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The hypothesis that computational models can be reliable enough to be adoptedin prognosis and patient care is revolutionizing healthcare. Deep learning, inparticular, has been a game changer in building predictive models, thus leadingto community-wide data curation efforts. However, due to inherent variabilitiesin population characteristics and biological systems, these models are oftenbiased to the training datasets. This can be limiting when models are deployedin new environments, when there are systematic domain shifts not known apriori. In this paper, we propose to emulate a large class of domain shifts,that can occur in clinical settings, with a given dataset, and argue thatevaluating the behavior of predictive models in light of those shifts is aneffective way to quantify their reliability. More specifically, we develop anapproach for building realistic scenarios, based on analysis of \textit{diseaselandscapes} in multi-label classification. Using the openly available MIMIC-IIIEHR dataset for phenotyping, for the first time, our work sheds light into dataregimes where deep clinical models can fail to generalize. This work emphasizesthe need for novel validation mechanisms driven by real-world domain shifts inAI for healthcare.

Understanding Behavior of Clinical Models under Domain Shifts