Irregular spellings in clinical free text present challenges to natural language processing. A number of spelling correction tools exist, but automated spelling correction of clinical text is not a routine practice due to the risk of introducing new errors. We developed a novel spelling analysis application that combines Word2Vec and Levenshtein Edit Distance Constraints to identify variant forms of words. The use case applied to this study was that of discovering herbal and dietary supplements that interact with prescription medications in clinical text. The prototype application processed a large corpus (approximately 1.6 million records), achieving a positive predictive value of 0.9322, in identifying spelling variants, outperforming two baseline methods that achieved positive predictive values of 0.0348 and 0.0067. Our findings suggest that this prototype application provides a more efficient method for researchers and clinicians to find valid misspellings of terms in clinical text.