Tutorial in Medical Decision Modeling Incorporating Waiting Lines and Queues Using Discrete Event Simulation

Authors

  • Beate Jahn PhD,

    Corresponding author
    1. Institute of Public Health, Medical Decision Making and Health Technology Assessment, Department of Public Health, Information Systems and Health Technology Assessment, UMIT—University for Health Sciences, Medical Informatics and Technology, Hall i.T., Austria;
    2. Department of Medical Statistics, Informatics and Health Economics, Innsbruck Medical University, Innsbruck, Austria;
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  • Engelbert Theurl PhD,

    1. Department of Public Finance, Leopold-Franzens-University of Innsbruck, Innsbruck, Austria;
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  • Uwe Siebert MPH, MSc,

    1. Institute of Public Health, Medical Decision Making and Health Technology Assessment, Department of Public Health, Information Systems and Health Technology Assessment, UMIT—University for Health Sciences, Medical Informatics and Technology, Hall i.T., Austria;
    2. Cardiovascular Research Program, Institute for Technology Assessment and Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Center for Health Decision Science, Department of Health Policy and Management, Harvard School of Public Health, Boston, MA, USA;
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  • Karl-Peter Pfeiffer PhD

    1. Department of Medical Statistics, Informatics and Health Economics, Innsbruck Medical University, Innsbruck, Austria;
    2. FH Johanneum University of Applied Sciences, Graz, Austria
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Beate Jahn, Institute of Public Health, Medical Decision Making and Health, Technology Assessment, Department of Public Health, Information Systems and Health Technology Assessment, UMIT—University for Health Sciences, Medical Informatics and Technology, Eduard Wallnoefer Center I, A-6060 HALL i.T., Austria. E-mail: Beate.Jahn@umit.at

ABSTRACT

In most decision-analytic models in health care, it is assumed that there is treatment without delay and availability of all required resources. Therefore, waiting times caused by limited resources and their impact on treatment effects and costs often remain unconsidered. Queuing theory enables mathematical analysis and the derivation of several performance measures of queuing systems. Nevertheless, an analytical approach with closed formulas is not always possible. Therefore, simulation techniques are used to evaluate systems that include queuing or waiting, for example, discrete event simulation. To include queuing in decision-analytic models requires a basic knowledge of queuing theory and of the underlying interrelationships. This tutorial introduces queuing theory. Analysts and decision-makers get an understanding of queue characteristics, modeling features, and its strength. Conceptual issues are covered, but the emphasis is on practical issues like modeling the arrival of patients. The treatment of coronary artery disease with percutaneous coronary intervention including stent placement serves as an illustrative queuing example. Discrete event simulation is applied to explicitly model resource capacities, to incorporate waiting lines and queues in the decision-analytic modeling example..

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