Journal article

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

Brian T Bucher, Jianlin Shi, Jeffrey P Ferraro, David E Skarda, Matthew H Samore, John F Hurdle, Adi Gundlapalli, Wendy W Chapman, Samuel RG Finlayson

ANNALS OF SURGERY | LIPPINCOTT WILLIAMS & WILKINS | Published : 2020

Abstract

OBJECTIVES: We present the development and validation of a portable NLP approach for automated surveillance of SSIs. SUMMARY OF BACKGROUND DATA: The surveillance of SSIs is labor-intensive limiting the generalizability and scalability of surgical quality surveillance programs. METHODS: We abstracted patient clinical text notes after surgical procedures from 2 independent healthcare systems using different electronic healthcare records. An SSI detected as part of the American College of Surgeons' National Surgical Quality Improvement Program was used as the reference standard. We developed a rules-based NLP system (Easy Clinical Information Extractor [CIE]-SSI) for operative event-level detec..

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Grants

Awarded by Agency for Healthcare Research and Quality


Awarded by NIH Shared Instrumentation Grant


Funding Acknowledgements

This research was supported by grant 1K08HS025776 from the Agency for Healthcare Research and Quality (Dr. Bucher). The computational resources used were partially funded by the NIH Shared Instrumentation Grant 1S10OD021644-01A1.