The following article was originally published in Preventing Infection in Ambulatory Care, the quarterly e-publication from the Association for Professionals in Infection Control and Epidemiology (APIC). To learn more about receiving this resource and joining APIC, visit www.apic.org/ambulatorynewsletter. To learn more about APIC, visit www.apic.org.
Infection surveillance is increasingly becoming an area of focus and challenge in ambulatory healthcare settings as mandatory report requirements expand. Twenty-five years ago in 1986, R. Scott Evans, MS, PhD, FACMI, published details of his successful infection surveillance automation as a method of enhancing infection prevention programs. Since then, a number of data mining programs have come to market, designed to assist the infection preventionist (IP) by automating a number of functions such as outbreak identification, institution of appropriate isolation, monitoring of compliance with appropriate isolation, identification of pathogens of interest, communicable disease reporting, and some direct data exportation to the National Healthcare Safety Network (NHSN).
To assist the IP in assessing these products, APIC published the "Infection Prevention and Control Surveillance Technology Assessment Tool" in 2007. The infection detection capability of many of these software programs is still rudimentary because most are laboratory-based. However, it is possible to design reports within an electronic medical record (EMR) that can detect infections, collate denominator data, and look for pathogens of interest.
Whether software or home grown reports, automated infection surveillance can reduce diversion of the IP's time and improve outcomes. Read APIC's position paper titled "The Importance of Surveillance Technologies in the Prevention of Healthcare-Associated Infections (HAIs)" to learn more about the benefits. In a study conducted from October 2008 through January 2009 by the University of California, 32.4 percent of California's 241 acute care hospitals were noted to have adopted automated surveillance technology. Researchers found that these hospitals were more likely than those manually tracking infections to have fully implemented research-based practices to reduce methicillin-resistant Staphylococcus aureus (MRSA) infections by 85 percent vs. 66 percent, ventilator-associated pneumonia (VAP) infections by 96 percent vs. 88 percent, and surgical site infections (SSIs) by 91 percent vs. 82 percent.
Reducing infection surveillance time is increasingly imperative in both ambulatory and inpatient arenas as public reporting mandates for infection data increase across the nation (via the Centers for Medicare and Medicaid Services, The Joint Commission and state legislation). In parallel, infection risks continue to escalate with the advent of new treatments, technologies, products, pathogens of interest and continual transition of many healthcare services from inpatient to ambulatory settings. Now more than ever, IPs are needed at the front lines of patient care instead of behind a computer – both in ambulatory and inpatient settings. They are needed in leading performance improvement projects, participating in ICU rounds, and providing education, consultation and input on product selection. Automating surveillance functions is a strategy that can re-focus IP time on infection prevention priorities.
In 2010, one large integrated healthcare system developed an automated infection detection (numerator) report as a screening tool to identify patients who had developed SSIs after hernia procedures. This was undertaken in order to support one of the ambulatory surgery centers required by state legislation to report hernia procedure SSIs to NHSN. The following report "triggers" were used to detect infection: (1) Antibiotic order >48 hours and <30 days after procedure and/or (2) Wound culture order within 30 days post op and/or (3) Diagnosis of SSI by the International Statistical Classification of Diseases and Related Health Problems (ICD)-9 code within 30 days post op and/or (4) Emergency department visit or hospital admission within 30 days of procedure (claims data where EMR not used). A second, (denominator) report was designed to automatically collate more than two-thirds of denominator data elements required by NHSN for any surgical procedure.
The accuracy of the SSI report was compared to traditional manual infection surveillance. It was first compared to hernia procedures, and then to total hip, total knee, colon, laminectomy, spine fusion, breast, and abdominal hysterectomy procedures. Accuracy was found to be equal for all procedures, and in some instances, even better. Surveillance and reporting time to NHSN was reduced by approximately 80 percent, and the report is now available to hospital-based IPs in the same healthcare system. The SSI report is now being used as a screening tool and record review is performed for the small set of patients identified in order to confirm or exclude the infection cases.
This approach will now be applied in development of additional automated infection detection reports including central line-associated bloodstream infections, VAP, Clostridium difficile, MRSA and vancomycin-resistant enterococci. These reports are critical tools for reducing diversion of IP resources and have the potential also to improve the quality of surveillance data by standardizing and increasing the reliability of surveillance processes and reducing inter-rater reliability issues.
For more information on the surveillance reports, please contact: firstname.lastname@example.org.
1. Evans RS, Larsen RA, Burke JP, Gardner RM, Meier FA, Jacobsen JA, et al. Computer Surveillance of Hospital-acquired Infections and antibiotic use. JAMA 1986; 256(8):1007-1011.
2. APIC. Infection Prevention and Control Surveillance Technology Assessment Tool. March 5, 2007. Available at www.apic.org. Accessed August 2011.
3. Green LR, Cain TA, Khoury R, Krystofiak SP, Patrick M, Streed S. APIC Position Paper: The Importance of Surveillance Technologies in the Prevention of Healthcare-Associated Infections (HAIs). Available at www.apic.org. Accessed September 2011.
4. 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 May; 39(4):270-6.