Intelligent Pathway Management

30 Second Summary

The project uses data science to quantify the impact of deviations from the “Best Case Scenario” in patient pathways. It involves process mining, an unsupervised learning model to determine relationships between deviations and patient risk, validation, to create Streamlit app reporting suite. The suite presents statistics and risk stratification, with simulations to identify risk profile changes as pathways change.

Process Mining

Unsupervised learning

Discrete Event Simulation (DES)

Streamlit

The aim of the project is to use data science techniques to quantify the impact of deviations from the “Best Case Scenario” in a patient pathway, this could be diagnostics, surgical or pre-operative assessment.

Process Mining – analysis is done into the number of deviations from a best-case scenario patient pathway.
An unsupervised learning model is used to determine the relationship between these deviations, and the level of patient risk.
The relationships identified in step 2 are validated.
The End product will be a live reporting suite in Streamlit, presenting statistics in relation to the newly identified measures, plus risk stratification of the patient population.
Simulation to identify whether risk profile changes as the pathway changes