OBJECTIVES: Common methods to visualize Markov states over time (e.g., Markov-cycle trees or state probability graphs) do not scale well to many cycles and have limitations concerning the perception of proportions.  An objective of the IFEDH research project (FFG grant number 827347) was to overcome these limits by investigating new visualization methods of Markov models and their results. METHODS:  Inspired by the “Parallel Coordinates”, an interactive technique called Parallel Sets has been developed for visualizing multidimensional categorical data. The visualization lays out axes in a parallel way where each axis represents one categorical dimension. Within each axis, boxes represent the categories which are scaled according to the respective frequencies. Applied to Markov Models, the categorical dimensions correspond to the various cycles. Joint probabilities of categories from adjacent axes are shown as parallelograms connecting the respective categories.  The parallelograms can be interpreted as the number of patients transiting from one state to another. Depending on the purpose, the color of the parallelograms indicates the categories of a chosen cycle or could refer to additional attributes of the patients like age or sex. RESULTS: State probability and survival curves merely show specific aggregates of the data while classic Markov trace visualizations with for example bubble diagrams do not visualize data in a sense that would facilitate a detection of proportions and trends. Applying Parallel Sets to analyze Markov models provides an interactive visualization technique where changing the reference Markov cycle is as easy as highlighting particular dimensions, thus enabling the exploration of the progress of patient cohorts with certain characteristics through the model. CONCLUSIONS: Model development always requires thorough analysis of its structure, behavior and results. Parallel Sets enable an intuitive and efficient visualization technique for presentation purposes as well as exploratory analysis.