Conditional frontier models, including full and partial, robust frontiers, have evolved into an indispensable tool for exploring the impact of exogenous factors on the performance of the decision-making units in a fully nonparametric setup. Nonparametric conditional frontier models enable the handling of heterogeneity in a formal way, allowing explanation of the differences in the efficiency levels achieved by units operating under different external or environmental conditions. A thorough analysis of both full and robust time dependent conditional efficiency measures and of their corresponding estimators allows unravelling the compounded impact that exogenous factors may have on the production process. The nonparametric framework does not make assumptions on error distributions and production function forms and avoids misspecification problems when the data-generation process is unknown, as is common in applied studies. This chapter proposes a comprehensive review and journey through the conditional nonparametric frontier models developed so far in the efficiency literature. The authors show how this nonparametric dynamic framework is important for evaluating efficiency in the healthcare sector. They provide numerical illustrations on datasets from the Italian healthcare system, including summaries of practical implementation details.