The National Academy of Medicine has called for physicians to document social isolation in the electronic health record (EHR), because it can affect health outcomes. However, social isolation cannot be entered as coded data in current EHRs but only mentioned in clinical notes, which have historically been unintelligible to computers. Medical University of South Carolina (MUSC) investigators have trained natural language processing software to search clinical notes and identify socially isolated patients with 90 percent accuracy. MUSC owes its expertise in NLP in part to the NCATS-funded South Carolina Clinical & Translational (SCTR) Institute, a Clinical and Translational Science Award hub housed at MUSC. The NLP strategy developed by the MUSC team can be applied to other social determinants of health, particularly those that cannot be entered as coded data, and to other diseases. The team is already using NLP to identify patients with financial insecurity and alcohol abuse.