on ED operational metrics and costs. Finally, we analyze design and staffing problems that arose from physical relocation of the ED, which lead to the implementation of design and process im-provements. A prerequisite for all of the above is an extensive (cleaned and validated) hospital data-based system, which is the data source for our simulations, presently offline and potentially (after implementing an RFID system) in real-time.

1 INTRODUCTION

1.1 Operations management in Emergency Departments: Main challenges and simulation-based modeling

The rising cost of healthcare services has been a subject of mounting importance and much dis-cussion, worldwide. Ample reasons have been proposed, for example increasing life spans and the availability of an ever-increasing number of costly diagnostic and therapeutic modalities (Hall et al. 2006). Yet, regardless of their cause, rising costs impose, and rightly so, pressures on healthcare providers to improve the management of quality, efficiency and the economics in their organizations.

A critical healthcare organization, widely recognized in need of urgent enhancements, is the large hospital, the complexity of which is well represented by the micro-cosmos of its Emergency Department (ED). The latter is our focus here – for being the window through which a hospital is judged for better or worse, and for amplifying a variety of problems that arise also elsewhere. ED management should intertwine clinical, operational and financial dimensions. In this paper,

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we focus on a somewhat operationally-biased (business process) view, which is then expanded to accommodate interactions with the other clinical and financial aspects. From an operational view, overcrowding and subsequent excessive delays are the most urging ED problems (Sinreich and Marmor 2005), having clear interactions also with ED clinical and fi-nancial dimensions. Citing Green, 2008, “arguably, the most critical delays for healthcare are the ones associated with healthcare emergencies”. Overcrowding in the ED can and does cause, among other things, the following (see, for example, Derlet and Richards, 2000): � Poor service (clinical) quality: Patients with a severe problem (e.g. undiagnosed myocar-dial infraction) can wait for hours until their first diagnosis by a physician (which could be-come life threatening). Other patients are getting treatment that is inferior to the one they would have gotten after being properly diagnosed and hospitalized in the appropriate wards. � Patient in unnecessary pain: When ED staff is too busy, patients are often neglected to ex-perience unnecessary pain or discomfort - there could simply be no one able to approach them, for example, when the staff is catering to more clinically-urgent cases. � Negative emotions, all the way to violence against staff: Extended waiting times, com-bined with an overcrowded environment and psychological pressures, is a recipe for agitation and violent behavior. � Ambulance diversion: Over-congested EDs could turn incapable of accepting newly arriv-ing ambulances, which gives rise to ambulance diversion and its multitude negative ripple ef-fects. � Patients' LWBS (Leave Without Being Seen): Some patients, being exhausted by waiting, abandon the ED at different phases of their process (often to be returning in later times and worsened conditions). � Inflating staff workload: The longer the ED sojourn the longer the ED effort required (for example, as is the case in our partner hospital, when the protocol calls for a nurse-visit every 15-minutes of a patient's ED stay). � Increased vulnerability: Long sojourns increase the likelihood of clinical deterioration, contagion of additional maladies and, all in all, the occurrence of adverse events.

There exist tools and methods that help to alleviate the problem of overcrowding and exces-sive waits. These solutions typically demand careful planning of the ED processes and appro-priate staffing scheduling techniques for nurses and physicians. See Sections �1.3.1 and �1.3.2 for a discussion on related work and Section �4.4.2 that introduces a new approach to staff scheduling. However, it turns out that, in order to apply these methods properly, one should first resolve several other critical problems. Some of these problems are studied in our paper and are briefly specified below. Availability of information on the current ED state. Proper functioning of the ED, even given that adequate workforce and technical resources are available requires precise informa-tion on the current state of the ED. Specifically, we have in mind at least the accessibility to reliable information on the number and profile of patients of different types in the ED, the pa-tients' state in the ED process (e.g. if results of a certain laboratory test are available for a spe-cific patient) and accessibility of data on the physical location of patients (e.g., when a patients in a bed returns from an X-ray and placed in a location that differs from the one she was origi-nally taken from). At least partially, this data should be available via the hospital IT systems. However, the data in these systems frequently turns out unreliable (Ash, Berg and Coiera, 2004) as it is fed by humans, who tend to circumvent or ignore procedures and thus fail to pro-vide updates in real time. (We hasten to add that, in the hospital setting, such data-maintenance

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failures are often the outcome of clinical emergencies taking their well-deserved priorities.) Moreover, some types of data cannot be extracted from most of the currently installed IT sys-tem. For example, assume that a patient is waiting in a queue for an X-Ray check: what is the patient's place in queue and the anticipated wait until service start? In our paper, we shall ad-dress the challenges that arise from such unreliable and inaccessible data by using an online simulation approach (Section �4.2), and examining the benefits and costs of RFID (Radio Fre-quency Identification) technology (Section �5), which enables patients location tracking in real-time. Short-term forecasting and operations planning. As a rule, operational decisions, including staff scheduling, are preferably planned in advance. In this paper, we consider the problem of short-term planning over a future horizon of several hours-to-days. Two problems should be solved in order to enable effective planning. First, one should implement an adequate forecast-ing model that predicts the number of exogenous arrivals to the ED. Such a model should pro-vide predictions for each patient type since different types conceivably require different treat-ments and amounts of work from ED resources. Second, one should develop a model that combines the forecasts of external arrivals with the internal dynamics of the ED. Such a model would support operational decision making throughout the ED. Providing short-term predic-tions within the context of a command and control system presents its own unique challenges, in particular analysis must be performed within a short time frame (in the order of minutes), and must be based on data regarding the current ED state. Section �4 below is dedicated to this issue. Scenario analysis and strategic planning. Assume that design changes are planned for the ED. Two concrete examples, considered in this paper, are physical re-location of the ED (Sec-tion �6) and evaluation of an RFID-based system for patients location tracking (Section �5). Given such proposed design changes, ED management should evaluate how the ED processes will be altered, what will be the consequences for staff scheduling and what are the financial costs to be incurred. Such considerations must be taken into account when deciding on whether to implement contemplated design changes or, in case the changes are avoidable, how to im-plement them in the best possible way. Simulation-based modeling of the ED. All challenges formulated above require a model of the ED. As analytical models are unable to capture the complexity of ED operations, a major component of our solution is an ED simulation model (as reported in Sinreich and Marmor 2005 and discussed in Section 3). It turns out that our simulation-based model is general and flexible enough to address these challenges. It provides estimates regarding the current opera-tional state, completing the missing data; it can incorporate forecasting and staffing techniques that enable high-quality short term operational planning. Moreover, it can be smoothly inte-grated with the real-time decision support system that we are currently developing (Green-shpan et al. 2009). Finally, it is very useful in scenario analysis towards supporting strategic planning.

1.2 Contribution and structure of the paper

In subsequent sections, we continue with the survey of related work (Section �1.3) and describe the ED of an Israeli hospital where our models are applied (Section �2). We now proceed with an outline of our research contributions.

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1.2.1 General simulation model of the ED

In this paper, a simulation-based modeling approach is applied to a wide spectrum of ED prob-lems. It is important to emphasize that all these problems were addressed using a single general flexible simulation model of the ED (Sinreich and Marmor 2005, 2006). Slight modifications of the base model were required for each specific problem but its flexible design enables quick and efficient adaptation, on demand. Significantly, although all our case studies were performed in a single Israeli hospital, the model can be easily modified and applied to other EDs. (In fact, the work of Sinreich and Marmor was based on research performed in the EDs of eight hospitals, leading to the creation of a "universal" ED simulator.)

Section 3 of our paper contains a description of our simulation model.

1.2.2 Short-term forecasting and operations planning

In Section �4, we apply our simulation-based approach to real-time control, short-term forecasting and operations planning. Starting with a brief problem statement in Section �4.1, we continue to simulation-based inference of the current state in Section �4.2. We emphasize especially the prob-lem of inferring patients' discharge times. (At discharge times, patients are either released home or transferred to hospital wards.) As it turns out, hospital information on these discharge times is not precise and we complete it via simulation. Section �4.3 describes forecasting of external arri-vals to the ED. Then, in Section �4.4, our two main staff scheduling methods are presented. We start with the prevalent RCCP (Rough Cut Capacity Planning) method (Vollman, Berry and Whybark 1993) and then present a new refined technique, based on the concept of offered-load. The offered load of a resource type (nurses, physicians, imaging devices), at a given time, is its (average) amount of work in process, where work is measured in time-units. This concept re-fines RCCP in the sense that it allocates workload accurately over time (while RCCP, on the other hand, accounts for all the workload brought in by a patient right at the arrival time of that patient). The offered-load concept originates in queueing-theory (see Feldman et al. 2008, and references therein); it is here adapted to the complex ED environment. Roughly speaking, the ap-plication of our offered load approach involves two stages. First, a simulation with an "infinite" number of resources is run; this yields, for each resource type, estimates of its time-dependent of-fered load (which assumes out delays due to scarce resource). The offered-load gives rise to a nominal time-dependent staffing level, required from each resource type. It provides the baseline for calculating actual staffing levels, accounting for performance goals and resource constraints. (Feldman et al. 2008 develops successful staffing strategies, based on the offered-load, but merely to single-queue systems.) Section �4.6 presents results of our simulation experiments. We start with validation of our current state inference approach, comparing simulation-based estimates with real data from the ED database. Satisfactory useful results are reported. Then, a simulation-based forecasting model is run and load estimates for physicians and nurses during the future 8 hours are derived, using RCCP and the offered load approaches. Finally, we assume that recommendations that follow the RCCP and offered load approaches were in fact implemented. We then run new simulations with the corresponding staffing levels of physicians and nurses. The results of these simulations�indi-cate that the offered load approach is preferable over the RCCP approach.� Remark. Content of Section �4 is based on the conference paper of Marmor et al. (2009).

