gms | German Medical Science

Kongress Medizin und Gesellschaft 2007

17. bis 21.09.2007, Augsburg

Queuing and the economic evaluation of health care interventions

Meeting Abstract

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  • Beate Jahn - Department für medizinische Statistik Informatik und Gesundheitsökonomie, Innsbruck
  • Engelbert Theurl - Department of Public Finance, Leopold-Franzes-University, Innsbruck
  • Karl-Peter Pfeiffer - Department für medizinische Statistik Informatik und Gesundheitsökonomie, Medizinische Universität, Innsbruck

Kongress Medizin und Gesellschaft 2007. Augsburg, 17.-21.09.2007. Düsseldorf: German Medical Science GMS Publishing House; 2007. Doc07gmds178

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Veröffentlicht: 6. September 2007

© 2007 Jahn et al.
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Objectives: Waiting lists are a major health policy concern. Most of the decision analytic models for the economic evaluations of treatment alternatives do not take into account existing and changing waiting lists (queues). This is mainly because of commonly used modelling methods (Markov models, decision trees) and software. Alternative modelling methods (e.g. Discrete-Event-Simulation; DES) provide this feature. When queues are incorporated in the models additional information and model assumptions are required. The paper aims to support conceptualizing such advanced models for health economic evaluations.

Methods: Queuing theory is a branch of applied probability and Operational Research. It aims to elucidate phenomenon of congestions arising from random customer arrival and random service duration. First, queue attributes are introduced based on a modelling example for alternative stent treatment of coronary artery disease. Thereafter, the focus will be on patient arrival. Input data and input modelling using stochastic processes are discussed. The impact of patient arrival on queue length, queuing time, long-term costs and effects (Quality adjusted life years) will be demonstrated.

Discussion: The illustrative model example provides a sense of queue related modelling issues. Because queuing theory is highly complex, only the (stochastic) arrival process will be discussed in detail. In the example the arrival of new patients and patients for repeated interventions is modelled. DES is a suitable technique to build decision analytic models and to incorporate waiting lists.

Conclusion: Decision analytic models can be built according to a realistic setting where waiting lists might exist. If waiting lists (queues) are modelled then the effects of delayed treatment on health and economic outcome can be estimated. Furthermore the impact of different treatment alternatives on the waiting list can be tested. Therefore these advanced models can substantially support decision maker in health policy.