Characterizing surgical site infection signals in clinical notes

Steven J. Skube, Zhen Hu, Elliot G. Arsoniadis, Gyorgy J. Simon, Elizabeth C. Wick, Clifford Y. Ko, Genevieve B. Melton

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations


Surgical site infections (SSIs) are the most common and costly of hospital acquired infections. An important step in reducing SSIs is accurate SSI detection, which enables measurement and quality improvement, but currently remains expensive through manual chart review. Building off of previous work for automated and semi-automated SSI detection using expert-derived "strong features" from clinical notes, we hypothesized that additional SSI phrases may be contained in clinical notes. We systematically characterized phrases and expressions associated with SSIs. While 83% of expert-derived original terms overlapped with new terms and modifiers, an additional 362 modifiers associated with both positive and negative SSI signals were identified and 62 new base observations and actions were identified. Clinical note queries with the most common base terms revealed another 49 modifiers. Clinical notes contain a wide variety of expressions describing infections occurring among surgical specialties which may provide value in improving the performance of SSI detection algorithms.

Original languageEnglish (US)
Title of host publicationMEDINFO 2017
Subtitle of host publicationPrecision Healthcare through Informatics - Proceedings of the 16th World Congress on Medical and Health Informatics
EditorsZhao Dongsheng, Adi V. Gundlapalli, Jaulent Marie-Christine
PublisherIOS Press
Number of pages5
ISBN (Electronic)9781614998297
StatePublished - 2017
Event16th World Congress of Medical and Health Informatics: Precision Healthcare through Informatics, MedInfo 2017 - Hangzhou, China
Duration: Aug 21 2017Aug 25 2017

Publication series

NameStudies in Health Technology and Informatics
ISSN (Print)0926-9630
ISSN (Electronic)1879-8365


Other16th World Congress of Medical and Health Informatics: Precision Healthcare through Informatics, MedInfo 2017

Bibliographical note

Publisher Copyright:
© 2017 International Medical Informatics Association (IMIA) and IOS Press.


  • Quality and safety
  • Surgical wound infection
  • Text-mining


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