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

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.

Using meta-regression analyses in addition to conventional systematic review methods to examine the variation in cost-effectiveness results – a case study

“…In one case, we found that a full report [ 8 ] and a paper [ 9 ] reported results from the same analyses; data was therefore extracted from the full report. In another case, we found two papers with the same content and results and considered them as one paper [ 10 , 11 ].

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“…Four studies calculated the incremental cost-effectiveness ratio (ICER) for both PES and SES [ 2 , 12 – 14 ], two studies [ 15 , 16 ] focused on PES, three studies focused only on SES [ 17 – 19 ], and one study used ZES as the intervention [ 20 ]. The remaining six publications [ 8 , 10 , 11 , 21 – 24 ] did not specifically identify the type of eluting drug under evaluation and calculated an ICER for a DES in general,…”

The Impact of One-Dose Package of Medicines on Patient Waiting Time in Dispensing Pharmacy: Application of a Discrete Event Simulation Model

“…The theoretic basis and explanation of the DES model can be found elsewhere. [12][13][14] Briefly, the Simul8 enables the replicates of a discrete event or behavior in a real-life process in the computer model, and allows the performance of various experiments on the computer. The model was constructed using the components of Entity, Resource, Activity, Queue, Start point, and End point.…”

“…Discrete event simulation (DES) is a computer simulation modeling technique that provides an intuitive and flexible approach to study complex systems. [12][13][14] The DES is known to be a powerful tool to support evidence-based decision-making in a risk-free environment. It has been used in studies in the field of hospital management, health resource planning, and to improve patient flow and waiting time at hospitals.…”

Clopidogrel Versus Aspirin in Patients with Recent Ischemic Stroke and Established Peripheral Artery Disease: An Economic Evaluation in a Chinese Setting

“…Chronologically, the model recorded all events experienced by patients [26]. At the start of the simulation, the times to all event were calculated and the event with the shortest time would occur.…”