Natural language processing to ascertain two key variables from operative reports in ophthalmology.

PURPOSE: Antibiotic prophylaxis is critical to ophthalmology and other surgical specialties. We performed natural language processing (NLP) of 743 838 operative notes recorded for 315 246 surgeries to ascertain two variables needed to study the comparative effectiveness of antibiotic prophylaxis in cataract surgery. The first key variable was an exposure variable, intracameral antibiotic injection. The second was an intraoperative complication, posterior capsular rupture (PCR), which functioned as a potential confounder. To help other researchers use NLP in their settings, we describe our NLP protocol and lessons learned.
METHODS: For each of the two variables, we used SAS Text Miner and other SAS text-processing modules with a training set of 10 000 (1.3%) operative notes to develop a lexicon. The lexica identified misspellings, abbreviations, and negations, and linked words into concepts (e.g. "antibiotic" linked with "injection"). We confirmed the NLP tools by iteratively obtaining random samples of 2000 (0.3%) notes, with replacement.
RESULTS: The NLP tools identified approximately 60 000 intracameral antibiotic injections and 3500 cases of PCR. The positive and negative predictive values for intracameral antibiotic injection exceeded 99%. For the intraoperative complication, they exceeded 94%.
CONCLUSION: NLP was a valid and feasible method for obtaining critical variables needed for a research study of surgical safety. These NLP tools were intended for use in the study sample. Use with external datasets or future datasets in our own setting would require further testing.