Code and material for IPACS Model - Improving the Flow of Patients between Acute, Community and Social Care
The IPACS project was funded by Health Data Research UK. It ran from May 2020-March 2023. The project was undertaken by a research team of six from Bristol, North Somerset, South Gloucestershire Integrated Care Board (BNSSG ICB) (Dr Richard Wood and Dr Paul Forte), University of Bath School of Management (Prof. Christos Vasilakis and Dr Zehra Onen Dumlu) and University of Exeter Medical School (Prof. Martin Pitt and Dr Alison Harper).
The IPACS project aimed to investigate what might constitute 'optimal capacity' along different parts of the complex care discharge pathways form acute hospital to community healthcare.
The IPACS simulation model is a high-level computer model of the discharge-to-assess (D2A) pathways. It takes a set of input parameters (from an Excel file) and estimates potential future service outputs (occupancy, number of patients with a discharge delay, number of days waiting for discharge, total system costs) based on different configurations of parameters. The model accounts for variation in inputs and outputs, and presents results over time.
Please read the files in the Documentation folder to understand how to use, parameterise and run the model.
The model is reported using STRESS-DES Reporting Guidelines (Monks et al. 2019)
If you use the IPACS model for research, reporting, education or any other reason, please cite it using details on Zenodo
@software{alison_harper_2023_7845908,
author = {Alison Harper and
Zehra Onen Dumlu and
Paul Forte and
Christos Vasilakis and
Martin Pitt and
Richard Wood},
title = {{Code and material for IPACS Model - Improving the
Flow of Patients between Acute, Community and
Social Care}},
month = apr,
year = 2023,
publisher = {Zenodo},
version = {v1.0.0},
doi = {10.5281/zenodo.7845908},
url = {https://doi.org/10.5281/zenodo.7845908}
}
Permissions of this strong copyleft license are conditioned on making available complete source code of licensed works and modifications, which include larger works using a licensed work, under the same license. Copyright and license notices must be preserved. Contributors provide an express grant of patent rights.
├── IPACS_model.Rproj
├── IPACS_main script.R
│ └──set_up.R
│ └── bed_functions.R
│ └── bed_model.R
│ └── visit_functions.R
│ └── visit_model.R
│ └── ipacs_produce_report.Rmd
├── model_inputs
│ └── IPACS_params.xlsx
├── outputs
│ └── report
│ └── .docx
│ └── report_data
│ └── .csv
│ └── stochastic_data
│ └── .csv
├── images
│ └── .png
├── Documentation
│ └── Overview
│ └── Technical guide
│ └── STRESS-DES reporting guidelines
├── LICENSE
├── README.md
└── .gitignore
└── .Rproj.user
└── testing
This study aims to demonstrate how, counter to intuition, pursual of elimination of acute delayed transfers of care is likely to be uneconomical, as it would require large amounts of community capacity to accommodate even the rarest of demand peaks, leaving much capacity unused for much of the time.
A simulation study using the IPACS model on the effects of COVID-19 on community capacity requirements and costs to minimise acute delayed discharges.
An application of the discrete-time simulation model showing that total costs across the acute-community interface can be minimized by identifying optimal community capacity in terms of the maximum number of patients for which home visits can be provided by the service.
This paper reports on the development and deployment of the versatile IPACS simulation tool for modelling both the home-based and bedded community step-down pathways, known as ‘Discharge to Assess’ in England’s NHS. Developed in open source ‘R’, the tool offers scalable solutions for exploring different scenarios relating to demand, capacity and patient length of stay.
A supplementary slide set is available here:
Version history is available here
This repository contains the models, with re-coding and bug fixes, with many thanks to Amy Heather
With additional thanks to Dr Thomas Monks
For questions or feedback, please contact:
- Dr Richard Wood [email protected]