Automated processing of electronic medical records is a reliable method of determining aspirin use in populations at risk for cardiovascular events.

BACKGROUND: Low-dose aspirin reduces cardiovascular risk; however, monitoring over-the-counter medication use relies on the time-consuming and costly manual review of medical records. Our objective is to validate natural language processing (NLP) of the electronic medical record (EMR) for extracting medication exposure and contraindication information. METHODS: The text of EMRs for 499 patients with type 2 diabetes was searched using NLP for evidence of aspirin use and its contraindications. The results were compared to a standardised manual records review. RESULTS: Of the 499 patients, 351 (70%) were using aspirin and 148 (30%) were not, according to manual review. NLP correctly identified 346 of the 351 aspirin-positive and 134 of the 148 aspirin-negative patients, indicating a sensitivity of 99% (95% CI 97-100) and specificity of 91% (95% CI 88-97). Of the 148 aspirin-negative patients, 66 (45%) had contraindications and 82 (55%) did not, according to manual review. NLP search for contraindications correctly identified 61 of the 66 patients with contraindications and 58 of the 82 patients without, yielding a sensitivity of 92% (95% CI 84-97) and a specificity of 71% (95% CI 60-80). CONCLUSIONS: NLP of the EMR is accurate in ascertaining documented aspirin use and could potentially be used for epidemiological research as a source of cardiovascular risk factor information.