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1.2.3 Scenario analysis and strategic planning: costs and benefits of RFID technology

In Section �5, we proceed to scenario analysis, estimating possible benefits of RFID implementa-tion in the ED. We start with the description of two “candidate” RFID technologies in Section �5.1. Both technologies and an “ideal” technology that combines their advantages are used in our simulation modeling. In Section �5.2, the required process changes are outlined. Specifically, our goals are to prevent unauthorized customers departures (LWBS – Left Without Being Seen), eliminate or decrease unnecessary waiting times of customers that are “forgotten” in Imaging (CT and X-Ray) units and, finally, expose possible problems in the physical layout of the ED.

Results of comprehensive simulation experiments, presented in Section �5.3, reveal that RFID implementation under the assumptions of our model affects the Average Length of Stay (ALOS) in two opposite directions. Decrease of unnecessary waiting times, naturally, implies ALOS de-crease. However, decrease or elimination of LWBS customers increases the number of patients in the system, hence also workload and ALOS. Our simulation experiments help decision-makers to estimate and compare both of these effects.

Concerning operational performance of the two RFID technologies – we have found that Pas-sive RFID technology provides a very good answer to our problems. However, the ultimate deci-sion on RFID technology implementation must be left to the hospital decision-makers, who could take into account additional considerations: implementation cost, layout constraints, psychologi-cal factors related to the use of different RFID systems etc.

1.2.4 Integration with decision-support system in ED

In order to provide ED managers and other decision-makers the appropriate tools for real-time control and operational planning, one should design an interface between our simulation models and the IT systems of the hospital. In addition, ED data should be presented to the decision-makers in an efficient and convenient way. Currently, we are developing a Decision Support Sys-tem that addresses this challenge (Greenshpan et al. 2009). In Section �4.7, we demonstrate how this system can be integrated with real-time control and forecasting features, as presented in Sec-tion �4. If RFID technology is introduced into the ED, the Decision Support System can be sig-nificantly enhanced - Section �5.4 elaborates on this issue.

1.2.5 Scenario analysis and strategic planning: ED re-design under physical relocation

A decision to design and construct a new ED was taken by management of our partner Israeli hospital. As the first stage in that transition, the ED was moved to a temporary location at the basement of the hospital, allowing the old ED to be renovated and expanded. Then, in 2010, a new permanent ED will be opened at the location of the previous ED. Our simulation-based ap-proach has been used to evaluate the consequences of these two ED transfers, from different per-spectives: staff scheduling, staff walking distances, design and performance of imaging facilities etc. In Section �6, we present the main research issues of a project that was dedicated to the trans-fer from the original location to the temporary location. Many of our recommendations, that arose from this research, were actually implemented. (A project on the second stage of transfer, to the permanent location, was also carried out but is not reported here.) Our main conclusions are as follows:

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� Staffing analysis of the nursing team was performed. (See Section �6.2.) It turned out that the nurses treating Internal Walking patients would become a bottleneck unless process changes are performed. In the initially planned state, this nurse team had to treat all walking patient (Internal, Surgical and Orthopedic) located in the same ED ambulatory unit. We rec-ommended to schedule an additional nurse to the Ambulatory unit during most hours of the day, and relocate a nurse from the Trauma (Surgical & Orthopedic) unit to the internal unit. � The ED, both in its previous and temporary state, has its own X-Ray unit. Patients are ei-ther sent to this unit or routed to the general X-ray unit of the hospital. We modeled several scenarios for ED X-Ray operations, under alternative operating hours and patients routing po-licies (see Section �6.3 for a discussion). Consequently, the initial suggestion to open the ED X-Ray room over the extended period of 10 or even 24 hours of the day was rejected. The op-timal opening hours turned out to be 12:00-18:00. During hours when the ED X-Ray unit is closed, all patients are sent to the general X-ray unit. Concerning the patients routing policy during 12:00-18:00, it was recommended to send patients to the general X-ray unit according to a threshold strategy, specifically once the length of queue to the ED X-ray exceeds seven patients. � Since the area of the temporary ED is significantly larger that the area of the previous ED (2,000 square meters versus 1,000 respectively), it turned out that the walking distances of the staff increase, on average. Consequently, slightly higher staffing levels should be used in the temporary ED, in order to sustain the same service level, as before. In addition, we recom-mended some changes in the ED design: related ED units with large flow of staff and patients between them should be located as close as possible to each other. For example, adjacent loca-tion of Trauma patients room and Treatment room have a high priority and it is preferable to move the Ambulatory ED, where Walking patients reside, closer to Trauma ED. (Some physi-cians treat both types of patients; therefore, their walking distances could be reduced.) � Simulation-based comparison between the originally planned state of the ED and the state after implementation of our recommendations (specified above and some others) has shown that the full implementation of our recommendation implies improvement of all significant ED performance measures. For example, Average Length of Stay (ALOS) of some patient types decreased by nearly 60 minutes. Sensitivity analysis with respect to arrival rates have shown that the temporary ED, working under our recommendations, can function under 10% load in-crease and still provide better service level than under its originally planned design.

1.3 Related work

1.3.1 Simulation in support of ED operations

The application of simulation has been instrumental in addressing the multi-faceted challenges that the healthcare domain is presenting (Kuljis, Paul, and Stergioulas 2007). Wide spectrum of ED problems also received significant attention in this kind of research. It is quite common to use simulation, mostly by researchers, to compare operational models or to assess a model that addresses a specific research question. For example, Medeiros, Swenson, and DeFlitch 2008, present a simulation-based validation of a novel approach to ED processes change, placing an emergency care physician at triage. Kolb et al. 2008, study different policies of patient transfer from ED to Internal Wards, in order to decrease the resulting over-

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crowding and delays. (Tseytlin 2009, solves a similar problem for our Israeli hospital using ana-lytical approaches, based on queueing models.) For some reviews on simulation-based approach in health care, see Jun, Jacobson, and Swisher (1999), White (2005) and Jacobson, Hall and Swisher (2006).

Improvement of patients experience in EDs via application of simulation and Lean Manufac-turing tools was considered in Khurma, Bacioiu, and Pasek (2008).

The prevalent approach for addressing ED overcrowding is staff (re)scheduling (e.g. Sinreich and Jabali (2007), Badri and Hollingsworth (1993), namely adding or shifting in time staff re-sources so as to uniformly maintain acceptable ED performance (e.g. time to the First Encounter with a Doctor, or FED time). Most such works focus on off-line steady-state decision making, as opposed to on-line operational and tactical control. Other researchers analyze alternative opera-tional ED designs (Garcia et al. 1995; King, Ben-Tuvim, and Bassham 2006; Liyanage and Gale 1995) – for example, comparing acuteness-driven models (e.g. triage) against operations-driven models (e.g. fast-track, which assigns high priority to patients with low resource requirements).

A widespread approach is to decompose the problem by focusing only on one type of re-source. An example is an effort to schedule nurses while ignoring the scheduling of other re-sources (Draeger, 1992); or scheduling physicians and nurses, one after the other (Sinreich and Jabali, 2007). These attempts, based on simulation models, predict performances of the ED as a function of staffing and scheduling decisions. The simulation models require input in the usual form of patient arrivals and service durations, of each patient by each resource type, exactly as in the simulation that we are using.

We are, however, unaware of any uses of simulation in a hospital setting for real time com-mand and control. Nor are we aware of any work in which simulation has been used to complete partial data regarding the current operational state. These research directions are pursued in Sec-tion �4.

In a broader perspective, our research gives rise to a multitude of practical and theoretical challenges, many of which touch on active simulation-driven research. For example, input model-ing (Biller and Nelson 2002) and historical (trace-driven, resampling) simulation (Asmussen and Glynn 2007; McNeil, Frey and Embrecht 2005), are both related to the problem of properly in-corporating actual ED data into our simulator.

Yet, deserving of an expanded attention is Symbiotic simulation (Fujimoto et al. 2002, Huang et al. 2006), defined as "one that interacts with the physical system in a mutually benefi-cial way", "driven by real time data collected from a physical system under control and needs to meet the real-time requirements of the physical system" (Huang et al. 2006). Additionally (Fuji-moto et al. 2002), symbiotic simulation is "highly adaptive, in that the simulation system not only performs "what-if" experiments that are used to control the physical system, but also accepts and responds to data from the physical system". In some of our ED implementations, however, the in-teraction between the simulator and its underlying physical system must go beyond the common symbiotic simulation framework (see Section �4). Specifically, we obtain real-time data regarding current state, then complete the data when necessary via simulation, next predict short-term evo-lution and workload, and finally proceed with simulation and mathematical models as decision support tools, all this in real-time or close to real-time.

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1.3.2 Alleviating overcrowding: analytical approaches to staff scheduling

Although simulation-based approach is in the focus of our research, we emphasize that an opti-mal approach to real-life ED problems should combine simulation and analytical insights. These insights can be especially valuable when staff scheduling problems must be solved. In general, both deterministic and stochastic mathematical methods can be applied.

For example, Beaulieu et al. (2000) present a deterministic mathematical programming ap-proach to staff scheduling. The RCCP approach, demonstrated in Section �4 (Vollmann, Berry, and Whybark 1993), is also based on deterministic considerations.

