Infection Control & Hospital Epidemiology (2018), 39, 931–935 doi:10.1017/ice.2018.116
Original Article
Validation of semiautomated surgical site infection surveillance using electronic screening algorithms in 38 surgery categories
Sun Young Cho MD1,2, Doo Ryeon Chung MD, PhD1,2, Jong Rim Choi RN1, Doo Mi Kim RN1, Si-Ho Kim MD2, Kyungmin Huh MD2, Cheol-In Kang MD, PhD2 and Kyong Ran Peck MD, PhD2 1Center for Infection Prevention and Control, Samsung Medical Center, Seoul, Republic of Korea and 2Division of Infectious Diseases, Department of Internal
Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea 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.
(Received 11 February 2018; accepted 28 April 2018; electronically published June 12, 2018)
Surgical site infections (SSIs) are among the most common healthcare-associated infections (HAIs) worldwide1,2; they are asso- ciated with significant morbidity, mortality, and cost.3,4 Surveillance of SSIs has been established as an important preventive measure,5,6 and the need for accurate, timely, and efficient methods for detecting SSIs is becoming more evident. However, conventional surveillance methods based on manual chart review are labor intensive and prone to interobserver variability.7 Recently, electronic surveillance systems have been increasingly utilized for the surveillance of HAIs.8–10 Such semiautomated electronic surveillance systems have been reported to reduce the workload for chart reviews while maintaining a high sensitivity of SSI detection for specific surgical procedures such as total hip and knee arthroplasty.11–15 However, previous studies have been limited to certain types of surgery. The goal of this study was to verify the validity of semiautomated SSI surveillance using electronic screening algorithms in 38 categories of surgery.
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 PREVIOUS PRESENTATION: A portion of this work was presented at the 4th International Conference on Prevention and Infection Control (ICPIC) in Geneva, Switzerland, on June 23, 2017 Cite this article: Cho SY, et al. (2018). Validation of semiautomated surgical site
infection surveillance using electronic screening algorithms in 38 surgery categories. Infection Control & Hospital Epidemiology 2018, 39, 931–935. doi:10.1017/ice.2018.116
© 2018 by The Society for Healthcare Epidemiology of America. All rights reserved.
Methods Study setting and design
This study was conducted at the Samsung Medical Center, a 1,989-bed tertiary-care referral center in Seoul, Republic of Korea, in which ~45,000 surgical procedures are performed every year. All surgical procedures in the 38 categories monitored under the conventional SSI surveillance system between January 1, 2013, and December 31, 2014, were included in this study. The sur- veillance dataset stored in the conventional SSI surveillance reg- istry was used as the reference standard for validating the semiautomated SSI surveillance system. All SSI surveillance was performed based on decisions by the hospital’s Infection Control Committee. The semiautomated surveillance system was devel- oped and implemented for quality improvement.
Conventional SSI surveillance
Since 2013, for the quality improvement of in-hospital patient care, prospective SSI surveillance has been implemented for 38 categories of surgery. With the exception of pacemaker surgery, these categories are consistent with the National Healthcare Safety Network (NHSN) operative procedure categories.16 The cate- gories are shown in Supplementary Table 1. The conventional SSI surveillance was based on surgeons’ voluntary self-reporting using
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