Effects of non-cardiac drugs on cardiac contractility can lead to serious adverse events. Furthermore, programs aimed at treating heart failure have had limited success and this therapeutic area remains a major unmet medical need. The challenges in assessing drug effect on cardiac contractility point to the fundamental translational value of the current preclinical models. Therefore, we sought to develop an adult human primary cardiomyocyte contractility model that has the potential to provide a predictive preclinical approach for simultaneously predicting drug-induced inotropic effect (sarcomere shortening) and generating multi-parameter data to profile different mechanisms of action based on cluster analysis of a set of 12 contractility parameters. We report that 17 positive and 9 negative inotropes covering diverse mechanisms of action exerted concentration-dependent increases and decreases in sarcomere shortening, respectively. Interestingly, the multiparametric readout allowed for the differentiation of inotropes operating via distinct mechanisms. Hierarchical clustering of contractility transient parameters, coupled with principal component analysis, enabled the classification of subsets of both positive as well as negative inotropes, in a mechanism-related mode. Thus, human cardiomyocyte contractility model could accurately facilitate informed mechanistic-based decision making, risk management and discovery of molecules with the most desirable pharmacological profile for the correction of heart failure.