External Validation of Natural Language Processing Algorithms to Extract Common Data Elements in THA Operative Notes

Background: Natural language processing (NLP) systems are distinctive in their ability to extract critical information from raw text in electronic health records (EHR). We previously developed three algorithms for total hip arthroplasty (THA) operative notes with rules aimed at capturing (1) operative approach, (2) fixation method, and (3) bearing surface using inputs from a single institution. The purpose of this study was to externally validate and improve these algorithms as a prerequisite for broader adoption in automated registry data curation. Methods: The previous NLP algorithms developed at Mayo Clinic were deployed and refined on EHRs from OrthoCarolina, evaluating 39 randomly selected primary THA operative reports from 2018 to 2021. Operative reports were available only in PDF format, requiring conversion to “readable” text with Adobe software. Accuracy statistics were calculated against manual chart review. Results: The operative approach, fixation technique, and bearing surface algorithms all demonstrated perfect accuracy of 100%. By comparison, validated performance at the developing center yielded an accuracy of 99.2% for operative approach, 90.7% for fixation technique, and 95.8% for bearing surface. Conclusion: NLP algorithms applied to data from an external center demonstrated excellent accuracy in delineating common elements in THA operative notes. Notably, the algorithms had no functional problems evaluating scanned PDFs that were converted to “readable” text by common software. Taken together, these findings provide promise for NLP applied to scanned PDFs as a source to develop large registries by reliably extracting data of interest from very large unstructured data sets in an expeditious and cost-effective manner.