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Improving the usability of open health service delivery simulation models using Python and web apps.

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Summary

Free and Open Source Software (FOSS) in health services research can be difficult to use. This study proposes using web apps to make discrete-event simulation models more accessible to researchers and decision-makers.

Keywords:
Discrete-Event SimulationHealth Services ResearchModel ReuseOpen SciencePythonReproducibilityWeb Applications

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Area of Science:

  • Health Services Research
  • Computational Health Science
  • Open Science

Background:

  • Open Science aims to improve research accessibility.
  • Discrete-event simulation models in health services research are often shared as Free and Open Source Software (FOSS) code, primarily using Python.
  • Sharing FOSS code can be a barrier due to required specialist skills, hindering adoption of open science practices.

Purpose of the Study:

  • To propose and demonstrate a method for increasing the accessibility of discrete-event simulation models in health services research.
  • To facilitate the use of these models by researchers and National Health Service (NHS) decision-makers through user-friendly web applications.
  • To provide a reproducible framework for developing web-app-ready simulation models in Python.

Main Methods:

  • Developing a standardized approach for structuring discrete-event simulation model code in Python to be web app compatible.
  • Utilizing the streamlit library to create interactive web applications for deployed Python models.
  • Implementing and demonstrating the method using simpy and ciw packages with an urgent care call centre model example.
  • Providing a step-by-step tutorial for linking simulation models to a streamlit web app interface.

Main Results:

  • A generalizable method for structuring Python-based discrete-event simulation models for web app deployment.
  • Successful creation of streamlit web applications for healthcare discrete-event simulation models.
  • Demonstration of the method's applicability across different Python simulation packages (simpy, ciw).
  • A tutorial enabling other health data science researchers to implement the proposed approach.

Conclusions:

  • Web applications, specifically using streamlit with Python, significantly enhance the accessibility of discrete-event simulation models in health services research.
  • The proposed code structuring method lowers the barrier for researchers and NHS decision-makers to utilize complex simulation models.
  • This approach supports the broader adoption of open science principles within health data science by making research outputs more readily usable and reproducible.