Reproducible analytical pipelines (RAP) for healthcare simulation: Quarto-based guide and dual Python/R implementations

Discrete‑event simulation (DES) is a popular technique for modelling healthcare systems, from clinic queues to complex patient pathways. While implementing these models as reproducible analytical pipelines (RAPs) is increasingly recognised as best practice, modellers often struggle to achieve this due to gaps in accessible guidance, skills, and time. This poster presents a set of open resources designed to make RAP for healthcare simulation concrete and achievable. At the centre is a Quarto book that provides a step‑by‑step guide to building DES models as RAPs, covering project set‑up, model inputs, simulation logic, output analysis, experimentation, verification and validation, style, documentation, and sharing. Each chapter combines narrative with executable code and outputs, supporting both Python and R so users can work in their preferred language while seeing how RAP principles transfer across ecosystems. Alongside the book, four repositories provide Python and R implementations of two case studies: a simple synthetic queueing model and a more complex stroke pathway model. Together, these resources provide practical guidance for healthcare modellers to implement DES models as RAPs, drawing on research software engineering practices to develop robust, testable, and reproducible models.