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Monks T, Harper A. Improving the usability of open health service delivery simulation models using Python and web apps. NIHR OPEN RESEARCH 2023; 3:48. [PMID: 37881450 PMCID: PMC10593330 DOI: 10.3310/nihropenres.13467.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 11/16/2023] [Indexed: 10/27/2023]
Abstract
One aim of Open Science is to increase the accessibility of research. Within health services research that uses discrete-event simulation, Free and Open Source Software (FOSS), such as Python, offers a way for research teams to share their models with other researchers and NHS decision makers. Although the code for healthcare discrete-event simulation models can be shared alongside publications, it may require specialist skills to use and run. This is a disincentive to researchers adopting Free and Open Source Software and open science practices. Building on work from other health data science disciplines, we propose that web apps offer a user-friendly interface for healthcare models that increase the accessibility of research to the NHS, and researchers from other disciplines. We focus on models coded in Python deployed as streamlit web apps. To increase uptake of these methods, we provide an approach to structuring discrete-event simulation model code in Python so that models are web app ready. The method is general across discrete-event simulation Python packages, and we include code for both simpy and ciw implementations of a simple urgent care call centre model. We then provide a step-by-step tutorial for linking the model to a streamlit web app interface, to enable other health data science researchers to reproduce and implement our method.
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Affiliation(s)
- Thomas Monks
- University of Exeter Medical School, University of Exeter, Exeter, England, UK
- NIHR Applied Research Collaboration South West Peninsula, University of Exeter, Exeter, England, UK
| | - Alison Harper
- University of Exeter Medical School, University of Exeter, Exeter, England, UK
- NIHR Applied Research Collaboration South West Peninsula, University of Exeter, Exeter, England, UK
- University of Exeter Business School, University of Exeter, Exeter, England, UK
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Vázquez-Serrano JI, Peimbert-García RE, Cárdenas-Barrón LE. Discrete-Event Simulation Modeling in Healthcare: A Comprehensive Review. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:12262. [PMID: 34832016 PMCID: PMC8625660 DOI: 10.3390/ijerph182212262] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 11/12/2021] [Accepted: 11/13/2021] [Indexed: 11/26/2022]
Abstract
Discrete-event simulation (DES) is a stochastic modeling approach widely used to address dynamic and complex systems, such as healthcare. In this review, academic databases were systematically searched to identify 231 papers focused on DES modeling in healthcare. These studies were sorted by year, approach, healthcare setting, outcome, provenance, and software use. Among the surveys, conceptual/theoretical studies, reviews, and case studies, it was found that almost two-thirds of the theoretical articles discuss models that include DES along with other analytical techniques, such as optimization and lean/six sigma, and one-third of the applications were carried out in more than one healthcare setting, with emergency departments being the most popular. Moreover, half of the applications seek to improve time- and efficiency-related metrics, and one-third of all papers use hybrid models. Finally, the most popular DES software is Arena and Simul8. Overall, there is an increasing trend towards using DES in healthcare to address issues at an operational level, yet less than 10% of DES applications present actual implementations following the modeling stage. Thus, future research should focus on the implementation of the models to assess their impact on healthcare processes, patients, and, possibly, their clinical value. Other areas are DES studies that emphasize their methodological formulation, as well as the development of frameworks for hybrid models.
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Affiliation(s)
- Jesús Isaac Vázquez-Serrano
- School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey 64849, Northeast Nuevo Leon, Mexico; (J.I.V.-S.); (L.E.C.-B.)
| | - Rodrigo E. Peimbert-García
- School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey 64849, Northeast Nuevo Leon, Mexico; (J.I.V.-S.); (L.E.C.-B.)
- School of Engineering, Macquarie University, Sydney, NSW 2109, Australia
| | - Leopoldo Eduardo Cárdenas-Barrón
- School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey 64849, Northeast Nuevo Leon, Mexico; (J.I.V.-S.); (L.E.C.-B.)
