A Simulation Model for Resource Balancing in Healthcare Systems

A Simulation Model for Resource Balancing in Healthcare Systems

Arzu Eren Şenaras, Hayrettin Kemal Sezen
DOI: 10.4018/978-1-5225-2515-8.ch004
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This study aims to analyze resource effectiveness through developed model. Changing different number of resources and testing their response, appropriate number of resources can be identified as a basis of resource balancing through what-if analysis. The simulation model for emergency department is developed by Arena package program. The patient waiting times are reduced by the tested scenarios. Health care system is very expensive sector and related costs are very high. To raise service quality, number of doctor and nurse are increased but system target is provided by increased number of register clerk. Testing different scenarios, effective policy can be designed using developed simulation model. This chapter provides the readers to evaluate healthcare system using discrete event simulation. The developed model could be evaluated as a base for new implementations in other hospitals and clinics.
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The growing costs of healthcare are a major concern for healthcare providers. As healthcare organizations move towards the goals of reducing costs, optimizing patient experience, and improving health of populations; operations research tools are becoming more important. These tools provide the ability to assess trade-offs between resource utilization, quality of service, and operating costs (Lal and Roh, 2013).

The Emergency Department (ED) is the service within hospitals responsible for providing care to life threatening and other emergency cases over 24 hours daily, 7 days a week. Therefore, such departments are highly frequented by patients and this frequency is continuously increasing (Weng et al. 2011, Saghafian et al. 2012, Ghanes et al. 2014).

Emergency Departments (ED) are one of the most complex parts of hospitals to manage, and yet a major entry point for patients. It deals with patients without an appointment and with a wide range of illnesses. Even if most patients arriving to an ED leave the hospital after having seen a physician at the ED, a significant part of them need to be hospitalized. In many hospitals, finding available beds for unscheduled patients is extremely complicated. Even if all patients arriving at the ED do not require the same level of care, many hospitals proceed with the following policy: accept any patient until no bed is available. However, more sophisticated policies, including bed booking strategies and dynamic decisions, can lead to significant improvement of overall hospital performance (Prodel et al., 2014).

Discrete event simulation (DES) is one of the most commonly used operations research tool in healthcare. Its unique ability to account for high levels of complexity and variability that exist in the real world, along with animation capability makes it easier to illustrate and gain buy-in from physicians and other clinical providers compared to other black-box mathematical models offered by operations research. However, DES also has some limitations. In scenarios where there is a large number of stochastic input decision variables and there is little information about the structure of output function using simulation modeling by itself can be tedious and complicated. In such cases, optimization via simulation can help to maximize or minimize measures of the performance by evaluating the system using discrete event simulation (Banks et al, 2004).

DES models for healthcare facilities commonly focus on improving wait time, patient flow and management of capacity (Hamrock et al. 2014; Jacobsen et al. 2006). Although DES is adept at modeling the complex queuing structure for patients in healthcare environments, transition process variation driven by organizational and human factors is more difficult to capture mathematically. For example, analyses of patient location data used to construct DES models may find that patients are consistently waiting for servers (e.g., beds, imaging suites, clinicians) at time-points despite their availability. In the DES, queued patients would efficiently shift to open servers. However in clinical practice, transition process factors such as inefficient communication, lack of awareness of server availability, complex administrative guidelines, interruptions, and cumbersome documentation create further delays (Shi et al. 2015; Armony et al. 2010). These delays are not inherent to queuing nor well understood from time-stamped patient flow data alone. To fully capture the dynamics of healthcare facilities or any flow-based socio-technical system, transition process variability should be understood.

Key Terms in this Chapter

Simulation Clock: A variable giving the current value of simulated time.

Verification: Verification pertains to the computer program prepared for the simulation model. Is the computer program performing properly? With complex models, it is difficult, if not impossible; to translate a model successfully in its entirety without a good deal of debugging; if the input parameters and logical structure of the model are correctly represented in the computer, verification has been completed.

Validation: Validation usually is achieved through the calibration of the model, an iterative process of comparing the model against actual system behavior and using the discrepancies between the two, and the insight gained, to improve the model. This process is repeated until model accuracy is judged acceptable.

Discrete Model: Change can occur only at separated points in time.

Computer Simulation: Reproducing the behavior of a system using a mathematical model.

Discrete Event Simulation: DES is the modeling of systems in which the state variable changes only at a discrete set of points in time.

Event List: A list containing the next time when each type of event will occur.

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