Detecting Use of Patient Dietary Supplements in Free Text Clinical Notes

Abstract

Abstract Background: There is widespread use of dietary supplements, some prescribed but many taken without a physician’s guidance. There are many potential interactions of supplements with both over the counter and prescription medications. Many of these are not known to the patient. Documentation of supplement use is incomplete in structured medical records, however additional information about supplements is often found in unstructured clinical notes. Natural language processing (NLP) techniques can be used to detect supplement use in these notes. Methods: We study a group of 377 patients from three healthcare systems and develop an NLP system to detect supplement use. We then use surveys of these patients to investigate correlation between self-reported supplement use and NLP predictions from the clinical notes. Results: We attain an F1 score of 0.914 on creation of the model for all supplements. Individual supplement detection had variable correlation with survey responses, ranging from and F1 of 0.83 for calcium, to F1 of 0.39 for folic acid. Conclusions: We demonstrate the ability to capture the use of dietary supplements from free text clinical notes, enabling clinical studies including drug interactions and outcomes research. Generalizability is demonstrated due to the use of notes from a nationwide electronic health record system. We also show that patients from three healthcare systems self-reported supplement use that often contradicted what was recorded in the clinical record.