Clinical documents in electronic health records (EHRs) contain detailed information about patients and their care. Natural language processing (NLP) can unlock the information conveyed in clinical narratives, thus playing a critical role in real-world studies. The NLP Working Group at the Observational Health Data Sciences and Informatics (OHDSI) consortium was established to develop methods and tools to promote the use of textual data and NLP in real-world observational studies. In this talk, I will describe a framework for representing and utilizing textual data in real-world evidence generation, including representations of information from clinical text in the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM), the workflow and tools that were developed to extract, transform and load (ETL) data from clinical notes into tables in OMOP CDM, as well as current applications and specific use cases of the proposed OHDSI NLP solution at large consortia and individual institutions. Challenges faced and lessons learned during the process will also be discussed to provide valuable insights for researchers who are planning to implement NLP solutions in real-world studies.