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Portable Automated Surveillance of Surgical Site Infections Using Natural Language Processing: Development and

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Automated surveillance of surgical site infections (SSIs) using natural language processing (NLP) of clinical notes is feasible. This portable NLP approach achieved high sensitivity and specificity in detecting SSIs across different healthcare systems.

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Area of Science:

  • Medical Informatics
  • Natural Language Processing
  • Surgical Quality Improvement

Background:

  • Surgical site infection (SSI) surveillance is crucial for quality improvement but is labor-intensive.
  • Current methods limit the generalizability and scalability of surgical quality surveillance programs.

Purpose of the Study:

  • To develop and validate a portable natural language processing (NLP) approach for automated surveillance of surgical site infections (SSIs).

Main Methods:

  • Developed a rules-based NLP system (Easy Clinical Information Extractor [CIE]-SSI) for operative event-level SSI detection.
  • Trained and validated the system on clinical text notes from two independent healthcare systems with different electronic health records.
  • Performance was measured using sensitivity, specificity, and area under the receiver-operating-curve (AUC), with American College of Surgeons' National Surgical Quality Improvement Program as the reference standard.

Main Results:

  • The EasyCIE-SSI system demonstrated high performance in both internal (sensitivity 94%, specificity 88%, AUC 0.912) and external validation (sensitivity 79%, specificity 92%, AUC 0.852).
  • SSI prevalence was 4% and 5% in internal and external validation cohorts, respectively.
  • Sensitivity decreased in specific procedure types (clean, skin/subcutaneous, outpatient) during external validation.

Conclusions:

  • Automated surveillance of SSIs using NLP of clinical notes is achievable with high sensitivity and specificity.
  • The developed NLP approach offers a scalable and generalizable solution for SSI surveillance.