License: GPL v3 Read the Docs ORCID: Harper ORCID: Monks ORCID: Heather ORCID: Mustafee

đź’« Towards Sharing Tools, Artifacts, and Reproducible Simulation: a simmer model examplar

Overview

The materials and methods in this documentation support work towards developing the S.T.A.R.S healthcare framework (Sharing Tools and Artifacts for Reproducible Simulations in healthcare). Long term S.T.A.R.S aims to support researchers share open simulation models regardless of language choice, improve the quality of sharing, and reduce the workload required to meet high standards of open science for the modelling and simulation community.

The code and written materials are a work in progress towards STARS version 2.0. It demonstrates the application od sharing a discrete-event simuilation model and associated research artifacts:

  • All artifacts in this repository are linked to study researchers via ORCIDs;
  • Model code is made available under a GNU Public License version 3;
  • [To do: validate and test R dependencies managed through renv]
  • The R code and simmer model are documented and explained in a quarto website served up by GitHub pages;
  • [To do: the materials are deposited and made citatable using Zenodo;]
  • [To do: The models are sharable with other researchers and the NHS without the need to install software.]

Author ORCIDs

ORCID: Harper ORCID: Monks ORCID: Heather ORCID: Mustafee

Citation

To Add

Funding

This work was supported by the Medical Research Council [grant number MR/Z503915/1]

Case study model

This example is based on exercise 13 from Nelson (2013) page 170. Please also credit this work is you use our materials.

Nelson. B.L. (2013). Foundations and methods of stochastic simulation. Springer.

We adapt a textbook example from Nelson (2013): a terminating discrete-event simulation model of a U.S based treatment centre. In the model, patients arrive to the health centre between 6am and 12am following a non-stationary Poisson process. On arrival, all patients sign-in and are triaged into two classes: trauma and non-trauma. Trauma patients include impact injuries, broken bones, strains or cuts etc. Non-trauma include acute sickness, pain, and general feelings of being unwell etc. Trauma patients must first be stabilised in a trauma room. These patients then undergo treatment in a cubicle before being discharged. Non-trauma patients go through registration and examination activities. A proportion of non-trauma patients require treatment in a cubicle before being discharged. The model predicts waiting time and resource utilisation statistics for the treatment centre. The model allows managers to ask question about the physical design and layout of the treatment centre, the order in which patients are seen, the diagnostic equipment needed by patients, and the speed of treatments. For example: “what if we converted a doctors examination room into a room where nurses assess the urgency of the patients needs.”; or “what if the number of patients we treat in the afternoon doubled”

Online Notebooks via Binder

To do

Online documentation produced by Quarto

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