infection prevention
By Sue Barnes , RN , CIC , FAPIC
A Call for Standardized Automation : HAI Surveillance in the 21st Century
Infection surveillance data is used to measure the success of IP & C interventions , to identify areas for improvement , to assess the benefit of innovative products and practices , and to meet public reporting mandates and pay for performance goals .
Prevention and control of healthcare-associated infections ( HAIs ) became a greater priority in U . S . hospitals after publication of the 1999 Institute of Medicine ( IOM ) report , To Err is Human , which focused on avoidance of preventable harm . 1 One component of infection prevention and control ( IP & C ) programs is surveillance of HAIs , which was originally the primary function of the infection preventionist ( IP ). However , the scope of these programs has massively expanded ever since , with prevention and control of infections now the primary imperative , and surveillance just one tool . Infection surveillance data is used to measure the success of IP & C interventions , to identify areas for improvement , to assess the benefit of innovative products and practices , and to meet public reporting mandates and pay for performance goals . In hospitals where semi-automated infection surveillance software is not available , HAI surveillance is accomplished manually . Both methods provide data which can be subject to inter-rater reliability deficiency , inaccuracy , and for the manual process subjectivity as well . 2 , 3 For manual surveillance these limitations are due in part to variable interpretation of the complex HAI definitions , but also to simple human error . In addition , though HAI definitions are standardized by the Centers for Disease Control and Prevention ( CDC ), the process for manual infection detection has never been standardized . Similarly , for infection surveillance software there is no standard infection detection algorithm , and this is variable among the many software programs . In addition , infection surveillance software report accuracy is dependent upon the completeness of clinical documentation in the electronic medical record ( EMR ).
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Legislative Support and Funding for Infection Prevention and Control
The Health and Human Services department ( HHS ) recently announced an $ 80 million plan focused in part on information technology ( IT ) development , but for the public health sector , not hospitals . 4 The current HHS National Action Plan to Prevent HAI addresses the limitations of hospital based HAI surveillance processes in general terms , though not cost of infection surveillance software or the lack of algorithm standardization for infection detection . 5 The 2022 HHS Justification of Estimates for Appropriations Committees report reflects that there is $ 35.7 million designated to advance the generation of new knowledge and promote the application of proven methods for preventing HAIs . 6 However , there is no mention of directing any of these funds toward standardizing algorithms for electronic HAI surveillance , or making these systems equally available to all hospitals .
Technology - Automated vs . Manual Surveillance and the Need for Standardization
The advent of automated infection detection by applying data mining algorithms to the EMR or via specialized add-on infection surveillance software has been shown to improve the quality of HAI data , reduce the time required when compared to manual infection surveillance , as well as assist in early outbreak identification . 7-10 And given the data reporting burden of the CDC ’ s National Healthcare Safety Network ( NHSN ), the additional automation of data export to NHSN is reported to further protect IP & C resources . 11 However , automation requires a level of information technology ( IT ) support not always accessible to IP & C departments , which in addition to the cost of these programs has resulted in slow adoption . 12 In 2014 it was estimated that only 45 percent of U . S . hospitals used electronic infection surveillance systems . 13 And many of the software programs available today do not completely eliminate the need for manual record review to confirm case finding . Kenrick Cato , a researcher at the Columbia University School of Nursing has proposed that , “ If monitoring systems could be fully automated , it would revolutionize the way infection surveillance is done .” 13 One study reports that this has be done in at least one location , further concluding that “ automated detection of HAIs according to NHSN surveillance definitions , can bridge the gap between retrospective surveillance of HAIs and prospective clinical-decision-oriented HAI support .” 14 , 15 According to a 2020 study , we are still at least a decade away from widely utilizing electronic HAI surveillance due in part to lack of focus and funding by government , which could make automated surveillance available to every hospital IP & C department . 16
HAI surveillance remains central to prevention and control efforts ; however , HAI case finding as well as reporting to NHSN is resource-intensive , subject to inaccuracy due human error , and is hampered by a lack of standardization of manual and automated infection detection processes , which also challenges accurate inter-facility comparison . In addition , HAI data is retrospect , which prevents intervention in real time to expedite effective treatment .