Modeling Patient Care Pathways as Markov Chains

Abstract

Healthcare involves complex processes where patients move through multiple stages of care. Understanding these pathways is essential for improving outcomes. This chapter presents a pedagogical, worked-example application of Markov chain modeling to patient care pathways using the MIMIC-III Clinical Database Demo. The methodology represents each stage as a discrete state—Admission, ICU, Home, and Expired—and estimates transition probabilities empirically. By analyzing these transitions, the framework demonstrates how outcomes can be predicted, trajectories simulated, and resource planning informed by probabilistic estimates. The transparent Markov structure ensures interpretability. The chapter also discusses extensions toward adaptive systems, including Bayesian updating and reinforcement learning connections. Because the analysis uses a small demonstration dataset, the numerical results are illustrative rather than definitive. Nonetheless, the chapter provides a clear, reproducible template for readers to adapt to their own data