Time modeling is an important aspect of medical decision-support systems engineering. At the core of effective time modeling lies the challenge of proper knowledge representation design. In this paper, we focus on two important principles for effective time-modeling languages: (a) hybrid temporal representation, and (b) dynamic temporal abstraction. To explore the significance of these design principles, we extend a previously-defined formalism (single-granularity modifiable temporal belief networks--MTBN-SGs) to accommodate multiple temporal granularities and dynamic query and domain-specific model creation. We call the new formalism multiple-granularity MTBNs (MTBN-MGs). We develop a prototype system for modeling aspects of liver transplantation and analyze the resulting model with respect to its representation power, representational tractability, and inferential tractability. Our experiment demonstrates that the design of formalisms is crucial for effective time modeling. In particular: (i) Hybrid temporal representation is a desirable property of time-modeling languages because it makes knowledge acquisition easier, and increases representational tractability. (ii) Dynamic temporal abstraction improves inferential and representational tractability significantly. We discuss a high-level procedure for extending existing languages to incorporate hybrid temporal representation and dynamic temporal abstraction.