Modelling clinical pathways from Electronic Health Records (EHRs) can optimize resources and improve
patient care, but current methods for generating pathway models using clustering have limitations including
scalability and fidelity of the clusters. We propose a novel pathway modelling approach using Maximum
Likelihood (ML) data clustering on Markov chain representations of clinical pathways. Our method is
calibrated to produce clusters with low inter-cluster variability across the pathways. We use machine learning
with Stochastic Radial Basis Functions (SRBF) kernels for surrogate optimization to handle non-convexity
and propose an incremental optimization method to improve scalability. We also define a methodology
based on novel replayability scores to help analysts compare the fidelity of alternative clustering results.
Results show that our ML method produces clusters that have higher fidelity in terms of replayability scores
than k-means based clustering and in capturing queueing contention, which is important for bottleneck
identification in healthcare.