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Infection Control & Hospital Epidemiology Infection Control & Hospital Epidemiology

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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 ,
Doo Ryeon Chung ,
Jong Rim Choi ,
Doo Mi Kim ,
Si-Ho Kim ,
Kyungmin Huh ,
Cheol-In Kang  and
Kyong Ran Peck
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
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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
Information
Infection Control & Hospital Epidemiology , Volume 39 , Issue 8 , August 2018 , pp. 931 - 935
Copyright
© 2018 by The Society for Healthcare Epidemiology of America. All rights reserved. 

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References

1. Lewis, SS, Moehring, RW, Chen, LF, Sexton, DJ, Anderson, DJ. Assessing the relative burden of hospital-acquired infections in a network of community hospitals. Infect Control Hosp Epidemiol 2013;34:1229–1230.CrossRefGoogle Scholar
2. Magill, SS, Edwards, JR, Bamberg, W, et al. Multistate point-prevalence survey of health care-associated infections. N Engl J Med 2014;370:1198–1208.CrossRefGoogle ScholarPubMed
3. Zimlichman, E, Henderson, D, Tamir, O, et al. Health care-associated infections: a meta-analysis of costs and financial impact on the US health care system. JAMA Intern Med 2013;173:2039–2046.CrossRefGoogle ScholarPubMed
4. Klevens, RM, Edwards, JR, Richards, CL Jr, et al. Estimating health care-associated infections and deaths in US hospitals, 2002. Public Health Rep 2007;122:160–166.CrossRefGoogle Scholar
5. Brandt, C, Sohr, D, Behnke, M, Daschner, F, Rüden, H, Gastmeier, P. Reduction of surgical site infection rates associated with active surveillance. Infect Control Hosp Epidemiol 2006;27:1347–1351.CrossRefGoogle ScholarPubMed
6. Anderson, DJ, Podgorny, K, Berríos-Torres, SI, et al. Strategies to prevent surgical site infections in acute-care hospitals: 2014 update. Infect Control Hosp Epidemiol 2014;35:605–627.CrossRefGoogle ScholarPubMed
7. Calderwood, MS, Ma, A, Khan, YM, et al. Use of Medicare diagnosis and procedure codes to improve detection of surgical site infections following hip arthroplasty, knee arthroplasty, and vascular surgery. Infect Control Hosp Epidemiol 2012;33:40–49.CrossRefGoogle ScholarPubMed
8. Grota, PG, Stone, PW, Jordan, S, Pogorzelska, M, Larson, E. Electronic surveillance systems in infection prevention: organizational support, program characteristics, and user satisfaction. Am J Infect Control 2010;38:509–514.CrossRefGoogle ScholarPubMed
9. Woeltje, KF, Lin, MY, Klompas, M, Wright, MO, Zuccotti, G, Trick, WE. Data requirements for electronic surveillance of healthcare-associated infections. Infect Control Hosp Epidemiol 2014;35:1083–1091.CrossRefGoogle ScholarPubMed
10. Sips, ME, Bonten, MJM, van Mourik, MSM. Automated surveillance of healthcare-associated infections: state of the art. Curr Opin Infect Dis 2017;30:425–431.CrossRefGoogle ScholarPubMed
11. Sips, ME, Bonten, MJM, van Mourik, MSM. Semiautomated surveillance of deep surgical site infections after primary total hip or knee arthroplasty. Infect Control Hosp Epidemiol 2017;38:732–735.CrossRefGoogle ScholarPubMed
12. Bolon, MK, Hooper, D, Stevenson, KB, et al. Improved surveillance for surgical site infections after orthopedic implantation procedures: extending applications for automated data. Clin Infect Dis 2009;48:1223–1229.CrossRefGoogle ScholarPubMed
13. Perdiz, LB, Yokoe, DS, Furtado, GH, Medeiros, EA. Impact of an automated surveillance to detect surgical-site infections in patients undergoing total hip and knee arthroplasty in Brazil. Infect Control Hosp Epidemiol 2016;37:991–993.CrossRefGoogle Scholar
14. Rusk, A, Bush, K, Brandt, M, et al. Improving surveillance for surgical site infections following total hip and knee arthroplasty using diagnosis and procedure codes in a provincial surveillance network. Infect Control Hosp Epidemiol 2016;37:699–703.CrossRefGoogle Scholar
15. Inacio, MC, Paxton, EW, Chen, Y, et al. Leveraging electronic medical records for surveillance of surgical site infection in a total joint replacement population. Infect Control Hosp Epidemiol 2011;32:351–359.CrossRefGoogle Scholar
16. Surgical site infection (SSI) event. Centers for Disease Control and Prevention website. http://www.cdc.gov/nhsn/pdfs/pscmanual/9pscssicurrent.pdf. Published 2017. Accessed May 1, 2018.Google Scholar
17. Gaynes, RP, Culver, DH, Horan, TC, Edwards, JR, Richards, C, Tolson, JS. Surgical site infection (SSI) rates in the United States, 1992–1998: the National Nosocomial Infections Surveillance System basic SSI risk index. Clin Infect Dis 2001;33(Suppl 2):S69–S77.CrossRefGoogle ScholarPubMed
18. Russo, PL, Shaban, RZ, Macbeth, D, Carter, A, Mitchell, BG. Impact of electronic healthcare-associated infection surveillance software on infection prevention resources: a systematic review of the literature. J Hosp Infect 2018;99:1–7.CrossRefGoogle ScholarPubMed
19. Branch-Elliman, W, Strymish, J, Itani, KM, Gupta, K. Using clinical variables to guide surgical site infection detection: a novel surveillance strategy. Am J Infect Control 2014;42:1291–1295.CrossRefGoogle ScholarPubMed
20. Chalfine, A, Cauet, D, Lin, WC, et al. Highly sensitive and efficient computer-assisted system for routine surveillance for surgical site infection. Infect Control Hosp Epidemiol 2006;27:794–801.CrossRefGoogle ScholarPubMed
21. Spolaore, P, Pellizzer, G, Fedeli, U, et al. Linkage of microbiology reports and hospital discharge diagnoses for surveillance of surgical site infections. J Hosp Infect 2005;60:317–320.CrossRefGoogle ScholarPubMed
22. Freeman, R, Moore, LS, García Álvarez, L, Charlett, A, Holmes, A. Advances in electronic surveillance for healthcare-associated infections in the 21st Century: a systematic review. J Hosp Infect 2013;84:106–119.CrossRefGoogle ScholarPubMed
23. Jeroen, S de Bruin. Walter Seeling, Christian Schuh. Data use and effectiveness in electronic surveillance of healthcare associated infections in the 21st century: a systematic review. J Am Med Inform 2014;21:942–951.Google Scholar
24. Kim, NS, Hwang, JH, Park, SH, Chae, SM, Choi, YK. Feasibility of using administrative data to compare healthcare-associated infection performance. Health and social welfare review by the Korean Institute for Health and Social Affairs 2017;37:495–581.Google Scholar
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