However, in our opinion, stochastic models, based on queueing theory, are more appropriate for capturing volatile and inherently non-deterministic ED reality. Although, it is hard to design a tractable comprehensive queueing model for ED, it is possible to apply simpler models combin-ing them with simulation. The research on the offered load concept, presented in Section �4 pro-vides us with an example of this approach. Using the technique, applied to time-varying queueing systems in Feldman et al. (2008), we develop the staff scheduling algorithm that jointly uses simulation and analytical staffing formulae.

See Green (2008) for further references on these and related issues.

1.3.3 Applications of RFID technology in health care

Significant research and development efforts have been devoted to the search after efficient and accurate Indoor Location Tracking (ILT) systems. While the Global Positioning System (GPS) has become the de-facto standard for outdoor tracking, and it serves as the foundation for many location tracking applications, GPS has yet no equivalent leading technology, which is suit-able for indoor tracking (Lee et al. 2006). ILT systems are also referred to as RFID, after the technology of Radio Frequency IDentifica-tion. RFID technology has recently become widespread due to its many merits. Basically, RFID provides unique identifications to objects, hence it can be used as the foundation for objects tracking, monitoring and control (Hightower and Borriello 2001; Hightower, Want and Borriello 2000). RFID has traditionally been used for tracking passive entities, such as consumer package goods, medications and medical equipment. Yet this same technology can be used for uniquely identifying humans, e.g. patients and care personnel in hospitals. Applying RFID for indoor loca-tion tracking requires an additional layer, which associates the RFID tag with a specific location. This association can be implemented via two conceptually different approaches (Saha et al. 2003): � Cell-based location tracking – location identified through the location of the reader of the RFID tag. � Triangulation – location calculated from radio frequencies, used in the communication be-tween the RFID tag and scattered RFID readers (Bahl and Padmanabhan 2000).

RFID-based ILT systems have been recently developed for addressing specific needs that arise in patients' care. For example, MASCAL (Emory and Leslie 2005) is an integrated solution for tracking patients and equipment during events of mass causality; MASCAL is based on the 802.11 communication network, and it is integrated with the hospital's clinical database. As an-other example, an RFID-based system was deployed in Taiwan (Wang et al. 2006), for identifica-tion and tracking of potential SARS cases; the system provides active patient-location tracking information as well as body temperature indication. In this present work, RFID it the technology

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behind our proposed ILT systems enabling data-based business process management - in particu-lar transformation towards improvement.

2 RESEARCH FRAMEWORK

This research is a part of an Open Collaborative Research program, a combined research effort of three organizations partnered together: the Faculty of Industrial Engineering & Management at the Technion Institute, IBM’s Haifa Research Laboratory and a government-affiliated Israeli hospital – which is Israel’s largest northern medical center, catering to over 2 million citizens (about one third of Israel’s population). The hospital comprises 36 Internal Wards, around 1,000 patients can be hospitalized simultaneously and 75,000 patients are hospitalized yearly. In this re-search project, we focus on several hospital units including the ED – which is the gate and the window to the hospital, and which must operate in a mass-customized mode – i.e., follow a struc-tured care process while providing to each individual the specific care required. The ED of our partner Israeli hospital accepts 82,000 patients per year, with 58% classified as Internal patients and 42% as Surgical or Orthopedic patients. Mean sojourn time of patients in the ED equals 4:38 hours, with a large variance over individual patients. The simulation-based approach turned out to be well-tuned for operational and strategic chal-lenges that arise in the ED. Our recommendations, that arose from the application of this ap-proach, were successfully applied when the ED was moved from the permanent to a temporary location (Section �6). Other research issues that are studied in Sections �4 and �5: real-time control, decision support system for operational planning and validation of RFID technology implementa-tion, provide promising research results and will, hopefully, be followed by full-scale implemen-tation.

3 BASIC SIMULATION MODEL OF THE EMERGENCY DEPARTMENT

In Figure 1, we depict two perspectives of the care process that patients undergo at the ED: the resource (i.e. physicians, nurses, etc.) perspective, and the process (activities) perspective. In this care process, two types of queues portray the delays that patients experience: first are resource queues (rectangular, in red), which are due to limited resources (e.g. nurses, imaging equipment); the second are synchronization queues (triangular, in green), which arise when one process activ-ity awaits another (e.g. a patient waiting for results of blood tests and x-ray, in order to proceed with the doctor's examination).

The care process in an ED was captured in a simulation model, created with the generic simu-lation tool of Sinreich and Marmor (2005). In addition to the care process, the simulation model requires patients arrival processes, for each patient type, and staffing levels of the medical staff, with their respective skills. For our purpose, the model was configured to the ED specs of our partner hospital, as follows. There are six types of patients, which also require different skills from the caring Physicians. Patient types 1 and 2, which are Internal Acute and Internal Walking respectively, are treated by internal physicians. Patient types 3 and 4, which are Surgical Acute and Surgical Walking respectively, require treatment by surgical physicians. Finally, Patient types 5 and 6, Orthopedic Acute and Orthopedic Walking respectively, require an orthopedic physician. Acute patients need a bed while walking patients use chairs. In addition, patient types differ by the arrival process (e.g. number of arrivals per hour and by day-of-week), and by the decisions made in the patient care process (e.g. the percentage of patients sent to X-ray).

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Figure 1 – ED resource-process chart The actual simulation tool is comprised of the following three modules:

1. The first module is a Graphical User Interface (GUI) that describes the general unified proc-ess illustrated in Figure 2. Through the GUI, the user can input data and customize the gen-eral process to fit the specific ED modeled and receive operational results from the ED after simulation run.

2. The second module includes two mathematical models that are used to estimate patient arri-vals and staff walking time. The simulation tool uses the models for patient arrival estimation that were developed in Sinreich and Marmor (2005).

3. The third and final module is the simulation model itself. This model receives data from both the GUI and the mathematical models. The simulation is updated and customized automati-cally to fit a specific ED based on data and information the user passes on to the GUI. The simulation model is transparent to the user who is only required to interact with a user friendly GUI without the need to learn a simulation language syntax.

4 SIMULATION-BASED MODELING FOR REAL-TIME CONTROL AND OPERATIONS PLANNING IN ED

4.1 Research goal

In this section, we start to apply our simulation-based modeling approach to real-life ED prob-lems. We show that this approach can help to ED managers infer the missing information on the current ED state, provide a reliable forecast of the ED state in the short-term and perform opera-

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tional staff scheduling decision. Finally, in Section �4.7 we demonstrate how our simulation-based tools are integrated with the Decision Support Systems for hospital managers.

4.2 Simulation-based inference of current state

As discussed in Section �1.1, reliable information on the current state of ED is crucial for the real-time control and operational planning. Typically, only partial data of the current ED state is main-tained and available from the hospital's electronic data systems. For example, in our case, no data exists regarding the queue (number) of patients waiting to be seen by a physician. One expects the amount and quality of usable data to constantly improve over time, due to the introduction of additional data entry systems or new technologies (e.g. sensor technologies, such as RFID and ul-tra-sound, for accurate location tracking of patients, staff and equipment). However, within the chaotic ED environment, it is reasonable to expect that some data will always remain unavailable or too costly to acquire. We now discuss how to infer missing data, using the simulation model described above. Such simulation-based inference must deal with several issues. The first is consistency: how to gener-ate simulation paths that are consistent with available ED data. Another important issue is data inaccuracy. (Note that inaccurate data adds complexity to generating simulation realizations that are consistent with the provided data.) A third challenge, arising due to the availability of only incomplete data, is the identification of an appropriate initial state for the simulation. The way we overcome this last hurdle is to feed in actual arrival data for a long enough period of time that en-sures that the simulation warm-up period is over, prior to estimating the missing data. Coping with consistency and inaccuracy raises interesting research questions, as already al-luded to. Here we content ourselves with two ED-specific practical examples, of accommodating actual ED data – accurate and inaccurate. Accurate data - taking actual arrivals into account: In our partner ED, receptionists enter data into the IT systems, in particular regarding patient arrivals, as part of the admittance process. The medical state of the majority of arriving patients is such that they actively participate in the registration process, as the first step upon arrival. Registration of the others, acute patients unable of self-registration, is carried out by the paramedics bringing them in, shortly after arrival. There-fore, arrivals data accurately captures actual patients' arrival times – it can be thus fed as is into the simulator. (Receptionists also record patient type - Internal, Surgical, or Orthopedic - upon ar-rival.) To this end, we modified, in obvious manners, our generic simulator, which originally generates arrivals as a stochastic process (Poisson or relatives). It can now generate realizations consistent with the arrival data, when the latter is fed from an external database. Inaccurate data - taking discharges into account: Data about patients' discharge (departure) time, in our partner hospital, may be inaccurate. Specifically, each departure time is registered by the receptionist upon completion of the ED treatments – the patient is then ready to leave, for ei-ther home or to other hospital wards. In the (common) case when there is no ward immediately available to accept the patient, inaccurate data arises. Then, patients spend additional time wait-ing in the ED, which not only goes unrecorded but it also influences subsequent beds/chairs oc-cupancy and ED staff utilization (due to time spent on catering to these delayed patients). Addi-tional inaccuracies occur due to patients leaving without being seen (Green 2008), with or without their medical files, and some other accounting-related reasons. We found no efficient way for generating simulation realizations that are consistent with our discharge data, except for discarding inconsistent simulation paths. Note, however, that the prob-ability of generating a realization in which the simulated departure times correspond exactly to

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the provided departure times is negligible. To this end, and to overcome both inaccuracy issues, we condition on the number of patients of each type that were discharged from the ED according to the data. Namely, we considered a (short-term) simulation realization to be consistent if, at the end of the simulation run, the number of patients that were allowed discharge (of each type) equals, to within some accuracy constant, the number of patients of this type that were discharged according to the data. The results turned out satisfactory though, clearly, more thought is required here. In Section �5, we shall perform scenario analysis for the RFID technology implementation and further explore the issues related to benefits of exact knowledge of the ED state.

