Rachel Pryor, RN, MPH, is a nurse specializing in health care data analytics and research for the Health Care Infection Prevention Program at Virginia Commonwealth University Medical Center.

Rachel Pryor, RN, MPH

Infection prevention (IP) practices must evolve with advancing technology. To follow the dictum “first, do no harm,” hospital leaders must remain vigilant in their prioritization of patient safety and seek new technologies in order to align with the Hippocratic oath. Some recent developments in IP technologies include electronic hand hygiene monitoring systems, antimicrobial textiles, ultraviolet C (UV-C) devices, and the optimization of electronic medical records (EMRs) to include decision-support tools and predictive analytics using machine learning to prevent health care–associated infections (HAIs).

Electronic Hand Hygiene Monitoring Systems

Electronic hand hygiene monitoring systems (EHHMSs) offer hospitals large data sets of hand hygiene (HH) compliance among health care workers (HCWs) who wear their badges. Though the World Health Organization recommends direct observation of hand hygiene practices,

 and though that remains the gold standard,

 it is labor intensive, can be biased by the Hawthorne effect, and yields a much smaller denominator than EHHMSs provide. Limitations are inherent in all HH monitoring programs; however, the volume of data that EHHMs offers is of value to many IP programs. In addition to giving IP programs larger data sets of HH compliance, studies show that EHHMSs can increase HH compliance.

Despite the benefits of EHHMSs, barriers exist to widespread adoptability, including negative perceptions of the systems among HCWs,

 variability among the different types of systems,

 and an absence of studies demonstrating improvement in HAI outcomes with EHHMSs.

 Though HCWs routinely recognize the importance of HH and are generally positive about interventions to help improve compliance, Kelly et al found many HCWs worry about the accuracy of the data6 and may feel anxiety about the systems.

 In 2021 Knudsen et al found a statistically significant decrease in health care–associated bloodstream infections with improved HH in their small study,

 and Kovacs-Litman et al reported that hospital outbreaks occurred more often in units with lower HH compliance.

 These studies are encouraging and may lead to greater adoptability, particularly if future studies can reproduce similar results.

Despite mixed results in published studies, antimicrobial textiles (ATs) are
a promising technology for infection prevention. ATs uniquely decrease surface bioburden.

 Although little debate exists on the antimicrobial properties of copper and silver, the HAI prevention impact of impregnating fabrics or surfaces with these metals remains poorly defined.

Fan et al conducted a systematic review and meta-analysis in 2020 of 6 studies and did not find conclusive evidence that copper-impregnated linens decreased HAIs.

 Albarqouni et al conducted an additional systematic review and meta-analysis of 7 studies in 2020 and found the same lack of evidence.

 On a more promising note, Hesselvig et al demonstrated that iodine-impregnated incision drapes did reduce the contamination rate during primary knee arthroplasty compared with using no drape.

 This study does not answer the question of whether intraoperative contamination leads to a subsequent infection and, to date, no studies show a correlation. Madden et al found no decrease in HAIs following the introduction of copper-infused linens.

 However, Butler detected a statistically significant decrease in hospital-onset 

infections in 2018 among patients in 6 different hospitals.

An important consideration in the adoption of antimicrobial linens is wastewater management. These linens are more expensive than traditional hospital linens, so investigators must continue to study whether the savings hospitals may realize through reduction of HAIs through antimicrobial linens is worth the cost of purchasing them.

Much of the recent literature regarding UV-C robots discusses UV-C disinfection and SARS-CoV-2. In January 2022, Viana Martins et al reviewed 60 recent studies and found that UV-C devices inactivated airborne SARS-CoV-2, an important finding in the current fight against the COVID-19 pandemic.

 Although current rates of SARS-CoV-2 infections remain elevated as of this writing, focusing on preventing COVID-19 infections is crucial. Nonetheless, UV-C disinfection remains an important part of preventing many other infections in health care settings. The seminal study, the Benefits of Enhanced Terminal Room (BETR) Disinfection Study—a large, randomized 2016 control trial—found that “patients admitted to rooms previously occupied by patients harboring a multidrug-resistant organism or 

 were [10% to 30%] less likely to acquire the same organism if the room was terminally disinfected using an enhanced strategy,” with the largest risk reduction occurring when a UV-C device was a part of the disinfection strategy.

 This study demonstrated that UV-C light could be used as part of the standard of care for disinfection in rooms where discharged patients have multidrug-resistant organism infections or 

 infections, leading to a change in many health care facilities’ disinfection practices. In 2021, Rock et al found that UV-C disinfection did not decrease vancomycin-resistant enterococcus or 

 infections in immunocompromised patients.

