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

Cody C Wyles1, Sunyang Fu2, Susan L Odum3

  • 1Department of Orthopedic Surgery, Mayo Clinic, Rochester, Minnesota; Orthopedic Surgery Artificial Intelligence Laboratory, Mayo Clinic, Rochester, Minnesota.

The Journal of Arthroplasty
|October 24, 2022
PubMed
Summary
This summary is machine-generated.

Natural language processing (NLP) algorithms accurately extracted data from total hip arthroplasty (THA) operative notes at an external institution. These validated NLP tools show promise for efficient, cost-effective registry data curation from unstructured electronic health records.

Keywords:
artificial intelligenceelectronic medical recordsnatural language processingregistry sciencetotal hip arthroplasty

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Area of Science:

  • Medical informatics
  • Natural Language Processing (NLP)
  • Orthopedic Surgery Data Analysis

Background:

  • Natural language processing (NLP) systems excel at extracting critical information from electronic health records (EHR).
  • Previously developed NLP algorithms for total hip arthroplasty (THA) operative notes captured operative approach, fixation method, and bearing surface from a single institution.
  • External validation and improvement of these algorithms are necessary for automated registry data curation.

Purpose of the Study:

  • To externally validate and refine NLP algorithms for THA operative notes.
  • To assess the algorithms' performance on data from a different institution.
  • To determine the feasibility of using NLP for large-scale, automated registry data curation.

Main Methods:

  • NLP algorithms were deployed and refined on EHRs from OrthoCarolina, analyzing 39 primary THA operative reports (2018-2021).
  • Operative reports in PDF format were converted to readable text using Adobe software.
  • Accuracy statistics were calculated by comparing NLP extraction against manual chart review.

Main Results:

  • Algorithms demonstrated perfect 100% accuracy for operative approach, fixation technique, and bearing surface at the external center.
  • Compared to the developing center's performance (99.2% approach, 90.7% fixation, 95.8% surface), external validation showed high accuracy.
  • NLP algorithms successfully processed scanned PDFs converted to readable text without functional issues.

Conclusions:

  • NLP algorithms applied to external data achieved excellent accuracy in extracting key THA operative note elements.
  • The findings support the use of NLP on scanned PDFs for developing large registries.
  • NLP offers an expeditious and cost-effective method for extracting data from unstructured EHR datasets.