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Time-Saving Impact of an Algorithm to Identify Potential Surgical Site Infections

Published online by Cambridge University Press:  02 January 2015

B. C. Knepper ,
H. Young ,
T. C. Jenkins  and
C. S. Price
B. C. Knepper*
Affiliation:
Department of Patient Safety and Quality, Denver Health Medical Center, Denver, Colorado
H. Young
Affiliation:
Department of Medicine, Denver Health Medical Center, Denver, Colorado Division of Infectious Diseases, Denver Health Medical Center, Denver, Colorado
T. C. Jenkins
Affiliation:
Department of Medicine, Denver Health Medical Center, Denver, Colorado Division of Infectious Diseases, Denver Health Medical Center, Denver, Colorado
C. S. Price
Affiliation:
Department of Patient Safety and Quality, Denver Health Medical Center, Denver, Colorado Department of Medicine, Denver Health Medical Center, Denver, Colorado Division of Infectious Diseases, Denver Health Medical Center, Denver, Colorado
*
4000, 660 Bannock Street, Denver, CO 80204 ([email protected])
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Abstract

Objective.

To develop and validate a partially automated algorithm to identify surgical site infections (SSIs) using commonly available electronic data to reduce manual chart review.

Design.

Retrospective cohort study of patients undergoing specific surgical procedures over a 4-year period from 2007 through 2010 (algorithm development cohort) or over a 3-month period from January 2011 through March 2011 (algorithm validation cohort).

Setting.

A single academic safety-net hospital in a major metropolitan area.

Patients.

Patients undergoing at least 1 included surgical procedure during the study period.

Methods.

Procedures were identified in the National Healthcare Safety Network; SSIs were identified by manual chart review. Commonly available electronic data, including microbiologic, laboratory, and administrative data, were identified via a clinical data warehouse. Algorithms using combinations of these electronic variables were constructed and assessed for their ability to identify SSIs and reduce chart review.

Results.

The most efficient algorithm identified in the development cohort combined microbiologic data with postoperative procedure and diagnosis codes. This algorithm resulted in 100% sensitivity and 85% specificity. Time savings from the algorithm was almost 600 person-hours of chart review. The algorithm demonstrated similar sensitivity on application to the validation cohort.

Conclusions.

A partially automated algorithm to identify potential SSIs was highly sensitive and dramatically reduced the amount of manual chart review required of infection control personnel during SSI surveillance.

Type
Original Article
Information
Infection Control & Hospital Epidemiology , Volume 34 , Issue 10 , October 2013 , pp. 1094 - 1098
Copyright
Copyright © The Society for Healthcare Epidemiology of America 2013

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References

1.Wright, SB, Ostrowsky, B, Fishman, N, Deloney, VM, Mermel, L, Perl, TM. Expanding roles of healthcare epidemiology and infection control in spite of limited resources and compensation. Infect Control Hosp Epidemiol 2010;31(2):127–133.CrossRefGoogle ScholarPubMed
2.Smith, K, Adams, J, Kaplan, J. Charting a course to higher value health care. Hospitals and Health Networks website. http://www.hhnmag.com/hhnmag_app/jsp/articledisplay.jsp?dcrpath=HHNMAG/Article/data/09SEP2010/091410HHN_ Weekly_Smith&domain=HHNMAG. Accessed lanuary 13, 2012.Google Scholar
3.Sands, K, Vineyard, G, Livingston, J, Christiansen, C, Piatt, R. Efficient identification of postdischarge surgical site infections: use of automated pharmacy dispensing information, administrative data, and medical record information. J Infect Dis 1999;179(2):434–441.Google Scholar
4.Price, CS, Savitz, LA. Improving the measurement of surgical site infection risk stratification/outcome detection: final report. Prepared by Denver Health and its partners under contract 290-2006-00-20. Agency for Healthcare Research and Quality (AHRQ) Publication 12-0046-EF. Rockville, MD: AHRQ, 2012.Google Scholar
5.Inacio, MCS, 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(4):351–359.CrossRefGoogle Scholar
6. Surgical site infection (SSI) event. Centers for Disease Control and Prevention (CDC), January 2012. Procedure-Associated Module, National Health and Safety Network, CDC.Google Scholar
7.West, J, Khan, Y, Murray, DM, Stevenson, KB. Assessing specific secondary ICD-9-CM codes as potential predictors of surgical site infections. Am J Infect Control 2010;38(9):701–705.CrossRefGoogle ScholarPubMed
8.Apte, M, Landers, T, Furuya, Y, Hyman, S, Larson, E. Comparison of two computer algorithms to identify surgical site infections. Surg Infect 2011;12(6):459–464.CrossRefGoogle ScholarPubMed
9.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(9):1223–1229.Google Scholar
10.Shapiro, MF, Greenfield, S. The complete blood count and leukocyte differential count: an approach to their rational application. Ann Intern Med 1987;106(1):65–74.CrossRefGoogle ScholarPubMed