 Although this does not disprove the BETR study’s findings, Rock’s findings suggest that more research about the efficacy of UV-C disinfection in special populations is warranted.

Although UV-C devices can play a critical role in infection prevention programs, for them to provide optimum patient outcomes, health care facilities need a clear strategy for when the devices should be deployed. Like that of EHHMSs, the upfront cost of UV-C devices is significant, so a clear deployment plan helps to ensure the devices are used with consistency.

Optimization of the Electronic Medical Record for Infection Prevention Practices

As EMRs continue to become tools not just for clinician documentation, but also as data collection tools, harnessing the power of EMRs to prevent HAIs becomes increasingly important. Two innovative ways EMRs are being used in this capacity are decision-support tools, helping guide clinicians to make decisions to prevent HAIs; and predictive modeling via machine learning, aiding clinicians and infection programs in targeting patients at greatest risk of developing an HAI.

Decision-support tools are electronic nudges or hard stops that either direct clinicians away from choosing an intervention that may increase the risk of an HAI or disallow a clinician to make that choice at all (typically with an override from a hospital epidemiologist or other hospital leader). Recent studies continue to affirm that electronic nudges improve patient safety by decreasing inappropriate 

 and decreasing inappropriate antibiotic prescribing.

 However, decision-support tools are most likely to be effective when they feel easy to use, useful for decision-making, and reliable.

Preventing infections in hospitalized patients via predictive modeling using machine learning (ML) is still a novel field, but teams of investigators, clinicians, and information technologists are working to create tools that can be broadly adapted to help identify patients who are at greatest risk of developing an HAI. Multiple recent studies demonstrate promising results of a variety of predictive tools to assess who is at greatest risk of a central line–associated bloodstream infection,

 and catheter-associated urinary tract infections.

 Two of these studies found that the models they created outperformed more traditional regression models.

 However, Frondelius et al found fewer positive results in their systematic review of the literature looking at predictive modeling tools and their ability to prevent VAP.

 A limitation to these tools being used regularly and as part of standard infection prevention practice is their newness. More testing and validation must be done before ML can be used to predict at-risk patients consistently and reliably, especially on a large scale. What works at 1 facility must be further validated for other facilities to use the same tool. Extracting correct data from the EMR remains an ongoing challenge at many health care facilities.

Advances in infection prevention generally increase patient safety and decrease hospital costs. The role of infection prevention technologies is evolving. Future studies are required to explore the integration of infection prevention technologies into everyday practice. These investigations should focus on the validation of current technologies, particularly focusing on patient-centered outcomes such as HAI risk reduction. In addition, cost-benefit analyses are required for a better understanding of the potential impact of these technologies on the patient safety mission.

Gonzalo Bearman, MD, MPH, is an epidemiologist, chief of infectious diseases at Virginia Commonwealth University, and editor in chief of Antimicrobial Stewardship & Healthcare Epidemiology (ASHE) from the Society for Healthcare Epidemiology of America.

World Health Organization. WHO guidelines on hand hygiene in health care. January 15, 2009. Accessed July 21, 2022. https://www.who.int/publications/i/item/9789241597906

Stewardson and Pittet, 2014. Hand hygiene G.M. Bearman, M. Stevens, M.B. Edmond, R.P. Wenzel (Eds.), A guide to infection control in the hospital (5th ed.), International Society for Infectious Diseases, Brookline, MA (2014), pp. 22-30

World Health Organization. Systematic literature review of automated/electronic systems for hand hygiene monitoring. preliminary results. Accessed June 30, 2022. 

https://cdn.who.int/media/docs/default-source/integrated-health-services-(ihs)/hand-hygiene/automated-hand-hygiene-monitoring.pdf?sfvrsn=d1db267a_5&Status=Master

Lin TY, Lin CT, Chen KM, Hsu HF. Information technology on hand hygiene compliance among health care professionals: a systematic review and meta-analysis. 

. 2021;29(6):1857-1868. doi:10.1111/jonm.13316

Druckerman DG, Appelbaum N, Armstrong-Novak JD, Masroor N, Cooper K, Stevens MP, Godbout E, Bearman G, Doll ME. Health care worker perceptions of hand hygiene monitoring technologies: Does technology performance matter? Infect Control Hosp Epidemiol. 2021 Dec;42(12):1519-1520. doi: 10.1017/ice.2021.286

Kelly D, Purssell E, Wigglesworth N, Gould DJ. Electronic hand hygiene monitoring systems can be well-tolerated by health workers: Findings of a qualitative study. J Infect Prev. 2021;22(6):246-251. doi:10.1177/17571774211012781

Knudsen AR, Kolle S, Hansen MB, Møller JK. Effectiveness of an electronic hand hygiene monitoring system in increasing compliance and reducing health care-associated infections. J Hosp Infect. 2021 Sep;115:71-74. doi: 10.1016/j.jhin.2021.05.011. Epub 2021 May 29. PMID: 34058262.

