Discrete-Event Simulation in Healthcare Settings: A Review

Forbus, John J.; Berleant, Daniel

doi:10.3390/modelling3040027

Cited by 32 publications

(9 citation statements)

References 42 publications

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“…DES is also useful, because it makes it possible to ask ‘what if’ questions such as the research question. It has been used widely in healthcare, examining healthcare systems operations, and disease progression, screening and health behaviour modelling 7 13…”

Section: Methodsmentioning

confidence: 99%

Modelling NHS England 111 demand for primary care services: a discrete event simulation

Pilbery,

Smith,

Green

et al. 2023

BMJ Open

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ObjectivesThis feasibility study aimed to model in silico the current healthcare system for patients triaged to a primary care disposition following a call to National Health Service (NHS) 111 and determine the effect of reconfiguring the healthcare system to ensure a timely primary care service contact.DesignDiscrete event simulation.SettingSingle English NHS 111 call centre in Yorkshire.ParticipantsCallers registered with a Bradford general practitioner who contacted the NHS 111 service in 2021 and were triaged to a primary care disposition.Primary and secondary outcome measuresFace validity of conceptual model. Comparison between real and simulated data for quarterly counts (and 95% CIs) for patient contact with emergency ambulance (999), 111, and primary and secondary care services. Mean difference and 95% CIs in healthcare system usage between simulations and difference in mean proportion of avoidable admissions for callers who presented to an emergency department (ED).ResultsThe simulation of the current system estimated that there would be 39 283 (95% CI 39 237 to 39 328) primary care contacts, 2042 (95% CI 2032 to 2051) 999 calls and 1120 (95% CI 1114 to 1127) avoidable ED attendances. Modifying the model to ensure a timely primary care response resulted in a mean percentage increase of 196.1% (95% CI 192.2% to 199.9%) in primary care contacts, and a mean percentage decrease of 78.0% (95% CI 69.8% to 86.2%) in 999 calls and 88.1% (95% CI 81.7% to 94.5%) in ED attendances. Avoidable ED attendances reduced by a mean of −26 (95% CI −35 to −17).ConclusionIn this simulated study, ensuring timely contact with a primary care service would lead to a significant reduction in 999 and 111 calls, and ED attendances (although not avoidable ED attendance). However, this is likely to be impractical given the need to almost double current primary care service provision. Further economic and qualitative research is needed to determine whether this intervention would be cost-effective and acceptable to both patients and primary care clinicians.

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Section: Methodsmentioning

confidence: 99%

Modelling NHS England 111 demand for primary care services: a discrete event simulation

Pilbery,

Smith,

Green

et al. 2023

BMJ Open

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“…DES models allow patients to be unique and interact with resource supply (Manzoor et al, 2019). Although testing and running these models takes longer, they help model healthcare delivery systems, especially when resources are scarce (Forbus & Berleant, 2022).…”

Section: Discrete-event Simulationmentioning

confidence: 99%

Medical testing services’ quality enhancement with discrete-event simulation

Mira

Ferreira

Caldas

et al. 2023

Preprint

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In recent years, the demand for health care has increased owing to the ongoing introduction of new health problems and the intensification of existing illnesses. As research in this area progresses, knowledge becomes the primary motivation for patients to adopt a better quality of life. Consequently, many health units have struggled to develop solutions to manage excessive demand while maintaining a high quality of service to enhance patient satisfaction and boost revenues. One of these tactics is using simulation models as a method of operational research to analyze the flow of patients and predict potential solutions to enhance resource efficiency while decreasing waiting time. This work aims to develop and demonstrate a simulation model that replicates the functioning of a hospital unit. For this purpose, data derived from the health services of the Imaging Department of a private hospital, located in Lisbon, Portugal, were used. This model will replicate the daily flow that this department is subjected to by its patients and personnel and depict how the system evolves due to the management of the department's number of resources and examination rooms. At the end of the analyses, several solutions are presented and discussed, from level to level, concerning improvements in the performance of services, resources and patients. However, only the increase in one unit of examination rooms translates into a huge positive impact on waiting times in the respective queues.

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“…A number of important studies have recently been conducted on the development of a general model to assess the impact of data quality on the accuracy of model output (See, e.g., Hasan and Padman, 2006;Ekström et al, 2021;Wang et al, 2023). Indeed, as Forbus and Berleant (2022) recently retorted: "A simulation model output is only as good as the data input that drives it, and without taking the time to ensure quality input data, the output may not yield usable results." Data are regarded to be of high quality if they correctly represent the real-world situation to which they refer.…”

Section: Mercury Simulation Modellingmentioning

confidence: 99%

An updated review of the salient geomedical aspects of mercury for enhancement of data quality in simulation modelling and other prognostic applications: Africa case descriptions

Davies

2023

Front. Anal. Sci.

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Mercury (Hg) pollution is of global concern. Despite the prolificity of research in the past two decades or so, there are still several uncertainties and variabilities in our knowledge of both the element’s exposure dynamics and its health effects. Understanding the intricacies of the element’s emissions-to-impact path, for instance, is rendered intractable by its varied environmental fate and the overarching influence of environmental, geochemical, biological and socioeconomic drivers. In this paper, an updated synopsis of the relevant and more important geomedical characteristics of Hg is considered to constitute part of the provision of high-quality input data needed in Hg simulation modelling studies, and other applications such as the provision of long-term data necessary for evaluating the effectiveness of regulatory measures at various scales. A critical overview is presented on the importance of data quality in parameterisation, and validation of Hg simulation models and other related applications. In this connection, the dearth of modern measurements of Hg abundance in crustal rocks and other Earth materials which needs to be set prior to simulation as well as in modelling source to sink transfers in the Hg cycle, is highlighted. An improved input data quality would also foster the production of model outcomes that are accurate enough for applications in design of better exposure-limiting strategies; and in providing insights on how the course of diagnosis and treatment currently proffered by physicians for Hg-induced maladies, can be revised or expanded. Model results derived from high-quality input datasets also have a high potential for providing forecasting capabilities to inform policy.

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Discrete-Event Simulation in Healthcare Settings: A Review

Cited by 32 publications

References 42 publications

Modelling NHS England 111 demand for primary care services: a discrete event simulation

Modelling NHS England 111 demand for primary care services: a discrete event simulation

Medical testing services’ quality enhancement with discrete-event simulation

An updated review of the salient geomedical aspects of mercury for enhancement of data quality in simulation modelling and other prognostic applications: Africa case descriptions

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