4.3 Forecasting ED arrivals

For simulating an ED future evolution, one must simulate patients arrivals to the ED. Figure 2, from our partner hospital, demonstrates that ED arrival rates strongly depend on day-of-week and hour-of-day. In addition, holidays and days after holidays have unusual patterns as well (holidays are lightly loaded and days after holidays are, as a rule, very heavy-loaded). For a reference on forecasting and modeling of ED arrivals, leading also to related literature, see Channouf et al. (2007).

Internal

Surgical and Orthopedic

Figure 2 - Hourly arrival rates per patient type (averaged over 4 years)

Arrivals in our simulation model are Poisson processes, with hourly rates that are forecasted

for each future hour in question (say a shift, or a day) and each patient type. We use long term MA (Moving Averages) in order to predict hourly arrival rates. For example, in order to predict the arrival rate (assumed constant) on Tuesday during 11-12am, we average the corresponding ar-rival rates during the last 50 "Tuesdays 11-12am", excluding those that are holidays or days after holidays.

The reason for choosing long-term MA is that we found it to provide essentially the same goodness-of-fit as more complicated time-series techniques. (Indeed, long-term MA, applied to the overall arrival rate over a test period of 60 weeks, gave rise to Mean Square Error (MSE) equal to 3.56, while two methods, based on Holt-Winters exponential smoothing, provide MSE=3.55 and 3.54). Another argument in favor of the use of long-term MA stems from the level of stochastic variability in historical samples, calculated for each hour-of-week, which fits

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that of a Poisson process (Maman, Mandelbaum, Zeltyn 2009); then, the historical mean (or MA) is a natural (Maximum Likelihood) estimate for the Poisson parameter, namely the arrival rate.

4.4 Staff scheduling approaches

With the present ED state assumed given (following Section �4.2), simulation is now to be used for predicting ED evolution, say several hours (a shift, a day) into the future; the goal is to deter-mine appropriate staffing levels of resources – nurses, physicians and support staff, as a function of time. Staffing the ED is a complex multi-objective problem. It must tradeoff conflicting objectives such as (1) Minimizing costs, (2) Maximizing resource utilization, (3) Minimizing waiting time of patients, (4) Maximizing quality of care. The complexity of such multi objective optimization, more so in a stochastic environment (e.g. randomness with respect to patients arrivals, routing, service durations, resources availability, and more) renders the optimization problem intractable analytically. This has thus led researchers to simulation-based heuristic solutions.

A prerequisite for staffing is accurate forecasting of patients' arrivals, as described in Section �4.3. We then continue with predicting resource utilization; this leads to feasible staffing, based on pre-specified goals for resource utilizations (Section �4.4.1). However, the resources' view cannot accommodate the experience of patients – for example, controlling the time until first encounter with a physician (Section �4.4.2). To control the latter, we calculate, for each resource type, its of-fered load as a function of time; then a classical staffing principle (square-root safety-staffing), in conjunction with the appropriate queueing model, yields our recommended time-varying staffing levels. In Section �4.5, a summary of our methodology will be presented.

4.4.1 Rough Cut Capacity Planning staff scheduling solution

Rough Cut Capacity Planning (RCCP) is a technique for projecting resource requirements in a manufacturing or a service facility. As such, RCCP supports decisions regarding the acquisition and use of resources. Procedures for RCCP are listed in Vollmann, Berry, and Whybark (1993). These procedures are based on the estimated time on each product or service unit, and the alloca-tion of the total time among the different resource types. The goal is to match offered capacity with the forecasted demand for the capacity of each resource type. Thus, RCCP algorithms trans-late forecasts into an aggregate capacity plan, taking into account the time each resource type spends on each type of product or service.

We are proposing to apply RCCP in the ED environment, as follows: � For each patient type i , calculate its average total time required from each resource r (e.g.

physician, nurse): dir. � For each forecasted hour t , calculate the average number of external arrivals of patients of

type i , ( )iA t . Deduce the expected time required from each resource r at time t:

r ( ) ( ) .i iri

RCCP t A t d��

� The recommended number of units of resource r at time t, nr(RCCP,t), would be the load RCCPr(t), amplified by safety slack/staffing, or ST (we have used for in our experiments ST =90%): nr(RCCP,t) = RCCPr(t) / ST.

We expect RCCP to achieve pre-planned resource utilization levels; its shortcoming, however, is that it ignores the time lag between arrival times of patients and actual times when these patients

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receive service or treatment from ED resources. Since patients spend in ED several hours, on av-erage, this time lag can be significant: the patients arrival rate frequently reaches maximum be-fore the workload for a specific resource reaches maximum. This problem is remedied by our next approach.

4.4.2 Offered load approach

The concept of offered-load is central for the analysis of operational performance. It is a refine-ment of RCCP in the sense that it spreads workload more accurately over time. For example, suppose that a nurse is required twice by a patient, once for injecting a medicine (10 minutes) and then, 3 hours later (in order to let the medicine take its effect), for testing the results (also 10 minutes). RCCP would "load" 20 minutes of nurse-work upon patient's arrival; the offered-load approach, in contrast, would acknowledge the 3 hours separation between the two 10-minutes re-quirements. Such time-sensitivity enables one to accommodate time-based performance meas-ures, notably those reflecting the quality of care from the patients view point. In the simplest time-homogeneous steady-state case, when the system is characterized by a constant arrival rate � and a constant service rate �, the offered load is simply R = �/��= �E(S) where E(S) is the average service time. The quantity R represents the amount of work, measured in time-units of service, which arrives to the system per (the same) time-unit. Staffing rules can be naturally expressed in the terms of the offered load: for example, the well known “square-root staffing rule” (Halfin and Whitt 1981; Borst, Mandelbaum, and Reiman 2004) postulates staffing according to ,n R R�� � (1) where �>0 is a service-level parameter, which is set according to some Service Level Agreement (SLA) or goal. This rule gives rise to Quality and Efficiency-Driven (QED) operational perform-ance, in the sense that it carefully balances high service quality with high utilization levels of re-sources. Arrival rates to an ED are, however, manifestly non-homogeneous and depend on the day-of-week and hour-of-day. Piecewise stationary approximations (such as SIPP - Stationary Independent Period by Period; Green, Kolesar, and Soares 2001), work fine if the arrival rate is slowly-varying with respect to the durations of services. This, however, does not happen in ED case.

Assume that arrivals can be modeled by a non-homogeneous Poisson with arrival rate ( ), 0.t t� In this case, our definition of the offered load is based on the number of busy servers

(equivalently served-customers), in a corresponding system with an infinite number of servers (Feldman et al. 2008). Specifically, any one of the following four representations gives it:

( ) [ ( ) ( )] [ ( )] [ ] ( ) ( ) ( ) ,� � � �

� � � � � � � � �� �� �t t

e t SR t E A t A t S E t S E S E u du u P S t u du (2)

where A(t) is the cumulative number of arrivals up to time t, S is a (generic) service time, and Se is its so-called excess service time (See the review paper by Green, Kolesar, and Whitt (2007) for more details, as well as for useful approximations of (2)). Then, for calculating time-varying per-formance, we recommend to substitute (2) into the corresponding steady-state model, which is the classical M/M/n queue, or Erlang-C, in our case. To be concrete, assume that our service goal specifies a lower bound �, to the fraction of patients that start service within T time units. Our QED approximation then gives rise to

1 { } { 0} { | 0} ( ) ,t t t tT R Rq q q q tP W T P W P W T W h e �� �� � � � � � � � � � � � (3)

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Where h(�t) is the Halfin-Whitt function (Halfin and Whitt 1981). Equation (3) can now be solved numerically with respect to �t , and the staffing rule (1) is replaced by the time-varying staffing function: ( , ) ( ) ( ).tn OL t R t R t�� � (4) The above procedure has been called the "modified offered load approximations" – readers are referred to Feldman et al. (2008) for additional details and further references.

Square-root staffing are mathematically justified by asymptotic analysis, as workload (and hence the number of servers) increase indefinitely. (The practical motivation was large telephone call centers.) However, ample experience (as well as recent research; e.g. Janssen, Van Leeu-waarden, and Zwart (2008)) demonstrate amazing levels of high accuracy, already for single-digit staffing levels. This renders the above staffing rule relevant for EDs, as well as other healthcare systems, where the number of servers is indeed single-digit. (For small systems, one could al-ways apply exact Erlang-C formulae. And indeed, we tested these exact calculations against the QED approximations in our experiments below, and the results were essentially unaltered.)

Summarizing, we apply the proposed offered-load approach via the following steps: � First, we are running the simulation model with infinitely many resources (e.g. physicians, or

nurses, or both). � Second, for each resource r (e.g. physician or nurse) and each hour t, we calculate the number

of busy resources (equals the total work required), and use this value as our estimate for the offered load R(t) for resource r at time t. (The final value of R(t) is calculated by averaging over simulation runs.)

� Finally, for each hour t we deduce a recommended staffing level nr(OL,t) via formulae (4) and (3).

