Challenge background
In England, over 7.5 million people are waiting for hospital treatment or therapy. Demand exceeds hospital capacity, and the burden of the disease worsens as the waiting list gets longer.
The problem
Diagnostics are a constraint in accessing treatment. Healthcare planners need the capacity to simulate patient flow and forecast demand. SimPy is a process-based discrete-event simulation package based on standard Python.
An open-source simulation model with one-shot optimization capability deployed to a web interface would deliver a valuable tool for healthcare planners. Furthermore, a free simulation environment would enable better collaboration between healthcare systems and the generation of anonymous synthetic data.
Goal of the project
Leverage the SimPy library and Power Bi to develop an embedded web simulation service. Generate synthetic data and deploy to a URL-end point to capture run data for future ML training models.
Project timeline
- 1
Week 1
- Literature review on patient flow simulation
- Set up environment, repo and provision.
- Identify and collect relevant data sources.
- Identify and prioritize key features (MVP) for the web app.
- 2
Week 2
- Generate synthetic data
- Develop a simulation model in SimPy
- 3
Week 3
- Visualize outputs with seaborn
- 4
Week 4
- Embed simulation model into URL with BI viz tool
What you'll learn
Leverage the SimPy library and Power Bi to develop an embedded web simulation service. Generate synthetic data and deploy to a URL-end point to capture run data for future ML training models.