Discrete Event Simulation for Health Technology Assessment

Jaime, J.; Möller, Jörgen; Karnon, Jonathan; Stahl, James E.; Ishak, Jack

doi:10.1201/b19421

Cited by 33 publications

(32 citation statements)

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“…Discrete event simulation (DES) method is used to establish the outpatient treatment process of the dental clinic and a version 7.5 of Flexsim software was used. DES technique is wide use to model the operations of a system where entities compete for limited resources, forming queues as needed [19]. …”

Section: Simulation Modelmentioning

confidence: 99%

Design of a Patient-Centered Appointment Scheduling with Artificial Neural Network and Discrete Event Simulation

Chang¹,

Chang²

2018

JSSM

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Quality of medical services is even more critical than ever in a highly competitive health care industry. Long waiting time is a major source of patient dissatisfaction, thus, the issue of how to reduce waiting time to improve service quality is very important. A well-designed appointment scheduling system can effectively shorten the patient waiting time and enhance their satisfaction. This study aims to propose a framework for individualized outpatient appointment scheduling (OAS) in a dental clinic which composed of one attending dentist and two resident dentists. To design the OAS, firstly, the prediction model of the treatment duration of an individual patient was established by using artificial neural network. Secondly, discrete event simulation method was used to develop the simulation model which simulates the operations of the studied dental clinic. Finally, the established simulation model was used to evaluate the performance of the appointment scheduling. The proposed model consists of numbers of main features: 1) the service providers composed of multiple dentists with different professional competence levels; 2) there are two types of patients (return and not-return patients); 3) patient no-shows was considered; and 4) a variety of medical treatments (requiring different treatment time) are provided to the patients. The results of the study show that the proposed OAS can effectively improve the service performance of the dental clinic, this could be caused by patient's characteristics were taken into consideration of building an appropriated appointment interval.

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Section: Simulation Modelmentioning

confidence: 99%

Design of a Patient-Centered Appointment Scheduling with Artificial Neural Network and Discrete Event Simulation

Chang¹,

Chang²

2018

JSSM

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“…It is therefore a useful tool for modelling re-usable (or occupied) resource constraints, as proven in its widespread use in other sectors such as operational research, engineering and scheduling [8][9][10][11]. The use of DES is also gaining momentum within the field of HTA itself [12][13][14]. There has been a recent systematic review on the use of DES for HTA [15], which identified 42 relevant studies.…”

Section: Introductionmentioning

confidence: 99%

Discrete Event Simulation-Based Resource Modelling in Health Technology Assessment

et al. 2017

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OBJECTIVE:To conduct a systematic review of published research on the use of discrete event simulation (DES) for resource modelling in health technology assessment (HTA). Resource modelling (RM) is broadly defined as incorporating and measuring effects of constraints on physical resources (e.g. beds, doctors, nurses) in HTA models. METHODS: Systematic literature searches were conducted in academic databases (JSTOR, SAGE, SPRINGER, SCOPUS, IEEE, Science Direct, PUBMED, EMBASE) and grey literature (Google Scholar, NHS journal library), enhanced by manual searchers (i.e. reference list checking, citation searching and hand searching techniques). RESULTS:The search strategy yielded 4,117 potentially relevant citations. Following the screening and manual searches, 10 articles were included. Reviewing these articles provided insights into the applications of RM: firstly, different types of economic analyses, model settings, RM and costeffectiveness analysis (CEA) outcomes were identified. Secondly, variation in the characteristics of the constraints such as types and nature of constraints, sources of data for the constraints were identified. Thirdly, it was found that including the effects of constraints caused the CEA results to change in these articles. CONCLUSION:The review found that DES proved to be an effective technique for RM but there were only a small number of studies applied in HTA. However, these studies showed the important consequences of modelling physical constraints and point to the need for a framework to be developed to guide future applications of this approach. (230 words) Keywords Discrete event simulation, resource modelling, health technology assessment, simulation modelling 2 Key Points for Decision Makers  Economic evaluation studies in health care typically ignore the short-term constraints on physical resources (e.g. doctors, nurses) which can lead to incorrect results.  Discrete Event Simulation is an effective tool for modelling the effects of constraints but there were only a small number of studies applied in health technology assessment (HTA).  Further research is required to examine the possible developments for detailed modelling of the resource constraints in HTA.

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“…An alternative approach is provided by structuring the problem around the events that can happen. This technique -discrete event simulation (DES) -was developed in operations research to model systems where entities compete for limited resources, forming queues as needed [3]. DES has been adapted for HTA, by redefining the events as clinically relevant occurrences and interpreting the entities as people, with individual attributes that reflect characteristics that determine their course [4].…”

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confidence: 99%

“…The data demands become even more acute when one considers that some of them should be reserved for validation of the model's predictions [18]. It also requires deeper understanding of standard distributions, their quantile forms, and the statistical methods for estimating them (see Chapter 6 in [3]). A competent biostatistician becomes a key member of the modeling team.…”

mentioning

confidence: 99%

“…Thus, most DES are programmed using specialized software that can handle the clock, properly schedule events and find the next one to occur, allow for dynamic entity creation, assignment of attribute values and movement, process events at the appropriate time, and even implement animation of the model structure to facilitate visualization, understanding and debugging. These DES software (see, for example, chapter 10 in [3]) tend to be expensive and, of course, require learning the appropriate syntax and approach to structuring a DES peculiar to each one. This, in turn, increases training requirements and can prove a barrier to review groups who cannot be expected to know every piece of software that might be used.…”

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confidence: 99%

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Advantages and disadvantages of discrete-event simulation for health economic analyses

Jaime

Möller

2016

Expert Review of Pharmacoeconomics & Outcomes Research

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Discrete Event Simulation for Health Technology Assessment

Cited by 33 publications

References 0 publications

Design of a Patient-Centered Appointment Scheduling with Artificial Neural Network and Discrete Event Simulation

Design of a Patient-Centered Appointment Scheduling with Artificial Neural Network and Discrete Event Simulation

Discrete Event Simulation-Based Resource Modelling in Health Technology Assessment

Advantages and disadvantages of discrete-event simulation for health economic analyses

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