Introduction
Central line-associated bloodstream infections (CLABSI) is a considerable bother for the US and the world. According to the Centers for Disease Control and Prevention (CDC, 2018), the condition is associated with thousands of deaths and billions of dollars for the US healthcare system. Considering the high mortality rates from the disease, it is crucial to create effective and cost-efficient prevention strategies. A literature review revealed that the use of automated surveillance systems for CLABSI might be associated with improved patient outcomes due to early detection of the problem. The purpose of the present paper is to assess the current body of evidence concerning the use of automated surveillance systems for preventing CLABSI and to offer a path for the evidence to be translated into practice.
Manual surveillance of patient data for the possible cases of hospital-acquired infections (HAIs) is a time-consuming procedure for detecting unreported CLABSIs (Noaman, Ragab, Al-Abdullah, Jamjoom, A., Nadeem, & Ali, 2018). The identified problem can be approached by the introduction of automated surveillance systems to scan hospital databases for at-risk patients who have CLABSI (Noaman et al., 2018; Emmanuel, & Torres, 2018).
However, before designing an automated surveillance system, it is vital to assess the current body of evidence to identify the best practices, which is the central idea of evidence-based practice (EBP). According to Black, Balneaves, Garossino, Puyat, and Qian (2015), patient outcomes are improved when front-line personnel performs their duties in an evidence-based manner. Evidence is often translated to practice by Doctors of Nursing Practice (DNPs), whose central role is to apply relevant, up-to-date research to introduce innovations (Trautman, Idzik, Hammersla, & Rosseter, 2018). Therefore, DNP-prepared nurses can address the identified problem with the highest degree of efficiency and proficiency.
Practice Problem and Question
CLABSIs are associated with increased morbidity and mortality, together with additional financial burden and prolonged hospital stay. According to Emmanuel and Torres (2018), on average, patients with CLABSI stay in the hospital for 19 additional days, and the associated cost of the condition is $55,646 per case. Considering the fact that in the US, there are around 30,000 CLABSI events annually and the mortality rate of 12%-25%, the modernization of current prevention strategies is vital for decreasing the cost of care (CDC, 2018).
CDC (2018) states that more than 600 lives and $40 million can be saved annually using evidence-based prevention guidelines, among which CLABSI surveillance is one of the critical points. However, according to Noaman et al. (2018), manual monitoring is an inefficient practice, which is associated with increased workload and a high degree of error. Therefore, the utilization of automated methods is a viable strategy to improve the effectiveness of surveillance practices by reducing the required time, use of financial and human resources, and the probability of error. The present paper aims to assess the current body of evidence to answer the following question.
PICOT Question: In hospital settings (P), does the introduction of an automated surveillance system (I) in comparison with manual surveillance (C) reduce morbidity and mortality associated with CLABSI (O) in six months (T).
Evidence Synthesis of Literature
The evidence synthesis aimed at summarizing the findings of recent research concerning the utilization of automated surveillance systems in hospital settings. The review included four research articles published between 2016 and 2018 that utilized different research methodologies to address the identified issue. The research findings and designs are summarized in the evidence table provided in Appendix A. Even though the quality of the research differs, it was helpful to answer the PICOT question.
Emmanuel and Torres (2018) utilized retrospective research design to review the charts alerts triggered for CLABSIs, catheter-associated urinary tract infections (CAUTIs), neonatal sepsis, or clinical deterioration. The results revealed that CLABSI alerts led to more interventions than all other types of assessed signals combined. This implies that the algorithms utilized for the detection of possible cases of CLABSIs are relatively precise, which allows its use in hospital settings. Moreover, the findings suggest that device-associated infections, including CLABSIs and CAUTIs, should continue to be monitored as 12% of the alerts lead to an intervention.
Noaman et al. (2018) compared the automated surveillance system and manual surveillance of health records in terms of efficiency and effectiveness. The researchers utilized an experimental approach to examine the standard infection rate (SIR) before and after the introduction of an automated surveillance system. The SIRs were fixed and analyzed 12 months after the intervention. The results revealed an 87% improvement in the predictive ability of the surveillance for CLABSIs. This implies that automated surveillance systems can be utilized in hospital settings to decrease the cost of care and reduce morbidity and mortality associated with CLABSIs.
Ridgway, Sun, Tabak, Johannes, and Robicsek (2016) conducted a study to evaluate the effectiveness of an automated surveillance system, the blood Nosocomial Infection Marker (NIM), in terms of patient outcomes and economic impact. The research concluded that the blood NIM had 73.2% positive predictive value 99.9% negative predictive value. Moreover, the median charge for patients with CLABSI was $143,935, while the average cost of care for non-cases was $115,267, while the associated mortality was 17.5% versus 9.4%. The findings imply that the utilization of NIM for the prediction of CLABSI is an effective strategy to decrease morbidity and mortality and reduce the financial implications of care.
