Leadership & Management

Simulation Models for Just-in-Time Provision of Resources in an Emergency Department

Simulation Models for Just-in-Time Provision of Resources in an Emergency Department
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  Electronic copy available at: http://ssrn.com/abstract=1154163 Simulation Models for Just-in-Time Provision of Resources in an Emergency Department May 24, 2008 Larry J. LeBlanc, Ph.D. (Corresponding Author) 1  Nathan R. Hoot, Ph.D. 2  Ian Jones, MD 2, 3  Scott R. Levin, Ph.D. 4  Chuan Zhou, Ph.D. 5 Cynthia S. Gadd, Ph.D., MBA 2  Brent Lemonds 6 Dominik Aronsky, MD, Ph.D. 2, 3 1  Larry.LeBlanc@Owen.Vanderbilt.edu Owen Graduate School of Management Vanderbilt University Nashville, TN 37203 USA Phone 1 615 322 3662 Fax 1 615 343 7177 2  Department of Biomedical Informatics, Vanderbilt University Medical Center 3  Department of Emergency Medicine, Vanderbilt University Medical Center 4  Department of Emergency Medicine, Johns Hopkins University 5  Department of Biostatistics, Vanderbilt University Medical Center 6 Department of Emergency Services, Vanderbilt University Medical Center Vanderbilt University Nashville, TN 37232 USA Key Words: Health Care, Emergency Department, Simulation, Operations Control 1 I  Electronic copy available at: http://ssrn.com/abstract=1154163 2 Abstract  We address the issues for modeling problems of obtaining medical resources, such as beds and personnel, on a just-in-time basis for providing patient services in a hospital's emergency department [ED]. Simulation modeling is one approach to solving this type of problem. However, most past ED simulations have been highly detailed models that specifically consider individual doctors, nurses, paramedics, orderlies, X-ray machines and other unique types of lab testing equipment as separate “resources.” When arriving patients request any of these resources, the model queues them if the requested resources are busy with other patients or administrative duties. We argue that these previous simulation models are appropriate for operations planning. However, for operations control a much simpler simulation can be appropriate. We present a simpler model that forecasts in real time when a generic ED will need additional resources to avoid becoming overcrowded. After obtaining an ED bed (queuing if necessary), each patient's treatment time in the ED is represented simply as an observation of a random variable. Although our model is significantly simpler than previous ED simulation models, it has been used successfully by the Vanderbilt University Adult Emergency Department to activate additional resources, including beds, nurses, and other personnel. This ED is located within a state-of-the-art medical center in a large urban area licensed to treat the highest levels of trauma with more than 50,000 patient visits each year. Our model was used in a pilot evaluation of simulation as a real-time tool for providing additional resources on a JIT basis to prevent significant overcrowding by accurately predicting when overcrowding will likely occur. This has allowed management in real time to inform nurses, assistant managers and educators with joint administrative/patient care duties to work exclusively on patients; to open additional temporary beds in hallways; and to expedite patient discharges. It has also been used to let flexible night-shift staff go home early. Since these resources are mobilized only when needed, their overall  3 efficiency of utilization is increased. We discuss the simplifying assumptions in our simulation model and describe a comparable simulation written in a high-level simulation language, Arena [2008]. 1. Introduction Operations management models can contribute to delivering more efficient health care and increase the quality of patient care. However, selecting the most appropriate type of model within a specific context is a challenging research question. An increased focus on better managing of emergency departments [EDs] has become particularly important since the Emergency Medical Treatment and Active Labor Act [1986] became federal law. The law mandates that a physician provide a medical screening exam to all patients seeking care in an ED, irrespective of the patient’s payment source or medical condition. This legislation strains the operational capability of EDs that already face considerable challenges such as a nationwide nurse shortage, hospital closures, an aging population, increased patient volumes, and decreasing reimbursement levels. In its 2006 report, the Institute of Medicine [2006] notes that, on average, ambulances are turned away from emergency departments once every minute in the United States. Patients in many areas may wait hours or even days for a hospital bed. Although the law intends to improve patients’ access to care it may lead to unintended consequences such as ED overcrowding, which has been associated with worsened health outcomes, decreased safety, and lost potential revenues [Hwang, Richardson, Sonuyi, Morrison, 2006; Richardson, 