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Ren Y, Phan M, Luong P, Wu J, Shell D, Barras CD, Chryssidis S, Kok HK, Burney M, Tahayori B, Maingard J, Jhamb A, Thijs V, Brooks DM, Asadi H. Application of a computational model in simulating an endovascular clot retrieval service system within regional Australia. J Med Imaging Radiat Oncol 2021; 65:850-857. [PMID: 34105874 DOI: 10.1111/1754-9485.13255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2020] [Accepted: 05/13/2021] [Indexed: 11/28/2022]
Abstract
INTRODUCTION The global demand for endovascular clot retrieval (ECR) has grown rapidly in recent years creating challenges to healthcare system planning and resource allocation. This study aims to apply our established computational model to predict and optimise the performance and resource allocation of ECR services within regional Australia, and applying data from the state of South Australia as a modelling exercise. METHOD Local geographic information obtained using the Google Maps application program interface and real-world data was input into the discrete event simulation model we previously developed. The results were obtained after the simulation was run over 5 years. We modelled and compared a single-centre and two-centre ECR service delivery system. RESULTS Based on the input data, this model was able to simulate the ECR delivery system in the state of South Australia from the moment when emergency services were notified of a potential stroke patient to potential delivery of ECR treatment. In the model, ECR delivery improved using a two-centre system compared to a one-centre system, as the percentage of stroke patients requiring ECR was increased. When 15% of patients required ECR, the proportion of 'failure to receive ECR' cases for a single-centre system was 17.35%, compared to 3.71% for a two-centre system. CONCLUSIONS Geolocation and resource utilisation within the ECR delivery system are crucial in optimising service delivery and patient outcome. Under the model assumptions, as the number of stroke cases requiring ECR increased, a two-centre ECR system resulted in increased timely ECR delivery, compared to a single-centre system. This study demonstrated the flexibility and the potential application of our DES model in simulating the stroke service within any location worldwide.
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Affiliation(s)
- Yifan Ren
- Interventional Neuroradiology Service - Department of Radiology, Austin Hospital, Melbourne, Victoria, Australia
| | | | - Phillip Luong
- School of Science, Computer Science and Information Technology, RMIT University, Melbourne, Victoria, Australia
| | - Jamin Wu
- Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Victoria, Australia
| | - Daniel Shell
- Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Victoria, Australia
| | - Christen D Barras
- South Australian Health and Medical Research Institute, University of Adelaide, Adelaide, South Australia, Australia.,Department of Radiology, Royal Adelaide Hospital, Adelaide, South Australia, Australia
| | - Steve Chryssidis
- Department of Medical Imaging, Flinders Medical Centre, Adelaide, South Australia, Australia
| | - Hong Kuan Kok
- Interventional Radiology Service - Department of Radiology, Northern Health, Melbourne, Victoria, Australia
| | - Moe Burney
- Deloitte, Sydney, New South Wales, Australia
| | - Baham Tahayori
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, Victoria, Australia
| | - Julian Maingard
- Interventional Neuroradiology Service - Department of Radiology, Monash Health, Melbourne, Victoria, Australia.,School of Medicine - Faculty of Health, Deakin University, Geelong, Victoria, Australia
| | - Ashu Jhamb
- Interventional Radiology Service - Department of Radiology, St Vincent's Hospital, Melbourne, Victoria, Australia
| | - Vincent Thijs
- Stroke Theme, The Florey Institute of Neuroscience and Mental Health, University of Melbourne, Melbourne, Victoria, Australia.,Department of Neurology, Austin Health, Melbourne, Victoria, Australia
| | - Duncan Mark Brooks
- Interventional Neuroradiology Service - Department of Radiology, Austin Hospital, Melbourne, Victoria, Australia.,Stroke Theme, The Florey Institute of Neuroscience and Mental Health, University of Melbourne, Melbourne, Victoria, Australia
| | - Hamed Asadi
- Interventional Neuroradiology Service - Department of Radiology, Austin Hospital, Melbourne, Victoria, Australia.,Interventional Neuroradiology Service - Department of Radiology, Monash Health, Melbourne, Victoria, Australia.,School of Medicine - Faculty of Health, Deakin University, Geelong, Victoria, Australia.,Interventional Radiology Service - Department of Radiology, St Vincent's Hospital, Melbourne, Victoria, Australia.,Stroke Theme, The Florey Institute of Neuroscience and Mental Health, University of Melbourne, Melbourne, Victoria, Australia
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