Background: Veterans may benefit from promising innovations in treatments for mPCa. The Veterans Affairs (VA) and Prostate Cancer Foundation (PCF) leadership issued a challenge to identify, in real time, the national census of Veterans receiving care for mPCa. Administrative diagnostic and procedural coding do not accurately identify the risk status or disease state of prostate cancer (PCa). This study reports the development and validation of NLP tools deployed on clinical notes to identify risk status or disease state. Methods: Using diagnosis and histology codes, we queried the VA Corporate Data Warehouse to identify Veterans with prostate cancer. We included structured laboratory tests, medications, procedures, and surgeries related to prostate cancer diagnosis or treatment in the analysis. Using structured data, we identified 1000 likely mPCa cases and controls. Medical records were reviewed to confirm status and to extract term dictionaries related to cancer, anatomy, metastasis, and other diagnostic concepts. We went through several iterations of testing to refine and validate the NLP tool on a limited set of known cases and controls. We deployed the tool on all cancer, urology, pathology, and radiation oncology notes. Results: The NLP system was able to identify the patients' history of metastatic disease with 0.975 precision and 0.828 recall. Among the 1,081,137 Veterans with prostate cancer, NLP identified 63,222 (5.8%) with mPCa. There are 16,282 Veterans alive with mPCa. Mean age of diagnosis was 67 and 8,847 (54.3%) were diagnosed in the VA. Demographics were: White 9,756 (60%), Black 4,466 (27%), and other 2,060 (13%). Conclusions: NLP is a reliable tool for identifying Veterans who may benefit from novel innovations in mPCa diagnosis and treatment.