Skip to main content Accessibility help

Data Requirements for Electronic Surveillance of Healthcare-Associated Infections

  • Keith F. Woeltje (a1), Michael Y. Lin (a2), Michael Klompas (a3) (a4), Marc Oliver Wright (a5), Gianna Zuccotti (a4) (a6) and William E. Trick (a7)...


Electronic surveillance for healthcare-associated infections (HAIs) is increasingly widespread. This is driven by multiple factors: a greater burden on hospitals to provide surveillance data to state and national agencies, financial pressures to be more efficient with HAI surveillance, the desire for more objective comparisons between healthcare facilities, and the increasing amount of patient data available electronically. Optimal implementation of electronic surveillance requires that specific information be available to the surveillance systems. This white paper reviews different approaches to electronic surveillance, discusses the specific data elements required for performing surveillance, and considers important issues of data validation.

Infect Control Hosp Epidemiol 2014;35(9):1083-1091


Corresponding author

Infectious Diseases Division, Washington University School of Medicine, Campus Box 8051, 660 South Euclid Avenue, St. Louis, MO 63110 (


Hide All
1. Lee, TB, Montgomery, OG, Marx, J, Olmsted, RN, Scheckler, WE; Association for Professionals in Infection Control and Epidemiology. Recommended practices for surveillance: Association for Professionals in Infection Control and Epidemiology (APIC), Inc. Am J Infect Control 2007;35:427440.
2. Emori, TG, Edwards, JR, Culver, DH, et al. Accuracy of reporting nosocomial infections in intensive-care-unit patients to the National Nosocomial Infections Surveillance System: a pilot study. Infect Control Hosp Epidemiol 1998;19:308316.
3. Lin, MY, Hota, B, Khan, YM, et al. Quality of traditional surveillance for public reporting of nosocomial bloodstream infection rates. JAMA 2010;304:20352041.
4. Doherty, J, Noirot, LA, Mayfield, J, et al. Implementing GermWatcher, an enterprise infection control application. AMIA Annu Symp Proc 2006:209213.
5. Evans, RS, Larsen, RA, Burke, JP, et al. Computer surveillance of hospital-acquired infections and antibiotic use. JAMA 1986;256:10071011.
6. Halpin, H, Shortell, SM, Milstein, A, Vanneman, M. Hospital adoption of automated surveillance technology and the implementation of infection prevention and control programs. Am J Infect Control 2011;39:270276.
7. Woeltje, KF, Butler, AM, Goris, AJ, Tutlam, NT, Doherty, JA, Westover, MB, Ferris, V, Bailey, TC. Automated surveillance for central line-associated bloodstream infection in intensive care units. Infect Control Hosp Epidemiol 2008;29:842846.
8. Wright, MO. Automated surveillance and infection control: towards a better tomorrow. Am J Infect Control 2008;36:S1S6.
9. KLAS Enterprises. Infection Control 2011: Better tools + More data = Less Infection? Orem, Utah: KLAS Enterprises, 2011.
10. Trick, WE, Zagorski, BM, Tokars, JI, et al. Computer algorithms to detect bloodstream infections. Emerg Infect Dis 2004;10:16121620.
11. Hota, B, Lin, M, Doherty, JA, et al; CDC Prevention Epicenter Program. Formulation of a model for automating infection surveillance: algorithmic detection of central-line associated bloodstream infection. J Am Med Inform Assoc 2010;17:4248.
12. Woeltje, KF, McMullen, KM, Butler, AM, Goris, AJ, Doherty, JA. Electronic surveillance for healthcare-associated central line-associated bloodstream infections outside the intensive care unit. Infect Control Hosp Epidemiol 2011;32:10861090.
13. Klompas, M, McVetta, J, Lazarus, R, et al. Integrating clinical practice and public health surveillance using electronic medical record systems. Am J Pub Health 2012;102(suppl 3):S325S332.
14. Klompas, M, Yokoe, DS. Automated surveillance of health care-associated infections. Clin Infect Dis 2009;48:12681275.
15. van Mourik, MS, Troelstra, A, van Solinge, WW, Moons, KG, Bonten, MJ. Automated surveillance for healthcare-associated infections: opportunities for improvement. Clin Infect Dis 2013;57:8593.
16. Boonstra, A, Broekhuis, M. Barriers to the acceptance of electronic medical records by physicians from systematic review to taxonomy and interventions. BMC Health Serv Res 2010;10:231.
17. Centers for Disease Control and Prevention/National Healthcare Safety Network. Surveillance definition of healthcare-associated infection and criteria for specific types of infections in the acute care setting. Accessed June 24, 2013.
18. Lukenbill, J, Rybicki, L, Sekeres, MA, et al. Defining incidence, risk factors, and impact on survival of central line-associated blood stream infections following hematopoietic cell transplantation in acute myeloid leukemia and myelodysplastic syndrome. Biol Blood Marrow Transplant 2013;19:720724.
19. National Healthcare Safety Network (NHSN). NHSN manual for CAUTI surveillance. Accessed June 9, 2012.
