Welcome to the Healthcare Services Analytics & Decision Science Atlas.
This is a directory of open-source tools, packages, and projects for analytics and decision science in healthcare.
This site is in its very early stages - please check back as we continue to develop it.
In time, we hope this can develop into a tool to help showcase the amazing work happening across the analytics and data science communities in healthcare, promoting reuse and collaborative work on the tools we all need.
Click on the links below, or use the menu bar to navigate.
and Tools
Contributions
Contributions are very welcome!
You don’t need to be able to write code or use Quarto to add to the Atlas - there are lots of ways to submit content.
Please see our contributors page for instructions on how to get involved.
Interested to see what things we propose should be added to the Atlas?
Click on the cards in the right hand column above to see the full list of proposed entries for each type of content.
Please note that inclusion of a tool on this site does not imply that the HSMA team, NIHR, or any other Atlas contributors, have reviewed these tools in detail. This resource is designed to act as a signpost to the work that is out there, and provide a high-level indication of the health of any given project, but you should always conduct your own reviews of the suitability of any of these tools and techniques for your own use case.
Inclusion of a tool on this page also does not indicate endorsement of the tool author of the HSMA programme, NIHR, the Atlas, or the description written of the tool.
If you have any concerns, or would like any content to be removed, please raise an issue on the repository.
Most Recently Added or Updated Projects, Packages and Tools
An R Package that uses routinely collected data to explore factors impacting emergency department crowding.
RAP code used for the Cancer Registration Statistics for England in 2020.
"Shiny app showing the percentage of tumours receiving radiotherapy, Systemic Anti-Cancer Therapy (SACT) and/or a surgical tumour resection as part of the primary course of treatment following diagnosis."
A SimPy discrete event simulation model, used as part of a hybrid modelling approach for investigating interventions in renal care.
A Python discrete event simulation and Streamlit app that models non‑elective inpatient flow to assess how bed capacity, length of stay and Same Day Emergency Care affects Emergency Department waiting times.
DES RAP Book is an open resource and website for building discrete-event simulation (DES) models within a reproducible analytical pipeline (RAP), supporting the healthcare simulation community. DES models are often used for building simulations of pathways and services, allowing 'what-if' questions to be tested. The resource demonstrates practical, code-based workflows and tools to help researchers and practitioners develop, validate, and share DES models in Python (SimPy) and R (simmer), ensuring models are reproducible.
Click here to view all projects, packages and tools.