4.5 Methodology for forecasting short-term future ED state

Our simulation-based methodology for short-term forecasting of the ED state is as follows: 1. Initialize with the simulation-based estimate of the current ED state. 2. Use the average arrival rate, calculated from the long run MA, to generate stochastic arrivals

in the simulation. 3. Simulate and collect data every hour, for 8 future hours, using infinite resources (nurses, doc-

tors). 4. From step 3, calculate staffing recommendations nr(RCCP,t) and nr(OL,t) using RCCP and

Offered Load methods, described in Sections �4.4.1 and �4.4.2, respectively. 5. Run the simulation from the current ED state with the recommended staffing. 6. Calculate performance measures. The above can be repeated with existing staffing (in Step

(5)), which enables to compare it against RCCP and Offered-Load staffing.

4.6 Simulation experiments

We now apply our methodologies through simulation experiments. First, we demonstrate the ability of our simulation-based tool to estimate current ED state, using a database from an Israeli hospital (Section �4.6.1). For that, we randomly choose a month (August 2007) in the database, and compare the known number of patients in the system with the simulation's outcome (follow-ing Section �4.2). In the second experiment (Section �4.6.2), we use the ED state at a specific time (September 2nd, 2007, 16:00) to predict 1-7 hours ahead. (The chosen day is a Sunday, which, in

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Israel, is a busy day of the week, being the first day following the weekend.) We then conclude, in Section �4.6.3, with a comparison of some ED performance measures, using two alternative staffing methods (following methodology, developed in Section �4.4).

4.6.1 Current state

We ran 100 one-month long replications of each scenario, in order to compare our simulation re-sults with the data from hospital's database. For each date and hour, we calculated the average number of patients over the simulation replication (Avg series in Figure 3), and the corresponding standard deviation (SD), an Upper Bound (UP = Avg + 1.96 SD), and a Lower Bound (LB = Avg - 1.96 SD). In Figure 3, we depict 4 days, chosen to test our methodology against the (actual) number of patients from the database (Wip-Work in progress). We chose two periods that are two days long, the last day of the weekend (Saturday in Israel) and the first working day of the next week (Sunday). (For example, DOW_7_4 at time axis stands for 4am on Saturday and DOW_1_16 denotes 4pm on Sunday.)

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Figure 3 – Comparing the Database with the simulated ED current-state (Weekdays and Week-ends)

These days are typically the calmest and busiest in the week, respectively. Note that the night and early morning shifts (hours 1-10 in Figure 3) are not overloaded (see, for example, the utilization profiles during 09-10, in Table 1), and performance measures are then less accurate. However, once the ED becomes congested, the simulation does yield an accurate prediction of the number of patients in the ED. At all times, though, the accuracy of prediction varies from reasonable to good. Remark. A probable explanation for somewhat worse fit of the simulation during lightly loaded hours is the following. When the load is low, the staff has more time for activities that are not in-corporated into our simulation (e.g. department meetings). In contrast, during heavy loaded peri-ods, there is virtually no time for such activities and reality becomes consistent with the simula-tion.

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4.6.2 Forecasting – staffing level

Next, we looked at performance measures in the near future, to see if there is a way to improve ED operations via staffing. We looked at the offered load of all the relevant resources: Internal physician (Ip), Surgical physician (Sp), Orthopedic physician (Op) and Nurses (Nu). For our ex-ample, we use ED data until 16:00 and then apply simulation to forecast each succeeding hour, until the end of the day. In Table 1, we display the ED state until 16:00, then continued with the simulation-based forecast; the staffing levels used in the simulation is the one exercised in our partner ED – we refer to it as "the existing staffing", and it appears in Table 2, under n(Current). Columns Ip, Sp, Op, and Nu list utilization levels of the respective staff. (For nurses, this ac-counts for the time devoted to patients care, and excluding administrative duties; Physicians are exempted from the latter.). #Beds and #Chairs represent the number of occupied beds and chairs, respectively; %W is the fraction of patients that are exposed to unsatisfactory care, which here is taken to be "physician's first encounter occurs later than 30 minutes after arrival to the ED". In Table 2, we display the following characteristics: � ED existing staffing - n(Current), � the offered load level (as explained in Section �4.4.2) in Offered Load column, � recommended staffing level based on the offered load (aiming to achieve %W< 0.25 hour) – n(OL), � the RCCP level (as explained in Section �4.4.1) – RCCP Load columns, � RCCP staffing recommendations aiming at less than 90% staff utilization – n (RCCP).

Table 1 – Simulation performance measures – current and forecasted (existing staffing) Hour Ip Sp Op Nu #Beds #Chairs %W 09-10 73% 1% 23% 55% 15.7 8.6 7% 10-11 93% 25% 59% 68% 23.5 17.0 33% 11-12 94% 59% 67% 72% 29.3 22.8 51% 12-13 90% 45% 81% 58% 33.2 30.3 53% 13-14 95% 68% 94% 71% 36.2 34.7 77% 14-15 90% 62% 76% 63% 34.2 33.3 70% 15-16 91% 51% 46% 51% 34.4 30.5 77% 16-17 100% 43% 41% 53% 34.6 27.6 69% 17-18 95% 58% 46% 57% 33.4 23.6 52% 18-19 90% 46% 52% 50% 32.4 23.9 31% 19-20 89% 64% 70% 58% 29.3 25.3 40% 20-21 79% 64% 75% 56% 26.5 20.6 39% 21-22 84% 46% 60% 45% 23.4 17.0 23% 22-23 66% 38% 51% 46% 20.2 13.9 20%

Table 2 – Staffing levels (present and recommended)

n (Current) Offered Load N (OL) RCCP Load n (RCCP) Hour Ip Sp Op Nu Ip Sp Op Nu Ip Sp Op Nu Ip Sp Op Nu Ip Sp Op Nu 16-17 4 1 2 5 7.8 0.8 0.8 4.1 9 2 2 5 3.0 0.5 0.6 2.4 4 1 1 3 17-18 4 1 2 5 3.7 0.4 0.9 2.5 5 1 2 3 3.3 0.4 0.7 1.3 4 1 1 2 18-19 4 1 2 5 3.2 0.4 1.1 2.7 4 1 2 4 2.3 0.4 0.4 1.3 3 1 1 2 19-20 4 1 2 5 2.3 0.5 1.2 2.5 3 1 2 3 2.4 0.5 0.6 1.0 3 1 1 2 20-21 4 1 2 5 2.7 0.6 1.5 2.7 4 1 2 4 2.3 0.5 0.4 1.0 3 1 1 2 21-22 4 1 2 5 2.4 0.4 1.3 2.4 3 1 2 3 2.8 0.5 0.4 1.1 4 1 1 2 22-23 4 1 2 5 2.3 0.2 0.9 2.0 3 1 2 3 2.4 0.3 0.2 1.0 3 1 1 2

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4.6.3 Short-term forecasting – performance and staffing levels

In Table 3, we record simulated performance, under staffing levels calculated via the Offered Load and RCCP methods. As anticipated, the offered-load method achieved good service quality: indeed, the fraction of patients getting to see a physician within their first half hour at the ED is typically less than half of those under RCCP, the latter being also more influenced by the changes in the arrival rate. RCCP of course yields good performance at the resource utilization column, all being near the 90% target (for the resources with staffing levels larger than 1-2). It is interesting to compare Table 3 (planned staffing) with Table 2 (existing staffing): the lat-ter has obvious hours of under- and over-staffing while the formers' performance is rather stable. (For example, n(Current) implies understaffing during 16-17 and overstaffing for 22-23 period.) Preplanned staffing, either for resource utilization (RCCP) or, better yet, patients' service level (offered load), clearly has its merit.

Table 3 – Simulation performance measures (using OL and RCCP)

Performance measures using

OL recommendation Performance measures using

RCCP recommendation Hour Ip Sp Op N Bed Chair %W Ip Sp Op N Bed Chair %W 16-17 62% 38% 40% 58% 36.0 29.0 56% 90% 54% 60% 59% 38.3 35.3 78% 17-18 59% 33% 35% 67% 34.8 31.6 36% 82% 47% 65% 81% 39.3 40.2 82% 18-19 75% 49% 53% 76% 32.2 29.9 46% 80% 45% 69% 92% 40.6 46.2 86% 19-20 84% 48% 57% 80% 31.5 31.1 38% 72% 43% 79% 97% 42.3 52.2 90% 20-21 76% 52% 65% 71% 28.7 28.4 38% 68% 46% 85% 99% 43.4 57.7 91% 21-22 83% 49% 59% 75% 27.8 27.9 42% 55% 45% 89% 99% 44.7 62.4 91% 22-23 85% 45% 50% 73% 25.7 25.4 50% 63% 39% 87% 99% 45.9 64.9 91%

4.7 Integration with decision-support system in ED

In order to provide to decision makers (e.g. ED department manager) access to our solution, integration between our simulation and a Decision Support System (DSS) should be performed. Collecting real-time data from various sources can give a snapshot of the current situation and by using the methodology above, such a system can provide predicted information based on the cur-rent ED state. Then this information can be presented to the decision maker in various ways. For example, the ED manager can use this system for trying to avoid a future possible lack of re-sources (e.g. physicians, beds, nurses, etc.).

