Hostname: page-component-586b7cd67f-dlnhk Total loading time: 0 Render date: 2024-12-01T12:07:51.445Z Has data issue: false hasContentIssue false
  • English
  • Français

Data Requirements for Electronic Surveillance of Healthcare-Associated Infections

Published online by Cambridge University Press:  10 May 2016

Keith F. Woeltje ,
Michael Y. Lin ,
Michael Klompas ,
Marc Oliver Wright ,
Gianna Zuccotti  and
William E. Trick
Keith F. Woeltje*
Affiliation:
Center for Clinical Excellence, BJC HealthCare, and Department of Medicine, Washington University School of Medicine, St. Louis, Missouri
Michael Y. Lin
Affiliation:
Department of Medicine, Rush University Medical Center, Chicago, Illinois
Michael Klompas
Affiliation:
Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
Marc Oliver Wright
Affiliation:
Departments of Quality and Infection Control, NorthShore University HealthSystem, Evanston, Illinois
Gianna Zuccotti
Affiliation:
Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts Partners eCare, Partners HealthCare, Boston, Massachusetts
William E. Trick
Affiliation:
Department of Medicine, Cook County Health and Hospitals System, Chicago, Illinois
*
Infectious Diseases Division, Washington University School of Medicine, Campus Box 8051, 660 South Euclid Avenue, St. Louis, MO 63110 ([email protected]).
Get access Rights & Permissions [Opens in a new window]

Extract

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

Type
SHEA White Paper
Information
Infection Control & Hospital Epidemiology , Volume 35 , Issue 9 , 01 September 2014 , pp. 1083 - 1091
Copyright
© 2014 by The Society for Healthcare Epidemiology of America. All rights reserved.

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

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;35:427–440.Google Scholar
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;19:308–316.CrossRefGoogle Scholar
3. Lin, MY, Hota, B, Khan, YM, et al. Quality of traditional surveillance for public reporting of nosocomial bloodstream infection rates. JAMA 2010;304:2035–2041.CrossRefGoogle ScholarPubMed
4. Doherty, J, Noirot, LA, Mayfield, J, et al. Implementing GermWatcher, an enterprise infection control application. AMIA Annu Symp Proc 2006:209–213.Google ScholarPubMed
5. Evans, RS, Larsen, RA, Burke, JP, et al. Computer surveillance of hospital-acquired infections and antibiotic use. JAMA 1986;256:1007–1011.Google Scholar
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;39:270–276.CrossRefGoogle ScholarPubMed
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;29:842–846.CrossRefGoogle ScholarPubMed
8. Wright, MO. Automated surveillance and infection control: towards a better tomorrow. Am J Infect Control 2008;36:S1–S6.Google Scholar
9. KLAS Enterprises. Infection Control 2011: Better tools + More data = Less Infection? Orem, Utah: KLAS Enterprises, 2011.Google Scholar
10. Trick, WE, Zagorski, BM, Tokars, JI, et al. Computer algorithms to detect bloodstream infections. Emerg Infect Dis 2004;10:1612–1620.CrossRefGoogle ScholarPubMed
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;17:42–48.CrossRefGoogle Scholar
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;32:1086–1090.Google Scholar
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):S325–S332.Google Scholar
14. Klompas, M, Yokoe, DS. Automated surveillance of health care-associated infections. Clin Infect Dis 2009;48:1268–1275.CrossRefGoogle ScholarPubMed
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;57:85–93.Google Scholar
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.Google Scholar
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. http://www.cdc.gov/nhsn/pdfs/pscmanual/17pscnosinfdef_current.pdf. Accessed June 24, 2013.Google Scholar
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;19:720–724.Google Scholar
19. National Healthcare Safety Network (NHSN). NHSN manual for CAUTI surveillance. http://www.cdc.gov/nhsn/PDFs/pscManual/7pscCAUTIcurrent.pdf. Accessed June 9, 2012.Google Scholar
20. National Healthcare Safety Network. National Healthcare Safety Network newsletter 2011; 6(1). http://www.cdc.gov/nhsn/PDFs/Newsletters/NHSN_NL_MAR_2011_final.pdf. Accessed July 29, 2013.Google Scholar
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;37:364–370.CrossRefGoogle ScholarPubMed
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;40:359–364.Google Scholar
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):2467–2475.CrossRefGoogle ScholarPubMed
24. Centers for Disease Control and Prevention (CDC)/National Healthcare Safety Network (NHSN). CDC/NHSN VAE surveillance protocol. http://www.cdc.gov/nhsn/PDFs/pscManual/10-VAE_FINAL.pdf. Accessed December 23, 2013.Google Scholar
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;33:768–773.Google Scholar
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;33:40–49.Google Scholar
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;31:544–547.Google Scholar
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;48:1223–1229.CrossRefGoogle ScholarPubMed
29. Yokoe, DS, Noskin, GA, Cunnigham, SM, et al. Enhanced identification of postoperative infections among inpatients. Emerg Infect Dis 2004;10:1924–1930.CrossRefGoogle ScholarPubMed
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. http://www.ncbi.nlm.nih.gov/books/NBK99879/.Google Scholar
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;78:50495–51040. https://federalregister.gov/a/2013–18956.Google Scholar
32. Arnold, KE, Thompson, ND. Building data quality and confidence in data reported to the National Healthcare Safety Network. Infect Control Hosp Epidemiol 2012;33:446–448.CrossRefGoogle Scholar
33. Lin, MY, Hota, B, Khan, YM, et al. Quality of traditional surveillance for public reporting of nosocomial bloodstream infection rates. JAMA 2010;304:2035–2041.Google Scholar
34. Mayer, J, Greene, T, Howell, J, et al. Agreement in classifying bloodstream infections among multiple reviewers conducting surveillance. Clin Infect Dis 2012;55:364–370.CrossRefGoogle ScholarPubMed
35. National Healthcare Safety Network (NHSN). NHSN validation guidance and toolkit 2012. Validation for central line–associated bloodstream infection (CLABSI) in ICUs. http://www.cdc.gov/nhsn/toolkit/validation-clabsi/index.html. Accessed April 1, 2013.Google Scholar
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):900–907.Google Scholar
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;38:832–838.Google Scholar
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 18–22, 2010. Abstract 456.Google Scholar
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;33:439–445.Google Scholar
40. Trick, WE. Decision making during healthcare-associated infection surveillance: a rationale for automation. Clin Infect Dis 2013;57:434–440.Google Scholar
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 2008:631–635.Google Scholar