Sips, Bonten, and van Mourik (2017) provide a narrative review of the literature concerning the surveillance practices of HAIs and surgical site infections (SSIs). The researchers claim that automated detection systems can effectively predict device-associated infections, such as CLABSIs, due to the characteristics of data concerning the conditions. The study concludes that data standardization is an effective strategy to improve the predictive abilities of automated surveillance systems.
Several conclusions can be made from the syntheses of the reviewed evidence. First, automated surveillance systems have a high predictive ability of HAIs (Emmanuel & Torres, 2018; Noaman et al., 2018; Ridgway et al., 2016; Sips et al., 2017). Second, the cases of CLABSIs are the easiest to predict among all other HAIs and SSIs (Emmanuel & Torres, 2018; Sips et al., 2017). Third, automated surveillance systems are more efficient than manual ones (Emmanuel & Torres, 2018; Noaman et al., 2018; Ridgway et al., 2016; Sips et al., 2017). Finally, evidence tells that the utilization of automated surveillance systems is associated with improved patient outcomes (Emmanuel & Torres, 2018; Noaman et al., 2018; Ridgway et al., 2016; Sips et al., 2017) and decreased cost of care (Emmanuel & Torres, 2018; Sips et al., 2017). In short, all the reviewed literature positively answer the stated PICOT question.
Appraisal of Evidence
The reviewed evidence is of varying levels and quality. The study by Noaman et al. (2018) is a retrospective cohort study of medium quality. The primary issue with the research is the insufficient sample size, as the research was conducted using five months of records of a 100-bed hospital. The database had only 244 alerts, which is not enough to make generalized statements. The study is also considered third-level evidence due to the utilized research methodology. At the same time, another retrospective study by Ridgway et al. (2016), while belonging to the same level of evidence, has a higher generalization ability since the sample size is more than 37,000 patients. The increased sample size and reliability of findings imply that the research is of high quality.
While Sips et al. (2017) provide holistic information concerning automated surveillance systems with references to reliable, up-to-date sources, the level of evidence is the lowest due to the study design. Sips et al. (2017) chose a narrative review as the primary method, which means that the study belongs to the fifth level of evidence. However, it should be viewed as medium-quality research instead of low-quality due to other characteristics of the study, such as the sample size and the quality of references.
The most reliable evidence overviewed in the present paper is the study conducted by Noaman et al. (2018). The researchers conducted an experiment by introducing the automated surveillance system and comparing predictions of CLABSIs before and after the adoption of the software. The research design implies that it is first-level evidence, and the period of study, together with settings, allow ranking it as high-quality research. Therefore, the findings of the study, which tell that the introduction of an automated surveillance system leads to 87% improvement of predictive ability of CLABSIs, should be considered valid and reliable.
Translation Path
The translation of the reviewed evidence to practice may be facilitated by various favorable and unfavorable factors. First, the external factors, such as the availability of ready-made automated surveillance systems and successful practices concerning the switch from manual to automated surveillance, should be considered. Second, internal factors, such as motivation, administrative support, availability of funds, and human resources, as well as personnel resistance to change, are to be taken into account (Thomas, 2018). Finally, personal skills of the nurse practitioner as the change agent, including research skills, willingness to change, and the ability to advocate an idea, have a considerable impact on the success of the research (Thomas, 2018). Therefore, the proposed evidence-based change is to be carefully evaluated and planned.
A successful EBP change should be guided by a relevant theoretical framework. According to Batras, Duff, and Smith (2016), an adequate model is beneficial for planning the change and evaluating its results. The most appropriate approach to the PICOT question Rogers’ diffusion of innovation (DOI) theory (Lien & Jiang, 2017). According to DOI, there are four crucial steps to effective adoption of new practices: awareness, decision to adopt or reject, initial use, and continuous use (Lien & Jiang, 2017). This implies that the proposed change will have four stages to adhere to the utilized theory.
The framework also suggests that evaluation is performed after the third stage of change. For the current project, the assessment will be conducted after six months, and the changes in the predictive ability of surveillance systems will be measured using the guideline described by Noaman et al. (2018). The expected outcomes are a 20% reduction in morbidity, mortality, and cost of care due to the reduced number of cases.
After the initial use is assessed, a long-term sustainability plan will be created and implemented. First, personnel will be informed about the success of the project to improve their motivation. Second, financial implications will be calculated to assure administrative support. Finally, the research about automated surveillance systems will be continued to ensure the use of the most recent and efficient practices.
Conclusion
The utilization of adequate surveillance systems is vital for reducing the consequences of CLABSI. The reviewed evidence suggests that a transition from a manual surveillance system to an automated one is associated with the improved performance of surveillance and decreased morbidity, mortality, and cost of care associated with CLABSI. The appraised evidence will be helpful for planning and evaluating an EBP change for addressing the identified practice problem. A DNP-prepared nurse will lead the change, as its primary role is to assess evidence and translate it to innovative practice with the highest degree of proficiency.
References
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Sips, M. E., Bonten, M. J., & van Mourik, M. S. (2017). Automated surveillance of healthcare-associated infections: State of the art. Current opinion in infectious diseases, 30(4), 425-431.
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