Incorporation of temporality into analytics and modeling fills a critical gap in the interpretation of data (precursors, outcomes, related events). Not only can temporality augment phenotypic associations for patients, necessary for life sciences initiatives and other health studies, but it is key for deriving meaningful links between medical treatment and health outcomes and for constructing advanced decision support systems. Precision medicine that lacks clear timelines is, simply put, not all that precise. Predictive modeling can only determine a pattern when temporal relationships capture intervals and sequencing. With the estimated 80% of data in a patient’s medical record entered as free text, our inability to tap information contained in that narrative leaves a substantial gap in the data we can utilize for analytics. Providing consistent rules for LLMs based on accepted inferred temporal phrase meaning can guide Natural Language Processing to achieve an acceptable degree of precision in interpreting clinical note text. The inclusion of text note temporality can inform and support the construction of predictive modeling.