20. National Healthcare Safety Network. National Healthcare Safety Network newsletter 2011; 6(1). Accessed July 29, 2013.
21. Wright, MO, Fisher, A, John, M, Reynolds, K, Peterson, LR, Robicsek, A. The electronic medical record as a tool for infection surveillance: successful automation of device-days. Am J Infect Control 2009;37:364370.
22. Fakih, MG, Greene, MT, Kennedy, EH, et al. Introducing a population-based outcome measure to evaluate the effect of interventions to reduce catheter-associated urinary tract infection. Am J Infect Control 2012;40:359364.
23. Magill, SS, Klompas, M, Balk, R, et al. Developing a new, national approach to surveillance for ventilator-associated events. Crit Care Med 2013;41(11):24672475.
24. Centers for Disease Control and Prevention (CDC)/National Healthcare Safety Network (NHSN). CDC/NHSN VAE surveillance protocol. Accessed December 23, 2013.
25. Yokoe, DS, Khan, Y, Olsen, MA, et al.; Centers for Disease Control and Prevention Epicenters Program. Enhanced surgical site infection surveillance following hysterectomy, vascular, and colorectal surgery. Infect Control Hosp Epidemiol 2012;33:768773.
26. Calderwood, MS, Ma, A, Khan, YM, et al.; CDC Prevention Epicenters Program. 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;33:4049.
27. Olsen, MA, Fraser, VJ. Use of diagnosis codes and/or wound culture results for surveillance of surgical site infection after mastectomy and breast reconstruction. Infect Control Hosp Epidemiol 2010;31:544547.
28. Bolon, MK, Hooper, D, Stevenson, KB, et al.; Centers for Disease Control and Prevention Epicenters Program. Improved surveillance for surgical site infections after orthopedic implantation procedures: extending applications for automated data. Clin Infect Dis 2009;48:12231229.
29. Yokoe, DS, Noskin, GA, Cunnigham, SM, et al. Enhanced identification of postoperative infections among inpatients. Emerg Infect Dis 2004;10:19241930.
30. Totten, AM, Wagner, J, Tiwari, A, et al. Closing the Quality Gap: Revisiting the State of the Science. Vol 5, Public Reporting as a Quality Improvement Strategy. Rockville, MD: Agency for Healthcare Research and Quality, 2012. Evidence reports/technology assessments, no. 208.5.
31. Centers for Medicare and Medicaid Services. Medicare Program; Hospital Inpatient Prospective Payment Systems for Acute Care Hospitals and the Long-Term Care Hospital Prospective Payment System and Fiscal Year 2014 Rates; Quality Reporting Requirements for Specific Providers; Hospital Conditions of Participation; Payment Policies Related to Patient Status. Fed Regist 2013;78:5049551040.–18956.
32. Arnold, KE, Thompson, ND. Building data quality and confidence in data reported to the National Healthcare Safety Network. Infect Control Hosp Epidemiol 2012;33:446448.
33. Lin, MY, Hota, B, Khan, YM, et al. Quality of traditional surveillance for public reporting of nosocomial bloodstream infection rates. JAMA 2010;304:20352041.
34. Mayer, J, Greene, T, Howell, J, et al. Agreement in classifying bloodstream infections among multiple reviewers conducting surveillance. Clin Infect Dis 2012;55:364370.
35. National Healthcare Safety Network (NHSN). NHSN validation guidance and toolkit 2012. Validation for central line–associated bloodstream infection (CLABSI) in ICUs. Accessed April 1, 2013.
36. Tejedor, SC, Garrett, G, Jacob, JT, et al. Electronic documentation of central venous catheter–days: validation is essential. Infect Control Hosp Epidemiol 2013;34(9):900907.
37. Backman, LA, Melchreit, R, Rodriguez, R. Validation of the surveillance and reporting of central line–associated bloodstream infection data to a state health department. Am J Infect Control 2010;38:832838.
38. Kainer, MA, Mitchell, J, Frost, BA, Soe, MM. Validation of central line associated blood stream infection (CLABSI) data submitted to the National Healthcare Safety Network (NHSN): a pilot study by the Tennessee Department of Health (TDH). In: Program and Abstracts of the Fifth Decennial International Conference on Healthcare-Associated Infections. Atlanta, GA: Society for Healthcare Epidemiology of America, Centers for Disease Control and Prevention, Association for Professionals in Infection Control and Epidemiolgy, Infectious Diseases Society of America; March 1822, 2010. Abstract 456.
39. Oh, JY, Cunningham, MC, Beldavs, ZG, et al. Statewide validation of hospital-reported central line–associated bloodstream infections: Oregon, 2009. Infect Control Hosp Epidemiol 2012;33:439445.
40. Trick, WE. Decision making during healthcare-associated infection surveillance: a rationale for automation. Clin Infect Dis 2013;57:434440.
41. Rubin, MA, Mayer, J, Greene, T, et al. An agent-based model for evaluating surveillance methods for catheter-related bloodstream infection. AMIA Annu Symp Proc 2008:631635.


Altmetric attention score

Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed