Process Mining with Python tutorial: A healthcare application — Part 4

This article is the fourth of a tutorial series made up of the following parts:

• Part 1: Introduction to process mining, data preprocessing and initial data exploration.
• Part 2: Primer on process discovery using the PM4Py (Python) library to apply the Alpha Miner algorithm.
• Part 3: Other process discovery algorithms and model representations.
• Part 4 (this article): More holistic models which integrate control flow, time (e.g. bottlenecks, wait times), resources (e.g. personnel capacity and performance, inter-personnel relationships, department/ward capacity and performance), case attributes (e.g. patient demographics, clinical condition).

You can find the complete source code and data for this tutorial series here. In Part 1 of the tutorial, you saw how to prepare and explore your data to get a high level ‘feel’ for the processes. In Part 2, you learned how to apply the alpha miner algorithm to discover a process model using the pm4py library. In Part 3, you learned about two further process discovery algorithms — heuristic miner and inductive miner — and implemented them with the pm4py library.

In this part, we will look at how process mining can be used in conjunction with other analyses to give more holistic explanatory and predictive models. Healthcare systems are complex, and it would be naive to think that a process model would give you the whole picture…