To save time, healthcare providers frequently use abbreviations while authoring clinical documents. Nevertheless, abbreviations that authors deem unambiguous often confuse other readers, including clinicians, patients, and natural language processing (NLP) systems. Most current clinical NLP systems "post-process" notes long after clinicians enter them into electronic health record systems (EHRs). Such post-processing cannot guarantee 100% accuracy in abbreviation identification and disambiguation, since multiple alternative interpretations exist. In this paper, authors describe a prototype system for real-time Clinical Abbreviation Recognition and Disambiguation (CARD) - i.e., a system that interacts with authors during note generation to verify correct abbreviation senses. The CARD system design anticipates future integration with web-based clinical documentation systems to improve quality of healthcare records. The prototype application embodies three word sense disambiguation (WSD) methods. We evaluated the accuracy and response times of the prototype CARD system in a simulated study. Using an existing test data set of 25 commonly observed, highly ambiguous clinical abbreviations the evaluation demonstrated that the best WSD method had an accuracy of 88.8%, and a reasonable average response time of 1.6 milliseconds per each abbreviation. The study indicates potential feasibility of real-time NLP-enabled abbreviation disambiguation within clinical documentation systems.