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Leveraging Natural Language Processing and Structured Scoring to Evaluate Operative Note Quality in Surgical

Phillip D Jenkins1, Eric Cramer2, Julie Doberne1

  • 1Surgical Data and Decision Sciences Lab, Department of Surgery, Oregon Health & Science University, Portland, Oregon.

Journal of Surgical Education
|June 16, 2026
PubMed
Summary

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This summary is machine-generated.

Resident operative notes have fewer key elements and lower quality scores than attending notes, particularly in preoperative and postoperative documentation. This study used NLP and structured grading to objectively assess surgical trainee notes.

Area of Science:

  • Medical Informatics
  • Surgical Education
  • Natural Language Processing

Background:

  • Operative notes are crucial for procedural documentation, care continuity, and surgical training.
  • Previous research indicates resident-authored notes may have omissions or errors.
  • Analyzing free-text operative notes at scale has been challenging.

Purpose of the Study:

  • To evaluate the quality of resident versus attending operative notes using a novel two-phase approach.
  • To leverage natural language processing (NLP) and structured assessment for objective quality measurement.
  • To explore the potential of operative notes as a competency assessment tool for surgical trainees.

Main Methods:

  • A two-phase approach combining NLP diffing and structured assessment (SAFE-OR) was employed.
Keywords:
competency-based assessmentnatural language processingoperative notesresident documentationsurgical education

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  • Operative notes from general surgery procedures with both resident and attending versions were analyzed.
  • Grader severity was adjusted using z-normalization and rescaling for accurate comparisons.

    • Main Results:

    • NLP diffing revealed significant variations in edit burden across different surgical procedures.
    • Resident-authored notes scored significantly lower than attending notes in preoperative (Section I) and postoperative (Section III) elements.
    • Overall quality scores were significantly lower for resident notes compared to attending notes, with no significant difference in intraoperative elements (Section II).

    Conclusions:

    • Resident operative notes demonstrate lower quality and fewer key elements compared to attending notes, especially in preoperative and postoperative sections.
    • The combined NLP and structured grading approach provides a high-throughput, objective method for assessing operative note quality.
    • This methodology offers a new pathway for evaluating surgical trainees' understanding and documentation skills.