Based on the above methodology, we were able to develop a DSS that presents, in a graphical interface, several important measurements of current and predicted factors of the ED (see Figure 4 and Figure 5). In fact, input to our system originates from numerous data sources. For example, ED current state is based on information from a multitude of hospital IT systems such as the Ad-mit Discharge Transfer (ADT) system, the Picture Archiving and Communication System (PACS), the Lab Order Reservation system and the Electronic Medical Records system. Yet these systems provide only minimal operational information such as start and end of an activity. In particular, no information on queue lengths or waiting times is available therefore raising the need for our simulation-based capabilities of ED state completion and prediction. In the future, an increasing number of data sources will provide more and more information about the current state of the ED. A very significant upgrade of data collection capabilities can be achieved by the in-

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corporation of an RFID system (see Section �5) that will provide information about location of pa-tients, physicians, equipment, etc.

The hospital IT system collects its information and presents it to the user as a set of indicators

and parameters. To interact with this hospital system, we have designed InEDvance (Greenshpan et al. 2009): a decision support system that can record, process, simulate, and present event data that hospital IT systems record and send, along with current and forecasted performance meas-ures. The InEDvance system comprises algorithms that assist the ED manager in planning re-sources allocation for the next several hours, in order to handle forecasted resource scarcity. In particular, InEDvance has, at its core, a simulation-based module that is fed (in real-time) data from the hospital IT systems and then, through simulation (as described above), identifies and presents patients flow bottlenecks (e.g. excessive lines at the X-Ray) and consequently alert ED management.

The information arriving from the various IT systems generates a dashboard of past, present and predicted activities within the ED. We sample-demonstrate the use of such a dashboard by combining it with our ED simulator, and graphically presenting (potentially in real-time) infor-mation on the dashboard, using a graphical user interface. Figure 4 shows a snapshot of the dashboard that presents, in various ways, past and current occupancy of the different ED rooms. Figure 5 demonstrates a dashboard tab that could alert, based on calculated forecasting indicators, against predicted congestion and resource shortage.

5 SIMULATION-BASED MODELING FOR SCENARIO VALIDATION: VALUE ASSESSMENT OF RFID TECHNOLOGIES IMPACT

5.1 Research goals and description of technologies

In this section, we consider the validation of RFID technology implementation in ED. It is obvi-ous that the actual introduction of RFID technology is costly and demands thorough re-design of ED processes and IT system. Therefore, there is a strong need to estimate benefits and costs of such implementation in advance and using relatively inexpensive evaluation procedure. The

Figure 4 - Dashboard snapshot showing rooms occupancy Figure 5 - Predicted arrivals and physicians load

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simulation based modeling is a natural answer to this challenge. In this section, we consider im-plementation of two alternative existing RFID technologies: WiFi (802.11) and short range pas-sive RFID. WiFi (see Emory and Leslie 2005, for example) is currently the most standardized and usable indoor wireless communication technology. Simple location tracking mechanisms can be built on top of an existing WiFi infrastructure. WiFi is designed to cover wide areas such as the overall hospital campus; hence, it can provide wide location tracking capabilities. The location tracking precision of WiFi, on the other hand, is poor. Naïve implementation uses the tag only for access point association and hence provides only room level resolution. Such installations may have also difficulties in distinguishing locations within two adjacent hospital floors. WiFi is based on active tag communication hence provides continuous location tracking. Passive RFID systems, on the other hand, offer very accurate location tracking, as tags can be identified only within short distances from the reader. The limited coverage issue can be resolved via additional readers, and by placing readers in designated frequently-accessed spots such as doors, pathways, mobile medical equipments (e.g. ECG machine) and patient beds. A significant advantage of passive RFID system is low tag cost. Passive RFID tags are disposable and require little to no maintenance. Thus, widespread deployment is more likely because tags can be given to patients, caregivers, families and visitors with little significant additional cost. Tags within a Passive RFID tags can be identified only during the reading transaction itself, hence they do not render continuous location tracking and monitoring.

5.2 Required process changes

As the first step, it is necessary to define how various components of the ED processes will change given the new data provided by RFID. In addition, it is necessary to define which meas-ures, or metrics, will improve due to the process change(s). It is important to specify the metrics that are expected to improve since only through these quantitative metrics, can the value of the RFID system be estimated (or the values of several RFID alternatives be compared) – see Section �5.3. In general, there are three different types of metrics: clinical, operational and financial. In this research, we explore operational metrics that measure the operational efficiency of the ED and emphasize their relation to clinical and financial metrics. Average Length of Stay (ALOS) is an important example of an operational metrics. ALOS is the average amount of time a patient spends in the ED before either being released from the hospital or being admitted to a ward; one could account separately for patients who "left" due to other reasons, for example death or LWBS (see Fernandes, Price and Christenson 1997). Another important operational metric is workload - the average amount of work-time required from the staff, or a subset of it (nurses, physicians), quantified as a function of time. Note that the three above-mentioned types of metrics are interdependent. For example, if a patient waits for a long time before first examination by a physician, this may adversely affect an operational outcome such as ALOS which, in turn, could result in clinical deterioration, hence in-creased workload (more care required by the staff), and additional costs. For concreteness and demonstration purposes, we have chosen three ED processes whose im-portance for our hospital was established, for assessing the value of their improvements: � From the operational point of view, implementing an alerting RFID system will help reduce

unnecessary waiting times. We focus on patients who are "forgotten" in two Imaging areas: (a) in a remote CT area after completing their scan. Based on practice, we are assuming that

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25% of such patients experience an average of one hour waiting before returning to the ED, when compared against an average of 10 minutes for regular waits. (b) the patients that are waiting after an X-Ray scan. Here "forgotten" patients wait just half an hour instead of the regular 10 minutes. (The X-Ray is relatively close to the ED and easier to locate "forgotten" patients at.)

� From the financial point of view, using patients RFID prevents abandonments of unregistered patients, and thus increases ED's turnover rate and, in turn, enhances hospital income. In es-sence, we measure the operational metrics – LWBS fraction, which in its turn can help to de-termine the increase of hospital income.

� From the clinical point of view, using staff (nurses, physicians) RFID exposes physical layout problems, such as poor placement of rooms or equipment in the ED, which have adverse clinical consequences. Again, a related operational metrics – staff walking distance, is consid-ered. Excessive walking distances would indicate physical layout problems.

5.3 Simulation experiments

To evaluate the benefits of using an RFID system for our three example processes, we have used an ED simulation model, described in Section �0, and programmed it to process six types of pa-tients: Orthopedic, Surgical, and Internal, each in two conditions – Walking and Acute (those in need of a bed). In addition, we made changes to the simulation in order to accommodate the ex-pected impact of the two RFID technologies that are tested. For the process improvement, based on tracking abandonment, we made the following as-sumptions: � Since data of actual abandonment times is currently unavailable, we distributed 4% abandon-

ment (historical average for LWBS fraction) over five process steps: (1) waiting for a nurse to take patients anamnesis; (2) waiting for a physician's initial diagnosis; (3) after the physician's first examination and before sending additional tests; (4) while waiting for a physician to col-lect all the relevant data for further evaluation; (5) after further evaluation, while waiting to be released, hospitalized or for additional intensive tests.

� We assumed that WiFi technology identifies 100% of the abandonments and feeds those pa-tients back into the process. Passive RFID, on the other hand, succeeds in only 50% of the cases. The difference arises because some patients would not abandon with their tags, while others might use vehicles, just as an example, to circumvent the passive sensors near the gates, which otherwise would detect them.

� Abandoning patients are not included in calculating lengths of stay, and they are naturally ex-cluded from those who contribute to hospital profit.

For the process improvement, dealing with reducing waiting times in the Imaging (CT or X-Ray) wards, we made the following assumptions and modifications: � CT patients are waiting to return to the ED. Return timed is within 10 minutes for 75% of the

patients and an hour for the rest. � Passive technology is more effective than WiFi in this case: Passive technology accurately

tracks room relocations and, therefore, gives rise to 100% reduction of the waiting time to 10 minutes. WiFi, on the other hand, reduces waiting times of only 50% of those who are expect-ing prolonged 60 minutes waiting.

� Of the delayed X-Ray patients, 20%, on average, are waiting 10 minutes and the others 30 minutes.

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The Passive and WiFi systems were compared against two additional scenarios: an "ideal RFID system" and the prevailing situation without RFID. An “ideal RFID system” combines benefits of the Passive and WiFi systems: it succeeds to identify 100% of abandonments and tracks all customers who are forgotten at CT and X-Ray. Since the RFID influence on the two processes is intertwined (less abandonment can imply larger workload and waits), we decided also to check the influence of two changes (reduction of waiting times and preventing LWBS) separately. One week was used for simulation warm-up and three months of data for analysis, eleven simulation runs were performed for each case. Table 4 provides us with a summary of simulation results. It includes simulation averages for overall number of patients and LWBS patients, ALOS estimate, standard deviation of ALOS es-timate �(ALOS), based on variability between 11 simulation runs, and finally, �(LOS) – standard deviation of individual customer LOS. We shall analyze Table 4 data from several points of view. Average Length of Stay. Comparing the first three lines of Table 4, we observe that ALOS de-creases once we reduce waiting times in the Imaging Units. Consistently with the story above, Passive RFID technology implies more significant improvement than WiFi. In contrast, LWBS reduction or elimination increases ALOS. Since patients are fed back into the process, congestion increases to a certain extent. (Garnett, Mandelbaum and Reiman, 2002, analyze such operational consequences of abandonments.) Finally, given the full implementation of RFID solution (Imag-ing waiting decreases and LWBS is reduced), Passive RFID provides ALOS that is slightly smaller with respect to the basic state, while WiFi implementation leads to ALOS increase.