Cawthorne KR, Cooke RPD. A survey of commercially available electronic hand hygiene monitoring systems and their impact on reducing health care-associated infections. J Hosp Infect. 2021 May;111:40-46. doi: 10.1016/j.jhin.2021.03.009. Epub 2021 Mar 19. PMID: 33753120.

Kovacs-Litman A, Muller MP, Powis JE, Ricciuto D, McGeer A, Williams V, Kiss A, Leis JA. Association Between Hospital Outbreaks and Hand Hygiene: Insights from Electronic Monitoring. Clin Infect Dis. 2021 Dec 6;73(11):e3656-e3660. doi: 10.1093/cid/ciaa1405. PMID: 32936910.

Madden GR, Heon BE, Sifri CD. Effect of copper-impregnated linens on multidrug-resistant organism acquisition and Clostridium difficile infection at a long-term acute-care hospital. Infect Control Hosp Epidemiol. 2018 Nov;39(11):1384-1386. doi: 10.1017/ice.2018.196. Epub 2018 Sep 20. PMID: 30231949; PMCID: PMC7063582.

Fan T, Shao L, Wang X, Ren P. Efficacy of copper-impregnated hospital linen in reducing health care-associated infections: A systematic review and meta-analysis. PLoS One. 2020 Jul 20;15(7):e0236184. doi: 10.1371/journal.pone.0236184. PMID: 32687517; PMCID: PMC7371175.

Albarqouni L, Byambasuren O, Clark J, Scott AM, Looke D, Glasziou P. Does copper treatment of commonly touched surfaces reduce health care-acquired infections? A systematic review and meta-analysis. J Hosp Infect. 2020 Dec;106(4):765-773. doi: 10.1016/j.jhin.2020.09.005. Epub 2020 Sep 9. PMID: 32916212; PMCID: PMC7480678.

Hesselvig AB, Arpi M, Madsen F, Bjarnsholt T, Odgaard A; ICON Study Group. Does an Antimicrobial Incision Drape Prevent Intraoperative Contamination? A Randomized Controlled Trial of 1187 Patients. Clin Orthop Relat Res. 2020 May;478(5):1007-1015. doi: 10.1097/CORR.0000000000001142. PMID: 32011378; PMCID: PMC7170680.

Butler JP. Effect of copper-impregnated composite bed linens and patient gowns on health care-associated infection rates in six hospitals. J Hosp Infect. 2018 Nov;100(3):e130-e134. doi: 10.1016/j.jhin.2018.05.013. Epub 2018 May 24. PMID: 29803808.

Direct Supply. What You Need to Know About Health care Antimicrobial Textiles. 2020 Apr 27. Accessed 2022 July 1. 

https://www.directsupply.com/blog/what-to-know-about-antimicrobial-textiles/

Viana Martins CP, Xavier CSF, Cobrado L. Disinfection methods against SARS-CoV-2: a systematic review. J Hosp Infect. 2022;119:84-117. doi:10.1016/j.jhin.2021.07.014

Anderson DJ, Chen LF, Weber DJ, Moehring RW, Lewis SS, Triplett PF, Blocker M, Becherer P, Schwab JC, Knelson LP, Lokhnygina Y, Rutala WA, Kanamori H, Gergen MF, Sexton DJ; CDC Prevention Epicenters Program. Enhanced terminal room disinfection and acquisition and infection caused by multidrug-resistant organisms and Clostridium difficile (the Benefits of Enhanced Terminal Room Disinfection study): a cluster-randomised, multicentre, crossover study. Lancet. 2017 Feb 25;389(10071):805-814. doi: 10.1016/S0140-6736(16)31588-4. Epub 2017 Jan 17. PMID: 28104287; PMCID: PMC5935446.

Rock C, Hsu YJ, Curless MS, Carroll KC, Howard TR, Carson KA, Cummings S, Anderson M, Milstone AM, Maragakis LL. Ultraviolet-C Light Evaluation as Adjunct Disinfection to Remove Multi-Drug Resistant Organisms. Clin Infect Dis. 2021 Oct 12:ciab896. doi: 10.1093/cid/ciab896. Epub ahead of print. PMID: 34636853.

