NHSRwaitinglist

{NHSRwaitinglist} is an R package implementing the waiting list management approach described in the paper Understanding Waiting Lists Pressures by Fong et al. The methodology is presented by Neil Walton, Professor in Operations Management at Durham University Business School. The package is being contributed to by members of the NHS-R Community, with the aim of making it simpler for NHS analysts to adopt these methods for the analysis of their own waiting lists.

Explanatory Videos

These videos explain more about the Operations Management methodologies which are encapsulated by the package. Click through to view them on YouTube.

Detailed presentation of method	Recap of method, and presentation of this package

Midlands Analyst Network Huddle, Nov 2023	Health and Care Analytics Conference e-lab, Jul 2024

Installation

You can install the current version of {NHSRwaitinglist} from GitHub as below.
If you have not installed from GitHub before, you may first need to create and store a “Personal Access Token” (PAT) to grant access to your account, for example by following the {usethis} guide.

# install.packages("remotes")
remotes::install_github("nhs-r-community/NHSRwaitinglist", build_vignettes = TRUE)

Getting Started

There is a minimal example below. To look in more detail at the functions within the package and some more ways of using them, it is a good idea to review the first two vignettes:

The example walkthrough vignette takes a step-by-step walk through the white paper, using the six core functions in this package.
The three example waiting lists vignette simulates three closely related waiting lists, and uses package functions to explore some of their characteristics.

At its most basic, the package can be used to simulate a waiting list (a dataframe of waiting list addition dates and removal dates), and then compute some important statistics. Of course, if you already have waiting list data ready to analyse, you can skip the simulation step in the code below.

# load the package
library(NHSRwaitinglist)

# simulate a waiting list
waiting_list <- wl_simulator(
  start_date = "2020-01-01",
  end_date = "2024-03-31",
  demand = 10, # simulating 10 patient arrivals per week
  capacity = 10.2 # simulating 10.2 patients being treated per week
)

# compute some waiting list statistics
overall_stats <- wl_stats(
  waiting_list = waiting_list,
  target_wait = 18 # standard NHS 18wk target
)

# review the minimal dataset needed to define a waiting list (first 5 rows only)
knitr::kable(head(waiting_list, 5))

referral	removal
2020-01-02	2020-01-03
2020-01-03	2020-01-04
2020-01-04	2020-01-05
2020-01-04	2020-01-06
2020-01-06	2020-01-07

# review the waiting list statistics
knitr::kable(overall_stats)

mean.demand	mean.capacity	load	load.too.big	queue_size	target_queue_size	queue.too.big	mean_wait	cv_arrival	cv_removal	target.capacity	relief.capacity	pressure
9.818065	10.08167	0.9738527	FALSE	5	44.18129	FALSE	1.8	1.131775	0.7001389	10.01485	NA	0.4

Contributing

If you want to learn more about this project, please join the discussion at the NHS-R Community Slack group and the specific channel #managing-waiting-lists.

Please see our guidance on how to contribute.

This project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

The simplest way to contribute is to raise an issue detailing the feature or functionality you would like to see added, or any unexpected behaviour or bugs you have experienced.

Pull-Request workflow

You are welcome to also submit Pull Requests and, as the main branch is protected and won’t accept pushes directly even if you have been added to the repository as a member, the workflow will be (from your own forked repository if you are not a member, or a clone of the repository if you are a member):

Create new branch with a descriptive name
Commit to the new branch (add code or delete code or make changes)
Push the commits
Create a pull-request in GitHub to signal that your work is ready to be merged
Tag one or more reviewers (@ThomUK and @ChrisMainey) so that your contribution can be reviewed and merged into main