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Natural Language Processing for Surgical Quality Enhancement.

Lakshika Tennakoon1, Nathan Roll2, Lisa Marie Knowlton1

  • 1Department of Surgery, Section of Acute Care Surgery, Stanford University School of Medicine, Stanford, California.

The Journal of Surgical Research
|April 19, 2026
PubMed
Summary
This summary is machine-generated.

Natural language processing (NLP) can automatically extract key surgical information from unstructured clinical notes. This technology, especially transformer-based models, aids in surgical research and improving patient care.

Keywords:
Electronic health recordsNatural language processing (NLP)Unstructured clinical data

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Area of Science:

  • Surgery
  • Medical Informatics
  • Natural Language Processing

Background:

  • Unstructured clinical data in operative reports and progress notes contain vital surgical information missing from structured electronic health records.
  • Manual data abstraction is inefficient for large-scale analysis.
  • Natural Language Processing (NLP) offers automated information extraction for data-driven perioperative care.

Purpose of the Study:

  • To review Natural Language Processing (NLP) applications in surgical and trauma care.
  • To evaluate NLP's effectiveness in areas like outcome prediction, complication detection, and patient phenotyping.
  • To identify implementation and data-governance factors for NLP in surgery.

Main Methods:

  • Conducted a narrative review of NLP applications in surgical and trauma settings.
  • Assessed NLP applications across outcome prediction, complication detection, registry generation, documentation quality, and high-risk patient identification.
  • Considered implementation and data-governance aspects.

Main Results:

  • NLP effectively extracts clinical variables and postoperative complications from unstructured text.
  • Transformer-based NLP models enhance contextual understanding, improving risk stratification and quality measurement.
  • Challenges include documentation variability, limited generalizability, bias, and interpretability.

Conclusions:

  • NLP, especially transformer models, offers a scalable method for using unstructured surgical text in research and quality improvement.
  • Integrating NLP into clinical workflows can improve perioperative outcomes and precision surgery.
  • Successful implementation requires rigorous validation and responsible practices.