Howard-Anderson JR, Sexton ME, Robichaux C, Wiley Z, Varkey JB, Suchindran S, Albrecht B, Ashley Jones K, Fridkin SK, Jacob JT. The impact of an electronic medical record nudge on reducing testing for hospital-onset Clostridioides difficile infection. Infect Control Hosp Epidemiol. 2020 Apr;41(4):411-417. doi: 10.1017/ice.2020.12. Epub 2020 Feb 10. PMID: 32036798; PMCID: PMC7909614.

Wu T, Davis SL, Church B, Alangaden GJ, Kenney RM. Outcomes of clinical decision support for outpatient management of Clostridioides difficile infection. Infect Control Hosp Epidemiol. 2021 Sep 29:1-4. doi: 10.1017/ice.2021.397. Epub ahead of print. PMID: 34583800.

May A, Hester A, Quairoli K, Wong JR, Kandiah S. Impact of Clinical Decision Support on Azithromycin Prescribing in Primary Care Clinics. J Gen Intern Med. 2021 Aug;36(8):2267-2273. doi: 10.1007/s11606-020-06546-y. Epub 2021 Feb 25. PMID: 33634383; PMCID: PMC8342651.

Souza-Pereira L, Ouhbi S, Pombo N. A process model for quality in use evaluation of clinical decision support systems. J Biomed Inform. 2021 Nov;123:103917. doi: 10.1016/j.jbi.2021.103917. Epub 2021 Sep 24. PMID: 34563692.

Rahmani K, Garikipati A, Barnes G, Hoffman J, Calvert J, Mao Q, Das R. Early prediction of central line associated bloodstream infection using machine learning. Am J Infect Control. 2022 Apr;50(4):440-445. doi: 10.1016/j.ajic.2021.08.017. Epub 2021 Aug 21. PMID: 34428529.

Abujaber A, Fadlalla A, Gammoh D, Al-Thani H, El-Menyar A. Machine Learning Model to Predict Ventilator Associated Pneumonia in patients with Traumatic Brain Injury: The C.5 Decision Tree Approach. Brain Inj. 2021 Jul 29;35(9):1095-1102. doi: 10.1080/02699052.2021.1959060. Epub 2021 Aug 6. PMID: 34357830.

Liang Y, Zhu C, Tian C, Lin Q, Li Z, Li Z, Ni D, Ma X. Early prediction of ventilator-associated pneumonia in critical care patients: a machine learning model. BMC Pulm Med. 2022 Jun 25;22(1):250. doi: 10.1186/s12890-022-02031-w. PMID: 35752818; PMCID: PMC9233772.

Du H, Siah KTH, Ru-Yan VZ, Teh R, En Tan CY, Yeung W, Scaduto C, Bolongaita S, Cruz MTK, Liu M, Lin X, Tan YY, Feng M. Prediction of in-hospital mortality of Clostriodiodes difficile infection using critical care database: a big data-driven, machine learning approach. BMJ Open Gastroenterol. 2021 Nov;8(1):e000761. doi: 10.1136/bmjgast-2021-000761. PMID: 34789472; PMCID: PMC8601086.

Møller JK, Sørensen M, Hardahl C. Prediction of risk of acquiring urinary tract infection during hospital stay based on machine-learning: A retrospective cohort study. PLoS One. 2021 Mar 31;16(3):e0248636. doi: 10.1371/journal.pone.0248636. PMID: 33788888; PMCID: PMC8011767.

Zachariah P, Sanabria E, Liu J, Cohen B, Yao D, Larson E. Novel Strategies for Predicting Health care-Associated Infections at Admission: Implications for Nursing Care. Nurs Res. 2020 Sep/Oct;69(5):399-403. doi: 10.1097/NNR.0000000000000449. PMID: 32604154.

Frondelius T, Atkova I, Miettunen J, Rello J, Jansson MM. Diagnostic and prognostic prediction models in ventilator-associated pneumonia: Systematic review and meta-analysis of prediction modelling studies. J Crit Care. 2022 Feb;67:44-56. doi: 10.1016/j.jcrc.2021.10.001. Epub 2021 Oct 18. PMID: 34673331.

Articles

Programs seeking to implement advances in health care epidemiology must critically evaluate their impact on infection prevention processes, patient safety, and cost prior to wholesale adoption. 

Rachel Pryor, RN, MPH

Articles

Latest Advancements in Infection Prevention Technology