The next generation phenotyping of Electronic Health Record (EHR) features the identification of true patient state in an accurate and high-throughput manner. To realize this vision, there is an urgent need to improve the reproducibility and interpretability of the underlying phenotype models and algorithms through a standards-based framework. The Fast Healthcare Interoperability Resources (FHIR) standard was developed to meet a variety of clinical interoperability needs. In this showcase session, we will demonstrate a FHIR-based clinical data normalization pipeline known as NLP2FHIR developed at the Mayo Clinic and an application of this pipeline to perform EHR-based phenotyping. As a case study, we applied the NLP2FHIR to the task of identifying the presence of obesity and 15 of its comorbidities in a given patient from their semi-structured discharge summary. We demonstrate the value of the NLP2FHIR in enabling precise clinical data capturing and making EHR-based patient identifications more reproducible and interpretable.