Background: A critical challenge in genomic medicine is identifying the genetic and environmental risk factors for disease. Currently available data links a majority of known coding human genes to phenotype, but the environmental component of human disease is extremely underrepresented in these linked data sets. Without environmental exposure information, our ability to realize precision health is limited, even with the promise of modern genomics. Achieving integration of gene, phenotype, and environment will require extensive translation of data into a standard, computable form and the extension of the existing gene/phenotype data model. The data standards and models needed to achieve this integration do not currently exist. Their development must be a bottom-up, community effort. Objectives: Our objective is to foster development of data reporting standards and a computational model that will facilitate the inclusion of exposure data in computational analysis of human disease. Methods: To this end, the Computable Exposures Workshop brought together a diverse community of toxicologists, computer scientists, and ontologists in September 2019 to discuss the nature of exposure data and how it might best be integrated with genomic and phenomic data. Results: This workshop presented a preliminary semantic data model, developed several use cases and competency questions, and laid the groundwork for sustained collaboration, including planning of a larger conference in 2020. Discussion: There is a real desire by the exposure science, epidemiology, and toxicology communities to use informatics approaches to improve their research workflow, gain new insights, and increase data reuse. Future collaboration over the longer-term will include an assessment of exposure data resources from the perspective of data modeling and integration and continued development of the Adverse Outcome Pathway (AOP) Wiki and AOP ontology.