Natural Language Processing for Surgical Quality Enhancement

Unstructured clinical data, including operative reports and progress notes, contain critical surgical information that is often absent from structured electronic health record fields. Manual abstraction is time-consuming at scale. Natural language processing (NLP) enables automated extraction of clinically meaningful information to support data-driven perioperative care.

We conducted a narrative review of NLP applications in surgical and trauma care. Applications were evaluated across outcome prediction, postoperative complication detection, automated registry generation, documentation quality assessment, and identification of high-risk patient phenotypes, along with key implementation and data-governance considerations.

NLP approaches demonstrated strong performance in extracting granular clinical variables and identifying postoperative complications from unstructured text. Transformer-based models, which process entire sentences at once using self-attention to understand word relationships and context, improve recognition of contextual clinical language, supporting more accurate risk stratification and quality measurement. Key challenges include documentation variability, limited generalizability across institutions, algorithmic bias, and limited interpretability.

NLP, particularly transformer-based approaches, provides a scalable strategy to leverage unstructured clinical text for surgical research and quality improvement. Integration into clinical workflows has the potential to enhance perioperative outcomes and precision surgery, contingent on rigorous validation and responsible implementation.