Implementing a Centralized Surveillance and Validation Program for Infection Prevention

Abstract

Background: Mandatory reporting of all healthcare-associated infections (HAIs) leads to substantial surveillance volume for infection prevention and control (IPC) programs. Prior to 2019, 6 infection preventionists were performing system-wide surveillance for all infection types using NHSN definitions at a large quaternary-care center in Pennsylvania. Limited surveillance validation was performed. With the continued expansion of the health system, increased demands for IPC expertise, and a growing team, the need for streamlined surveillance, and a validation program were identified. Methods: A surveillance training program for novice team members was developed and implemented. Infection prevention associates (IPAs), whose primary role was data management, began training. The new program included NHSN training videos, direct observation of surveillance with infection preventionists and practice case studies. Following training, IPAs performed surveillance for experienced infection preventionists covering high-risk inpatient units. To ensure high reliability, surveillance validation was initiated. Each month, ~10% of investigated infections were randomly pulled from the electronic surveillance system and divided among experienced infection preventionists. These validators performed unbiased reviews of the charts based on limited data, including patient demographics and culture results. Validation documentation included noting whether an infection was reportable to NHSN and a rationale. Data on whether or not each patient had a complex medical history and time spent validating each case were collected. Compliance of validator documentation aligning with original documentation was tracked. Discrepancies were discussed as a team and were adjudicated as needed. IPAs tracked hours spent on surveillance to capture effort transitioned from infection preventionists. Results: Between March and July 2019, an average of 223 (range, 178–261) potential infections were reviewed per month. From March through June 2019, 61 infections were selected for validation, with 98% compliance with original documentation. One minor discrepancy was attributed to interpretation of documentation in the medical record. Medical complexity accounted for 78% of reviews and validation time spent averaged 12 minutes per infection (range, 3–28 minutes). Self-reported effort directed from infection preventionists to 2 IPAs for surveillance was ~20 hours per week. An additional IPA was hired to perform surveillance in addition to other job responsibilities. Conclusions: Centralized surveillance programs can promote high reliability and cost-efficient IPC staffing for large healthcare systems, especially those with mandatory reporting requirements or medically complex patient populations. Improving surveillance skills among associate staff can increase experienced infection preventionist bandwidth for project management, staff supervision, and other leadership responsibilities. Lastly, validation programs are crucial to ensuring quality assurance of data reporting to both internal and external stakeholders.Funding: NoneDisclosures: None

Development and implementation of a centralized surveillance infection prevention program in a multi-facility health system: A quality improvement project

“…They will be important to multiple-facility health systems considering a centralized model for HAI surveillance, and they describe a model that may improve patient safety in acute care settings. 9 , 15 …”

“…They will be important to multiple-facility health systems considering a centralized model for HAI surveillance, and they describe a model that may improve patient safety in acute care settings. 9,15 When reporting on LIP and CSIP efficiency and activities, there are some limitations. There are no robust measures of time and efficiency; efficiency was lower than we anticipated for CSIP infection preventionists and more variable than we anticipated for LIPs.…”

Healthcare-associated infections during the coronavirus disease 2019 (COVID-19) pandemic and the modulating effect of centralized surveillance

“…These results will be important to multiplefacility health systems considering centralized models for HAI surveillance, and our findings provide a suggestion that such a model may improve patient safety in acute-care settings. 8,9 CSIP programs are ultimately intended to reduce HAI. Our data provide an indeterminate answer regarding whether CSIP program adoption reduces HAI, and these data are confounded by the COVID-19 pandemic.…”

“…These results will be important to multiple-facility health systems considering centralized models for HAI surveillance, and our findings provide a suggestion that such a model may improve patient safety in acute-care settings. 8 , 9 …”