Survival analysis is a statistical approach used to predict the time until the occurrence of a specific event. It has broad applications across various domains, including healthcare, manufacturing, and logistics. In this paper, we advance standard statistical approaches by introducing the Survival Hidden Markov Model (SHMM), which decouples the modeling of the failure event from the representation of the hidden state. This design choice enhances the model’s interpretability and allows the latent states to capture underlying dynamics independently of the event occurrence. We evaluate SHMM by comparing its performance to state-of-the-art survival analysis methods, including the Cox proportional hazards model and Random Survival Forests, on synthetic data as well as on chronic kidney disease (CKD) single session data.