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 18 of 32

18 ICT December 2018 this was a big mistake, and I quickly learned that these individuals are key players in conducting HAI surveillance. In the milieu of more complex surveillance defnitions and methods, reporting burdens and evolving technology, it is increasingly imperative for IPs to use a team approach to HAI surveillance, through education of healthcare personnel and providers regarding HAI surveillance processes and case defnitions so that they have an understanding of how their practices (e.g., ordering of cultures, medical records documentation), affect HAI case detection. Keeping up with rapidly evolving laboratory and information management technology is also a challenge, and IPs should not hesitate to reach out to these key partners for assistance in learning and implementing current technology tools." Borlaug continues, "Finally, as I conducted HAI surveillance data validation exercises among dozens of acute care and critical access hospitals, occasional faws in electronic surveillance algorithms were revealed, hence implementation of electronic surveillance methods requires active engagement, oversight, and ongoing evaluation and validation by the IP to ensure the accuracy and reliability of electronic methods. And whether electronic or manual surveillance methods are employed, both internal (conducted by facility staff) and external (conducted by an outside agency such as public health departments or independent IP consultants) data validation exercises can help detect systematic surveillance errors and improve data reliability." Looking toward the future of surveillance, Hebden (2015) summarizes, "Although automated surveillance technology has been evolving for decades, adoption of these technologies is in a nascent state. The current trajectory of public reporting, continued emergence of multidrug-resistant organisms, and mandated antimicrobial stewardship initiatives will result in an increased surveillance workload for IPs. The use of traditional surveillance methods will be ineffcient in meeting the demands for more data and are potentially fawed by subjective interpretation. An examination has been offered the slow adoption of automated surveillance technology from a system perspective with the inherent ambiguities that may operate within the IP work structure. Formal qualitative research is needed to assess the human factors associated with lack of acceptance of automated surveillance systems. Identifcation of these factors will allow the NHSN and professional organizations to offer educational programs and mentoring to the IP community that target knowledge defcits and the embedded culture that embraces the status quo. With the current focus on fully electronic surveillance systems that perform surveillance in its entirety without case review, effective use of the data will be dependent on IPs' skills and their understanding of the strengths and limitations of output from algorithmic detection models." As Freeman, et al. (2013) observe, "Automated methods for the identifcation of HAI allow the consistent application of simplified definitions designed for the purpose of surveillance. ESS should be seen as an opportunity to enhance current surveillance practices. Staff involved in surveillance activities should not feel threatened by advances in this area but should recognize that these methods can reduce the burdens associated with traditional surveillance methodologies, which will only increase as the emphasis on transparency and public reporting causes increased demand for more information to be reported." References: de Bruin JS, Seeling W and Schuh C. Data use and effectiveness in electronic surveillance of healthcare-associated infections in the 21st century: a systematic review. J Am Med Inform Assoc, 21; Pp. 942-951. 2014. Freeman R, Moore LSP, García Álvarez L, Charlett A and Holmes A. Advances in electronic surveillance for healthcare-associated infections in the 21st century: a systematic review. J Hosp Infect, 84; Pp. 106-119. 2013. Grota PG, Stone PW, Jordan S, Pogorzelska M and Larson E. Electronic surveillance systems in infection prevention: organizational support, program characteristics, and user satisfaction. Am J Infect Control, 38 (2010), pp. 509-514 Halpin H, Shortell SM, Milstein A and Vanneman M. Hospital adoption of automated surveillance technology and the implementation of infection prevention and control programs. Am J Infect Control, 39 (2011), pp. 270-276. Hebden JN. Slow adoption of automated infection prevention surveillance: are human factors contributing? Am J Infect Control, 43 (2015), pp. 559-562 Masnick M, Morgan DJ, Wright MO, Lin MY, Pineles L and Harris AD.SHEA Research Network. Survey of infection prevention informatics use and practitioner satisfaction in US hospitals. Infect Control Hosp Epidemiol, 35 (2014), pp. 891-893 Russo PL, Shaban RZ, Macbeth D, Carter A and Mitchell GB. Impact of electronic healthcare-associated infection surveillance software on infection prevention resources: a systematic review of the literature. Journal of Hospital Infection. Vol. 99, No. 1. Pages 1-7. May 2018. Recommended reading: Atreja A, et al. Opportunities and challenges in utilizing electronic health records for infection surveillance, prevention, and control. Am J Infect Control, 36 (3 Suppl) (2008), pp. S37-S46. Hebden JN, et al. Leveraging surveillance technology to beneft the practice and profession of infection control. Am J Infect Control, 36 (2008), pp. S7-S11. Keller SC, et al. Variations in identifcation of healthcare- associated infections. Infect Control Hosp Epidemiol, 34 (2013), pp. 678-686. Klompas M and Yokoe DS. Automated surveillance of health care-associated infections. Clin Infect Dis, 48 (2009), pp. 1268- 1275. Lo YS, et al. Improving the work effciency of healthcare- associated infection surveillance using electronic medical records. Comput Methods Programs Biomed, 117 (2014), pp. 351-359. Mitchell BG, et al. Time spent by infection control professionals undertaking healthcare associated infection surveillance: a multi-centred cross sectional study. Infect Dis Health, 21 (2015), pp. 36-40. Perl TM and Chaiwarth R. Surveillance: an overview. Eds: E. Lautenbach, K.F. Woeltje, P.N. Malani, Practical healthcare epidemiology (3rd ed.), University of Chicago Press, London (2010), pp. 111-142. Stone PW, et al. Staffng and structure of infection prevention and control programs. Am J Infect Control, 37 (2009), pp. 351-357. Ò Automated methods for the identif ication of HAI allow the consistent application of simplif ied def initions designed for the purpose of surveillance.

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