stars-treat-simmer

R Simmer implemention of the treatment simulation model

https://github.com/pythonhealthdatascience/stars-treat-simmer

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R Simmer implemention of the treatment simulation model

README.html

     

💫 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
   


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


The documentation can be access at https://tommonks.github.io/treat-sim-rsimmer

Owner

Citation (CITATION.cff)

# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!

cff-version: 1.2.0
title: >-
  Towards Sharing Tools, Artifacts, and Reproducible Simulation: a `simmer` model example
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
authors:
  - given-names: Thomas
    family-names: Monks
    affiliation: University of Exeter
    orcid: 'https://orcid.org/0000-0003-2631-4481'
  - given-names: Alison
    family-names: Harper
    affiliation: University of Exeter
    orcid: 'https://orcid.org/0000-0001-5274-5037'
  - given-names: Amy
    family-names: Heather
    affiliation: University of Exeter
    orcid: 'https://orcid.org/0000-0002-6596-3479'
  - given-names: Navonil
    family-names: Mustafee
    affiliation: University of Exeter
    orcid: 'https://orcid.org/0000-0002-2204-8924'
repository-code: 'https://github.com/pythonhealthdatascience/stars-treat-simmer'
abstract: >

  The materials and methods in this documentation support 
  work towards developing the STARS healthcare framework 
  (Sharing Tools and Artifacts for Reproducible Simulations 
  in healthcare). The code and written materials are a work 
  in progress towards STARS version 2.0 (aiming to support 
  both R and Python).  The materials are not currently 
  production ready and are not recommended for use in
  simulation practice at this stage.
  
keywords:
  - Free and Open Source Software
  - Model Reuse
  - Discrete-event simulation
  - Open Science
  - Simmer
  - R
license: GPL-3.0
references:
  - type: article
    title: Towards sharing tools and artefacts for reusable simulations in healthcare
    authors:
      - given-names: Thomas
        family-names: Monks
      - given-names: Alison
        family-names: Harper
      - given-names: Navonil
        family-names: Mustafee
    doi: 10.1080/17477778.2024.2347882
    journal: Journal of Simulation
    publisher:
      name: Taylor & Francis
    month: 5
    year: 2024
  - type: book
    title: Foundations and Methods of Stochastic Simulation
    authors:
      - given-names: Barry L.
        family-names: Nelson
    publisher:
      name: Springer New York
    year: 2013
    doi: 10.1007/978-1-4614-6160-9
    edition: 1st edition

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