Precision medicine, artificial intelligence, data analytics, and predictive modeling hold great promise to advance healthcare, possibly as dramatically as the introduction of rigorous scientific research methodology to medicine in the last century. Incorporation of temporality fills a critical gap in the interpretation of health data, providing agency for deriving meaningful associations between observables, treatments and outcomes. Unstructured text fields appearing throughout the medical record, contain essential narratives describing health events. Natural language processing can highlight and codify these narratives including their temporal qualities and be used to construct a health timeline. Timelines, in turn, enable a synopsis view of key health events for a patient, comparison with peers and identification of highly similar patients. Temporal objects, following syntactic rules and semantic validation, serve as building blocks for defining the temporal aspects of events. Three temporal perspectives provide important vantages for understanding events—biographic, differential, and extrinsic.