2006]. The just-in-time mobilization of resources has the potential to decrease health care costs while still providing patients with appropriate care. For example, suppose the demands of a continuously-increasing population outstrip the resources of a 25-bed ED, resulting in a proposal for an increase in capacity by 5 beds. This is illustrative of the reactive manner in which EDs operate. Due to hourly and daily fluctuation of patient volumes, all beds are needed only  4 periodically during the busier times; however, when they are not available during peak times, patient safety is severely jeopardized. Prior work has shown that realizing a capital expansion by adding five additional beds would provide temporary relief but does not provide an adequate, flexible, and just-in-time solution [Han, Zhou, France, Zhong, Jones, Storrow, Aronsky, 2007]. Real-time forecasts of ED status several hours into the future would allow an ED to mobilize resources proactively. For example, hospital managers could call in extra personnel and open hall beds that will be ready and staffed when new patients arrive. This may improve the utilization of existing resources with or without capital expansion. An ED cannot simply wait until all beds are occupied before opening hallway beds, since it takes time to open and staff them before they can accommodate patients. To make these locations suitable for patient care, each one must be equipped with an oxygen tank, IV pole, privacy curtain, a method to call a nurse, and ideally a monitoring device. Also, beds cannot be located permanently in the hallways, since they impede staff in their patient care duties and violate the fire code, which requires an eight-foot corridor width. It is essential that hallway bed preparation be done before reaching full capacity, since preparation consumes staff time that cannot otherwise be spent at the bedside of ED patients. However, simply opening hallway beds when nearly all beds are full is not wise, since the number of full beds might decrease, meaning the hallway beds are not actually needed. A real-time forecasting model could give the necessary lead time to open hallway beds and call in personnel to an ED that will reach full capacity in the near future. 2. Literature Review  The literature on operations management applications to health care is too large to survey in its entirety. Instead, we simply give an overview, concentrating on past applications of simulation. de Treville, Smith, Rölli and Arnold [2006] showed that lead-time reduction techniques borrowed from manufacturing can significantly improve health care delivery. Gowen, Mcfadden, Hoobler, and Tallon [2006] surveyed directors of hospital quality  5 programs and found that perceived quality program results are more highly related to employee commitment and control initiatives than to quality practices. Goldstein and Naor [2005] argue that organizations are not purely public or purely private but have a continuum of publicness. They show a linkage between operations-related quality practices in U.S. hospitals and ownership and control. Chesteen, Helgheim, Randall and Wardell [2005] compare non-profit and for-profit nursing homes. They conclude that although there is no direct link between non-profit status and outcome quality, process quality is higher at non-profit nursing homes. Simulation models are popular for managing EDs and have been applied previously for planning and analyses purposes; however simulation models have not been applied for real-time forecasting purposes. A commercial product (Apogee Informatics Corp. [2008]) provides a subscription-based patient flow simulation for EDs. In this interactive simulation, patients flow through triage, lab testing, radiology, and waiting for examinations. (“Triage” is an acuity level ranging from 1 to 5, with 1 indicating a life-threatening condition and 5 indicating the lowest acuity [Tanabe, Gimbel, Yarnold, and Kyriacou, Adams, 2004].) The display resembles a plumbing structure with valves and holding tanks to adjust for process delays and queues. Once an ED model is tuned to emulate the actual ED flow accurately, simulated tests or experiments can be run to determine the downstream effect of changes to the system. For example, an ED with one triage nurse assigned to the 3 PM to 11 PM shift can test the impact on the waiting room length of stay if a second nurse is added to that shift. Hung, Whitehouse, O’Neill, Gray, and Kissoon [2007] developed a simulation model of a pediatric ED using Arena [2008]. Their model is also highly detailed, specifically considering pre-triage, triage, registration, and acute care level events. The authors found the model useful for modeling the addition of individual physicians, nurses, and volunteer personnel. Connelly and Bair [2004] simulated an ED with individual staff types modeled and assigned appropriate responsibilities with a separate queue for each staff member. They simulated individual patient-care activities,
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