A common concern in Bayesian data analysis is that an inappropriately informative prior may unduly influence posterior inferences. In the context of Bayesian clinical trial design, appropriately chosen priors are important to ensure that posterior-based decision rules have good frequentist properties. However, it is difficult to quantify prior information in all but the most stylized models. This issue may be addressed by quantifying the prior information in terms of a number of hypothetical patients, i.e., a prior effective sample size (ESS). ESS of a Bayesian parametric model was defined by Morita, Thall, and Mueller (2008, Biometrics). The ESS provides an easily interpretable index of the informativeness of a prior with respect to a given likelihood. However, the approach requires somewhat complicated analytical computations of the distance between the prior and posterior. This talk will review several alternative definitions of ESS proposed so far and will discuss several remained practical problems.