Table 4 – The simulation results: comparison of different RFID systems RFID

System Number of pa-

tients (3 months) LWBS

(3 months) ALOS �(ALOS) �(LOS)

Without RFID 24,037 945 (3.9%) 178.9 0.9 128.4 WiFi, wait reduced 24,012 951 (4.0%) 175.3 0.9 127.9

Passive RFID, wait reduced 24,001 949 (4.0%) 172.1 0.7 127.3 WiFi, LWBS eliminated 23,977 478 (2.0%) 190.7 0.8 135.8

Passive RFID, LWBS reduced 24,026 0 184.6 0.9 132.7 WiFi 23,987 475 (2.0%) 186.8 0.9 133.9

Passive RFID 24,087 0 177.2 0.7 128.9 Ideal RFID (Passive + WiFi) 24,118 0 184.2 0.7 133.6

Number of LWBS patients. Table 4 shows that, in our simulation experiment, RFID technol-ogy fed back significant number of patients into the process. It should have both positive clinical and financial impacts: LWBS patients often return to an ED when their condition deteriorates; we also block attempts to leave the ED without providing payment guarantees. Another operational aspect of RFID implementation is captured by the intra-day staff work-load, displayed in Figure 6. We calculate the workload in order to check that implementation of RFID will not lead to any unexpected operational phenomena. (Physicians that treat Internal Acute patients are chosen for this example). We observe that differences between RFID scenar-ios are not too large.

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Another dimension that we checked is the physical layout of the ED. From the simulation, we found that orthopedic physicians are walking about 2 kilometers per shift, between the walking-patients area and the acute area (most times, there is just one orthopedic physician available for both locations. A second one would join from the orthopedic ward, when needed). Further inves-tigation revealed that the distance between the two locations was excessive (about 100 meters) and the hospital managers took it into account in a redesigned ED. (See also Section �6.4 where the issue of excessive walking distances is discussed.) With the distance being that long, both WiFi and Passive systems identified (and could quantify) this problem easily. (WiFi, however, is less appropriate for measuring short-distance movements.) Considering all three aspects (clinical, economical, operational), which RFID solution should the manager implement? In our case, Passive RFID technology seems to be a reasonable option: it does not increase overall ALOS and prevents significant number of customers from abandon-ment. In addition, it is much less expensive than the WiFi implementation. However, in general, there is no clear-cut answer to this question. A decision-maker should take into account simula-tion results (especially ALOS, bed utilization and LWBS), implementation costs of different so-lutions, revenue from abandonment blocking, hospital preferences etc. In the ideal case, the cost/revenue optimization problem should be solved. However, it is not always easy to quantify financial impact of ALOS decrease or increase, often the impact depends on the staffing-level changes that can be implemented due to the change in the workload. Our simulation model can help to answer such sort of questions.

5.4 Integration with decision-support system: RFID-based control views

The contribution of an RFID system to a hospital's environment should encompass two main as-pects. First, RFID should have impact on daily routine and hospital staff; second, long-term im-

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pact for strategic planning is desirable. Both aspects are implemented in the Decision Support System, introduced in Section �4.7. The system was designed on an IBM Cognos BI.

Examples of interfaces with the processes in Section �5.2 will be now demonstrated. Online View in Figure 7 supports real-time decisions by hospital staff and executives depicting detailed events of hospital processes. These events contain information about specific patients, staff and services provided by the hospital. For our demonstration, we used our main discrete-event simu-lator. Figure 7a demonstrates how such an “online view” alerts on extreme waiting times of pa-tients after CT services (the first process discussed in Section �5.2). Figure 7b demonstrates how a decision-maker is alerted on the presence of patients who attempt to abandon the ED (the second process discussed in Section �5.2), together with details of the process they have undergone until their abandonment attempt.

Figure 7. Online view showing: a) patients waiting time for CT services b) patient abandonment

The second “Offline View” in Figure 8 should be used for supporting long term planning. There-fore, it shows high-level details, aggregated over a pre-specified horizon. This view is to be used for high-level understanding and analysis of hospital processes, for example staff workload, qual-ity of service, impact of decision-making and planning etc. Figure 8a displays patterns of patients arrivals rate over hours of a day and along days of week. It also highlights the magnitude of the gradient, thus pointing at the times of day when pattern-changes are the most significant. In such a view, we display averages over a year, which are to be used for planning and assessment of strategic and longer run tactical decisions. Figure 8b depicts workload on physicians at the hospi-tal, through the analysis of patients waiting time for service – excessive waits could trigger an alert.

Figure 8. Offline view showing: a) Averaged patient arrival counts during daytime, in each of

the weekdays b) Averaged patient wait time for physician

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6 SIMULATION-BASED MODELING FOR ED RE-DESIGN UNDER PHYSICAL RELOCATION

6.1 Background

Managers of the Israeli hospital came to conclusion that the previous ED could not provide a suf-ficient service level given increasing load and growing demands to clinical and operational ser-vice levels. Therefore, the decision to design and construct a new ED was taken, in order to cre-ate more comfortable environment for patients and hospital staff. The ED transfer is implemented in two stages. First, in 2008, the ED was transferred to temporary location at the basement of the hospital. In 2010, a new permanent ED will be opened at the same location as the previous ED. Both transfers increase uncertainty concerning many issues related to ED func-tioning. Two undergraduate student projects were performed in order to help hospital manage-ment to deal with this uncertainty and provide recommendations on the issues that are still open. Here we concentrate on the first project that was dedicated to transfer from the previous location to the temporary location.

In both locations, patients were classified either as Acute or as Walking. The process struc-ture is similar for both types of patients. First, they are sent to triage, classified as Internal, Or-thopedic or Surgical (Orthopedic and Surgical patients are referred to as Trauma patients) and then transferred to the corresponding room of the ED. Here a nurse performs initial checks and a physician provides an initial assessment. The patient is then sent to additional tests if needed (X-Ray, special blood tests etc). When the results of the tests arrive, the physician assesses the pa-tient once again and, unless additional tests are required, takes decision either on transfer to In-ternal Ward or on releasing the patient home.

The area of the temporary ED is significantly larger than the area of the previous ED (2,000 square meters. versus 1,000 respectively), hence, the problem of large walking distances can po-tentially arise for the temporary ED. The number of physicians and nurses that work in the two EDs had to be the same and, due to large distances between Walking and Acute patients of Or-thopedic and Surgical types, there was a special need to evaluate the walking distances of physi-cians that had to treat both Walking and Acute patients.

In addition, the nurses scheduling had to be changed after the transfer. In the previous state, different teams of nurses treated Internal and Trauma Walking patients. Moreover, all Trauma pa-tients (both Acute and Walking) were treated by the same team. In the temporary ED, all walk-ing and acute patients are concentrated at the same location (so-called ambulatory ED) and a sin-gle nurse team treats them. Therefore, the need to compare different configurations of nurse teams arose.

Another important problem was related to the X-Ray unit. This is an important issue since approximately two thirds of ED patients are sent to X-Ray check. In the previous location, ED had its own X-ray room that functioned between 8am and 2pm. During these hours, 42% of ED patients that had to perform the check, were sent to this room and the others were sent to the ex-ternal X-Ray room that gave service to all hospital wards. During the period when the ED X-Ray room was closed, all patients were sent to the external X-Ray. In the temporary state, it was planned initially to prolong the working hours of ED X-Ray room (to 10 hours or even to 24-hours-per-day). Validation of this preliminary decision has been an important issue.

In order to explore these challenges, our main simulation model (Marmor and Sinreich, 2005, 2006) has been used.

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6.2 Nurse staffing in the temporary ED

The ambulatory ED room brought an important change in the temporary ED design with respect to the previous state. In the previous state, Walking Trauma patients were treated by the same nurses as Acute Trauma patients. In addition, Walking Trauma and Walking Internal patients were located in different areas. In the new ambulatory ED room, all Walking patients are located together and are treated by the same nurse team. Initially, it was assumed that the team dedicated to Internal Walking patients in the previous state would be able to treat all Walking patients in the ambulatory room. This team consisted of a single full-time nurse and the second nurse, added during several high-loaded hours only. Our simulation analysis demonstrated that, in order to sus-tain a reasonable service level under new conditions, it is necessary to add the second nurse dur-ing most hours of the day.

Table 5. Average Length of Stay under different scenarios in ambulatory room

Patient type

Second nurse added 9 hours/day

Second nurse added 18 hours/day

ALOS, min CI ALOS, min CI

Difference is statistically significant

Acute Internal 441.20 128.78 438.31 98.49 No Acute Surgical 161.67 33.18 165.54 33.76 No

Acute Orthopedic 170.46 17.96 173.03 32.86 No Walking Internal 455.70 174.78 272.75 107.72 Yes Walking Surgical 328.38 62.88 176.97 14.96 Yes

Walking Orthopedic 392.05 90.46 194.72 47.48 Yes Overall 381.59 276.95

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Table 5 compares two cases: the second nurse is added in the Ambulatory room for 9 and 18 hours, respectively. In the second case, two nurses are working in the unit during all hours of day, except 5am-11am period. The difference with respect to ALOS of Walking patients and, hence, overall ALOS is striking. (CI is the width of 95% confidence intervals.) However, even after the second ambulatory nurse is added for 18 hours per day, the nurse team that treats Walking patients remains overloaded. Figure 9 illustrates this phenomenon, com-paring workload per nurse of the three nurse teams. (There are four nurses in the Acute Internal team, three nurses most of the day in the Acute Trauma team and two nurses most of the day in the ambulatory Walking team.) Note that around 12:00-13:00 the workload of the ambulatory nurses is too close to one: nurses would work in a heavily overloaded regime probably implying undesirable consequence from the operational service-level and clinical points of view.

