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

doi:10.3233/978-1-61499-830-3-955

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 proceeding › Conference contribution

5 Scopus citations

Abstract

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 language	English (US)
Title of host publication	MEDINFO 2017
Subtitle of host publication	Precision Healthcare through Informatics - Proceedings of the 16th World Congress on Medical and Health Informatics
Editors	Zhao Dongsheng, Adi V. Gundlapalli, Jaulent Marie-Christine
Publisher	IOS Press
Pages	955-959
Number of pages	5
ISBN (Electronic)	9781614998297
DOIs	https://doi.org/10.3233/978-1-61499-830-3-955
State	Published - 2017
Event	16th World Congress of Medical and Health Informatics: Precision Healthcare through Informatics, MedInfo 2017 - Hangzhou, China Duration: Aug 21 2017 → Aug 25 2017

Publication series

Name	Studies in Health Technology and Informatics
Volume	245
ISSN (Print)	0926-9630
ISSN (Electronic)	1879-8365

Other

Other	16th World Congress of Medical and Health Informatics: Precision Healthcare through Informatics, MedInfo 2017
Country/Territory	China
City	Hangzhou
Period	8/21/17 → 8/25/17

Bibliographical note

Funding Information:
This research was supported by the University of Minnesota Academic Health Center Faculty Development Award (GS, GM), Agency for Healthcare Research and Quality (R01HS24532), National Institutes of Health (NIH) Clinical and Translational Science Award (CTSA) program (UL1TR000114), Fairview Health Services, and University of Minnesota Physicians.

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

Keywords

Quality and safety
Surgical wound infection
Text-mining

Publisher link

10.3233/978-1-61499-830-3-955

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Cite this

Skube, S. J., Hu, Z., Arsoniadis, E. G., Simon, G. J., Wick, E. C., Ko, C. Y., & Melton, G. B. (2017). Characterizing surgical site infection signals in clinical notes. In Z. Dongsheng, A. V. Gundlapalli, & J. Marie-Christine (Eds.), MEDINFO 2017: Precision Healthcare through Informatics - Proceedings of the 16th World Congress on Medical and Health Informatics (pp. 955-959). (Studies in Health Technology and Informatics; Vol. 245). IOS Press. https://doi.org/10.3233/978-1-61499-830-3-955

Characterizing surgical site infection signals in clinical notes. / Skube, Steven J.; Hu, Zhen; Arsoniadis, Elliot G. et al.

MEDINFO 2017: Precision Healthcare through Informatics - Proceedings of the 16th World Congress on Medical and Health Informatics. ed. / Zhao Dongsheng; Adi V. Gundlapalli; Jaulent Marie-Christine. IOS Press, 2017. p. 955-959 (Studies in Health Technology and Informatics; Vol. 245).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Skube, SJ, Hu, Z, Arsoniadis, EG , Simon, GJ, Wick, EC, Ko, CY & Melton, GB 2017, Characterizing surgical site infection signals in clinical notes. in Z Dongsheng, AV Gundlapalli & J Marie-Christine (eds), MEDINFO 2017: Precision Healthcare through Informatics - Proceedings of the 16th World Congress on Medical and Health Informatics. Studies in Health Technology and Informatics, vol. 245, IOS Press, pp. 955-959, 16th World Congress of Medical and Health Informatics: Precision Healthcare through Informatics, MedInfo 2017, Hangzhou, China, 8/21/17. https://doi.org/10.3233/978-1-61499-830-3-955

Skube SJ, Hu Z, Arsoniadis EG , Simon GJ, Wick EC, Ko CY et al. Characterizing surgical site infection signals in clinical notes. In Dongsheng Z, Gundlapalli AV, Marie-Christine J, editors, MEDINFO 2017: Precision Healthcare through Informatics - Proceedings of the 16th World Congress on Medical and Health Informatics. IOS Press. 2017. p. 955-959. (Studies in Health Technology and Informatics). doi: 10.3233/978-1-61499-830-3-955

Skube, Steven J. ; Hu, Zhen ; Arsoniadis, Elliot G. et al. / Characterizing surgical site infection signals in clinical notes. MEDINFO 2017: Precision Healthcare through Informatics - Proceedings of the 16th World Congress on Medical and Health Informatics. editor / Zhao Dongsheng ; Adi V. Gundlapalli ; Jaulent Marie-Christine. IOS Press, 2017. pp. 955-959 (Studies in Health Technology and Informatics).

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AB - 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.

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Characterizing surgical site infection signals in clinical notes

Abstract

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