Ongoing efforts exist to record variants and genetic diagnoses within the EHR (see Genomics Reporting IG by the Clinical Genomics WG). However, the inverse process is nascent - there is a great need to extract phenotypic information out of the EHR. Phenotypic information is increasingly utilized in a variety of clinical and research applications, such as rare disease genomics diagnostics, public health surveillance, adverse events, real world data analytics, and cancer treatment selection. However, the phenotypic content used in these genomic and other applications is spread in different components of any given EHR platform. Current manual practices to extract the records are slow and laborious, and the interpretation of the data is not often included in the EHR. Alternatively, a PDF of EHR content, or a candidate genetic diagnosis might be sent to a clinical lab for diagnostic purposes. None of these current approaches are scaleable or sustainable for research analytics or precision medicine. Phenotypic data as described here is included within laboratories and their values, conditions, diagnoses, imaging, problem lists, and clinical notes. Identification of this phenotypic information for a specific patient requires significant investment of time from a clinician to perform the collation of this information, but the information offers the potential for much better information for both clinical research and for genomic and other healthcare needs. The Global Alliance for Genomics and Health (GA4GH) and ISO have approved a new schema standard “Phenopackets” for exchanging case-level phenotype data. A Vulcan objective is to find ways in which FHIR can enable EHRs to be extended with phenotypic information to align with the Phenopacket file format. A Phenopacket represents an individual proband or patient and includes information about the individual such as age (which can be represented in multiple ways including ranges to protect privacy) and sex, any existing disease diagnoses. Almost all elements of a Phenopacket are optional. A simple Phenopacket containing only information about the proband and a list of phenotypic features is all that is required for use cases of Mendelian genomic disease diagnostics. More comprehensive Phenopackets containing additional data about biosamples and treatment may be appropriate for use cases surrounding rare disease, common/complex disease, or cancer diagnoses, treatment selection, and research. Relevant resources: 1. Phenopackets Schema: https://phenopacket-schema.readthedocs.io/en/latest/index.html 2. Draft IG: http://phenopackets.org/core-ig/ig/branch/master/index.html 3. HPO: https://hpo.jax.org/app/