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Since additional workforce was not available, it was decided to transfer a nurse from one of other two teams to the Ambulatory team. Transfers from Acute Internal and Acute Trauma were modeled via our simulator, it turned out that the transfer from Trauma team is slightly more preferable. (This conclusion could be expected from Figure 9, where we observe that the Acute Trauma team is the least loaded one.) Figure 10 shows the workload of nurse teams after the transfer: now the load on Walking team is reasonable. Following this research, our specific recommendations (nurse transfer from Trauma unit to Ambulatory unit and addition of the second Nurse to Ambulatory unit for 18 hours per day) were actually implemented.

6.3 Design and scheduling of X-Ray rooms in the temporary ED

In the previous state of the ED, the X-Ray room was open between 8am and 2pm, a single tech-nician worked there during this period. As mentioned above, even between 8am and 2pm, many

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patients were sent to the external X-Ray unit. The number of technicians at the external unit is time-varying, reaching maximum of four between 8am and 3pm. In our simulation experiments, we started with the comparison of several options for ED X-ray room opening hours. We compared between 8am-6pm, 8am-2pm (current practice), 12am-6pm and also considered closing ED X-Ray room and sending all patients to the external ED unit. During the ED X-Ray opening hours, the patients were sent to the external unit if the queue length in the ED X-Ray reached eight. (Unless such threshold routing rule was introduced, the queue to the ED X-Ray exploded. The specific threshold value was selected using simulation experiments. It turned out that such policy minimizes average waiting of patients at the ED X-Ray.) The mean queue at the external X-Ray unit was compared under several scenarios. It turned out that 12am-6pm opening hours at the ED X-Ray unit provide a good service level: the mean queue length during the day never exceeds two patients. In contrast, for 8am-2pm opening hours, the mean queue at the external X-Ray exceeded eight patients around 4-5pm. This phenomenon can be explained by the high load on X-Ray units during late afternoon hours: if the ED X-ray is closed during these hours, the load on the external X-Ray is very high. Then we compared the average utilization of the ED X-ray room under two scenarios (8:00-14:00 and 12:00-18:00 opening hours). It turned out that the utilization under the second scenario is more stable, slightly changing around 90%. According to the first scenario, the X-Ray will be underloaded in the early hours of the morning.

Table 6 - Waiting times of patients in ED X-Ray room, min

ED X-Ray Room opening hours Patient type 8:00-14:00 12:00-18:00

Internal Acute 16.03 22.56 Surgery Acute 15.28 23.37

Orthopedic Acute 17.29 23.00 Internal Walking 16.05 22.29 Surgery Walking 18.00 21.46

Orthopedic Walking 17.10 21.64

Table 6 compares between mean waiting times of all patient types at the ED X-Ray room. It turns out that patients wait slightly longer for 12:00-18:00 scenario. However, if we compare overall mean waiting times, taking into account both X-Ray units, the picture is very different.

Table 7 - Waiting times of patients in both X-Ray units (internal and external), min

ED X-Ray Room opening hours Patient type 8:00-14:00 12:00-18:00 8:00-18:00

Internal Acute 12.25 8.93 10.02 Surgery Acute 18.94 12.57 14.17

Orthopedic Acute 25.91 18.35 20.28 Internal Walking 8.52 5.52 6.17 Surgery Walking 13.80 8.83 9.94

Orthopedic Walking 21.10 14.78 17.22

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Table 7 demonstrates that 12:00-18:00 opening hours is clearly the best option with respect to overall patient waiting times. Since this policy has other attractive properties (smaller workload at the external unit, stable workload at the ED unit), it was the one practically implemented.

6.4 Summary of suggested changes in the temporary ED and estimate of the expected gain

In addition to the changes in nurse staffing and X-Ray scheduling, other recommendations were provided in order to improve functioning of the temporary ED. Below we summarize the two most significant recommendations: � Pooling treatment policy was suggested for the physicians that treat Internal Acute patients.

The pooling policy suggests that each Internal physician can treat each patient in the Internal Acute room. The alternatives to this approach suggest that responsibility on different groups of Internal Acute patients is divided between different physicians.

� Surgery and Orthopedic physicians treat both Acute and Walking (Ambulatory) patients. It turned out that the walking distance between the Ambulatory Room and the room were Acute Trauma patients were located is relatively long. Our simulations have shown that walking time for these types of physicians can reach 6.5% of overall time that they spend in the hospi-tal. In order to eliminate this undesirable factor, we suggested a new design of the temporary ED, which significantly decreased the walking distances of the physicians. In addition, we de-creased the walking distances of the nurses via optimal locations of the shelves with the medicine.

Table 8 - Average Length of Stay under different ED design

Patient type Internal physicians are not pooled

Internal physicians are pooled Optimal design

ALOS, min ALOS, min ALOS, min Acute Internal 489.66 438.31 389.64 Acute Surgical 167.60 165.54 157.01

Acute Orthopedic 172.00 173.03 166.98 Walking Internal 264.70 272.75 212.07 Walking Surgical 166.20 176.97 123.49

Walking Orthopedic 185.74 194.72 144.11 Overall 286.30 276.95 230.62

Table 8 displays ALOS that has been calculated using our simulation models in the three cases. The first column display ALOS in the case when Internal physicians are not pooled (several non-pooled working protocols were compared and here we display the output of the one that gives the best results). The second column shows the state after pooling was performed, we observe that ALOS of Acute Internal Patients decreased significantly. Finally, the third column displays ALOS for the optimal state, where all our suggested changes were performed. We observe a very significant improvement of service level for some patient types with respect to Pooling scenario (from 3.5% for Acute Orthopedic to 30.2% for Walking Surgical).

Number of beds that is needed in an ED is an additional important operational metrics. It turns out that our re-design improves this metrics significantly. For example, consider the fol-lowing metrics: number of beds in the Internal unit that is enough to absorb the load 95% of time.

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Our simulation experiments demonstrate that this metrics decreases from 43 to 36 beds on week-days and from 28 to 26 beds on weekends.

7 CONCLUSIONS AND POSSIBLE FUTURE RESEARCH

This paper demonstrates that a single well-designed simulation model of an Emergency Depart-ment can be instrumental in the solution of ED problems in very different domains: strategic planning, operations planning and real-time control. It also emphasizes the need for real-time De-cision Support System implementation in Emergency Departments. This system should receive data from hospital IT systems (connected to RFID, in the ideal case), and rely, in addition, on ED simulation and relevant analytical models. Using this tool, ED decision-makers will be able to evaluate the current ED state, perform operational planning based on short-term forecasts and, in addition, use the system capabilities for strategic planning. Since this research covers several heterogeneous topics, many future research directions can arise out of it. Here we briefly characterize some of these research issues. � Further research on RFID implementation in ED. In Section �5, we presented a case study on implementation of RFID technology. However, an actual full-scale RFID implementation af-fects many aspects of Ed functioning and we explored only several of them. Therefore, in order to take final decision on actual full-scale implementation of RFID, more comprehensive simula-tion study is needed. � Cooperation with the Israeli hospital on ED redesign under physical relocation. As men-tioned above, the ED of our hospital will be moved to the new permanent location in 2010. Nu-merous practical and research challenges arose prior to this relocation. For example, multi-skilled EM (Emergency Medicine) physicians will work in the new ED, in contrast to the previous state, when Internal and Trauma patients were treated by different physician teams. We performed analysis of different aspects of this transfer and cooperation between our research teams on this issue will be continued. � Integration between ED simulator and hospital data repository. The Service Engineering Enterprise (SEE) Center at the Faculty of Industrial Engineering and Management in the Tech-nion has created repository of Service Engineering projects. It is based on the DATAMOCCA (Data Model for Call Centers Analysis, see Trofimov et al. 2006). This model provides uniform presentation of data from various sources for statistical analysis, operational research and simula-tion. Initially designed for call centers data storage and processing, this model was generalized to accommodate other types of data, including healthcare data. Now the repository contains data from ED and Internal Wards of several hospitals. In order to increase the processing speed, databases are designed in two levels, containing as the second level the precompiled summary tables, which are created once and are efficient enough to support real-time few-seconds processing. This provides an environment that is suit-able for real-time statistical analysis and simulations. The software for statistical algorithms (in-cluding distribution fitting, fitting of distribution mixtures, survival analysis etc.) has been devel-oped and connected to the databases.

Data from the described repository can be used by our simulation model and statistical capa-bilities of DATAMOCCA should be integrated into simulator. Note that enhancement of data-collection methods (using, RFID, for example) will increase� benefits of such integration. For example, estimates of service times for nurses and physicians will be derived from the database, while now the field studies are often needed in order to incorporate them into the model.

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Acknowledgements. This work would not have been possible without the hard work and dedica-tion of the following people: Prof. Rafi Beyar, the head of the Rambam hospital; Oded Cohen, head of IBM Haifa Research Lab; Prof. Boaz Golany, Dean of Technion's IE&M Faculty; The Open Collaborative Research project steering committee, first and foremost the Rambam Mem-bers - Prof. Shimon Pollack, Dr. Yaron Barel, Dr. Hana Adami and Amir Weiman; The dedicated professionals in the Rambam ED and trauma room, led by Dr. Moshe Michaelson and head nurse Hagar Baruch. We are deeply thankful to Technion undergraduate students Kamel Badran, Rasha Khawaly and Inbal Haas. The outputs of their project, performed under the supervision of Yariv Marmor, were used in Section �6.