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Validation of semiautomated surgical site infection surveillance using electronic screening algorithms in 38 surgery categories

Published online by Cambridge University Press:  12 June 2018

Sun Young Cho
Affiliation:
Center for Infection Prevention and Control, Samsung Medical Center, Seoul, Republic of Korea Division of Infectious Diseases, Department of Internal Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
Doo Ryeon Chung*
Affiliation:
Center for Infection Prevention and Control, Samsung Medical Center, Seoul, Republic of Korea Division of Infectious Diseases, Department of Internal Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
Jong Rim Choi
Affiliation:
Center for Infection Prevention and Control, Samsung Medical Center, Seoul, Republic of Korea
Doo Mi Kim
Affiliation:
Center for Infection Prevention and Control, Samsung Medical Center, Seoul, Republic of Korea
Si-Ho Kim
Affiliation:
Division of Infectious Diseases, Department of Internal Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
Kyungmin Huh
Affiliation:
Division of Infectious Diseases, Department of Internal Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
Cheol-In Kang
Affiliation:
Division of Infectious Diseases, Department of Internal Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
Kyong Ran Peck
Affiliation:
Division of Infectious Diseases, Department of Internal Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
*
Author for correspondence: Doo Ryeon Chung, MD, PhD, Division of Infectious Diseases, Department of Internal Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Irwon-ro 81, Gangnam-gu, Seoul, 06351, Republic of Korea. E-mail: iddrchung@gmail.com

Abstract

Objective

To verify the validity of a semiautomated surgical site infection (SSI) surveillance system using electronic screening algorithms in 38 categories of surgery.

Design

A cohort study for validation of semiautomated SSI surveillance system using screening algorithms.

Setting

A 1,989-bed tertiary-care referral center in Seoul, Republic of Korea.

Methods

A dataset of 40,516 surgical procedures in 38 categories stored in the conventional SSI surveillance registry at the Samsung Medical Center between January 2013 and December 2014 was used as the reference standard. In the semiautomated surveillance system, electronic screening algorithms flagged cases meeting at least 1 of 3 criteria: antibiotic prescription, microbial culture, and infectious disease consultation. Flagged cases were audited by infection preventionists. Analyses of sensitivity, specificity, and positive predictive value (PPV) were conducted for the semiautomated surveillance system, and its effect on reducing the workload for chart review was evaluated.

Results

A total of 575 SSI events (1·42%) were identified by conventional SSI surveillance. The sensitivity of the semiautomated SSI surveillance was 96·7%, and the PPV of the screening algorithms alone was 4·1%. Semiautomated SSI surveillance reduced the chart review workload of the infection preventionists from 1,283 to 482 person hours per year (a 62·4% decrease).

Conclusions

Compared to conventional surveillance, semiautomated surveillance using electronic screening algorithms followed by chart review of selected cases can provide high-validity surveillance results and can significantly reduce the workload of infection preventionists.

Type
Original Article
Copyright
© 2018 by The Society for Healthcare Epidemiology of America. All rights reserved. 

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