Infection Control Today

DEC 2018

ICT delivers to infection preventionists & their colleagues in the operating room, sterile processing/central sterile, environmental services & materials management, timely & relevant news, trends & information impacting the profession & the industry

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Page 12 of 32

12 ICT December 2018 Borlaug continues, "Many hospitals still do not have electronic surveillance systems, however, despite their use of EMRs. Why? Because, with the exception of electronic transmission of antibiotic use and resistance data, healthcare entities do not have to implement systems with electronic surveillance capacity to receive fnancial incentives from CMS. Thus, the challenge for facilities is justifying the additional cost of digital health information systems that provide electronic surveillance capacity and providing information technology (IT) support for maintaining and updating electronic algorithms and automatic data importation and exportation capacity. Most electronic health systems vendors do not provide that support, resulting in additional burdens for facility IT departments as they take on these tasks. On the other hand, the availability of electronic surveillance systems provides the opportunity for healthcare facilities to evaluate the costs of current labor-intensive manual surveillance methods and explore how electronic surveillance systems can lead to more effcient HAI surveillance and ultimately enhanced HAI prevention efforts." Russo, et al. (2018) acknowledge the demands put on IPs by surveillance activities: "Surveillance is a cyclical process encompassing recognition of an event, data collection, analysis, interpretation and dissemination. Surveillance of HAIs is a highly resource-intensive activity, with 'traditional' surveillance methods involving the infection prevention (IP) staff in exclusively manual data collection processes that are time-consuming, resource intensive, and generate data of variable quality. It has been reported that up to 45 percent of IP staff time is dedicated to undertaking surveillance. A recent Australian study identifed that IP staff spend 36 percent of their time on surveillance. The proportion of overall surveillance time spent on data collection has not been identifed. It is also suggested that the annual staffng costs of IP staff (nursing component) in Australian hospitals could be AU$100 million. This represents signifcant resources devoted to HAI surveillance." Russo, et al. (2018) conducted a systematic review on the impact of electronic surveillance software (ESS) on infection preventionist (IP) resources, fnding 13 studies that demonstrated a reduction in IP staff time to undertake surveillance-related activities. The reduction proportion ranged from 12.5 percent to 98.4 percent (mean: 73.9 percent). The researchers explain, "Of the 16 studies, estimates in the reduction in time spent on surveillance were calculated for 13. Six of these 13 studies reported a reduction in time of ≥90 percent, and all but two suggested a reduction of >50 percent. The range of reduction identifed varied from 12.5 percent to 98.4 percent (mean: 73.9 percent). Two studies demonstrated a time reduction of 98.4 percent. Six studies supplied data to compare the proportion of overall time pre- and post-ES; of these, four were statistically signifcant. The researchers clarify, "Half of the studies identifed were conducted in the U.S., possibly refecting a more advanced implementation of electronic medical records and automated data retrieval. Despite the availability of commercial surveillance products, it is interesting to note the common use of in-house systems. Grota, et al.'s large cross-sectional study reported that the split between in-house and commercial products was almost half, and this is similar to the fndings from this review. This may be explained by an iterative in-house development process over many years within a hospital, favoring a local product over an external commercial product. Alternatively, it may refect the heterogeneity of existing hospital information technology systems, making implementation of an off-the-shelf ESS product that requires modifcation at each site less attractive and less feasible. Russo, et al. (2018) emphasize that "In an environment of public reporting, fnancial penalties and increasing demands on infection prevention resources, ESS has the advantage of minimizing the subjective nature of many manual surveillance activities that lead to variable accuracy, making them ideal for large scale implementation. Furthermore, combining the time benefts this review suggests that, with the recent demonstrated advantages of surveillance algorithms that can be built into ESS, the case for implementing an ESS is strengthened. Many infection prevention interventions fail due to a lack of careful implementation planning and associated resourcing. The planning for the introduction of an ESS would require a comprehensive implementation strategy to ensure uptake …Although the studies included in this review demonstrate that the use of ESS requires less IP time to undertake the same surveillance, it is important to understand that this may not equate to IP teams spending less time on surveillance overall. Time effciencies identifed through the use of ESS could potentially enhance the identifcation and response to possible outbreaks, or indeed extend the scope of surveillance activities, thereby improving the quality of HCAI surveillance undertaken. In an era of gradual transition to electronic medical records, it is imperative that data technology is in alignment with the requirements of IP, and there is scope for further research in this area. To be convinced of the benefts of ESS, we suggest that research is required in several areas: the effect of ESS on infection prevention resource as a primary outcome; how IP staff redirect their resources following the introduction of ESS, and whether this has any impact on infection and/or patient outcomes; and the infuence of ESS on HAI rates." Hebden (2015) explored the case for automated infection prevention surveillance, observing that, "The slow adoption of automated technology seems surprising in light of the time-consuming nature of manual surveillance and nearly three decades having passed since the frst report detailing a computer system using microbiology data to identify patients with possible HAIs. In recent years, supportive evidence for the use of automated surveillance includes a 61 percent reduction in time spent on surveillance activities, achievement of greater depth in the implementation of evidence-based infection control practices, and improvement in implementation of isolation practices." Ò T he slow adoption of automated technolog y seems surprising in light of the time-consuming nature of manual surveillance and nearly three decades having passed since the f irst report detailing a computer system using microbiolog y data to identif y patients with possible HAIs.

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