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IV INJECTION PORT DISINFECTION 1 Marian University Leighton School of Nursing Doctor of Nursing Practice Final Project Report Anesthesia Provider Compliance with Intravenous Injection Port Disinfection Protocols Brittany Brechbuhl Marian University Leighton School of Nursing Chair: Dr. Bradley Stelflug________ ________________________ (Signature) Committee Member: Dr. Stanley Weber__________ _________________________ (Signature) Date of Submission: October 19, 2020_________ IV INJECTION PORT DISINFECTION 2 Table of Contents Abstract ................................................................................................................................4 Introduction .........................................................................................................................6 Background ....................................................................................................................6 Problem Statement .........................................................................................................8 Organizational Gap Analysis of Project Site..............................................................9 Review of the Literature ....................................................................................................10 Theoretical Framework/Evidence Based Practice Model/Conceptual Model...................17 Goals/Objectives/Expected Outcomes ..............................................................................19 Project Design/Methods 20 Project Site and Population ..........................................................................................20 Measurement Instrument(s) ........................................................................................23 Data Collection Procedures .........................................................................................23 Ethical Considerations/Protection of Human Subjects ...24 Data Analysis and Results..24 Limitations .......26 Conclusion ........................................................................................................................27 References ..........................................................................................................................28 Appendix A ........................................................................................................................35 Appendix B ........................................................................................................................37 Appendix C ........................................................................................................................38 Appendix D ........................................................................................................................39 IV INJECTION PORT DISINFECTION Appendix E .......................................................................................................................40 Appendix F ........................................................................................................................42 Appendix G .......................................................................................................................41 3 IV INJECTION PORT DISINFECTION 4 Abstract Background and Review of Literature: Intravenous (IV) catheter-associated bloodstream infections (CABIs) are a substantial problem in healthcare. Recently, intraoperative care was identified as a risk factor for the development of CABIs. Patient IV stopcocks (i.e. injection ports), which are bacterially contaminated during anesthesia administration, serve as a vector for bacterial transmission and subsequent infection development in patients. IV injection port disinfection was shown to reduce this bacterial contamination and decrease postoperative infection rates. Current clinical practice guidelines (CPGs) and hospital policies recommend IV injection port disinfection prior to IV access. Purpose: The purpose of this DNP project is to determine anesthesia provider compliance with intraoperative IV injection port disinfection protocols and anesthesia provider attitudes regarding intraoperative IV injection port disinfection. Methods: To investigate this, an online survey will be administered using the survey software program Qualtrics. Implementation Plan/Procedure: Anesthesia providers at IU Health Arnett Hospital will be invited to participate via e-mail. Participation will be entirely voluntary and confidential. All survey responses will be sent to Qualtrics and stored in a password protected electronic format. Implications/Conclusion: The survey completion rate among anesthesia providers was 41.38%. Survey respondents estimated scrubbing the IV injection port 68.83% of the time and allowing drying time after scrubbing the IV injection port 51.33% of the time prior to IV-line access. Only 49.99% of survey respondents agreed that intraoperative IV-line care contributes to bacterial transmission to patients. Furthermore, 58.33% of survey respondents disagreed that intraoperative IV-line care contributes to the development of postoperative infections. These IV INJECTION PORT DISINFECTION results suggest that improvement is needed in this area of intraoperative infection control. They also suggest that certain anesthesia provider attitudes regarding intraoperative IV-line care may serve as a rationale for noncompliance. Future research and quality improvement (QI) initiatives should focus on such attitudes as a potential avenue for intervention for improving IV injection port disinfection compliance among anesthesia providers. Keywords: catheter hub disinfection, anesthesia workplace, stopcock contaminatio n, intraoperative bacterial transmission, intraoperative infection control 5 IV INJECTION PORT DISINFECTION 6 Anesthesia Provider Compliance with Intravenous Injection Port Disinfection Protocols Introduction This project is submitted to the faculty of Marian University Leighton School of Nursing as partial fulfillment of degree requirements for the Doctor of Nursing Practice (DNP), Nurse Anesthesia track. Disinfection of intravenous (IV) injection ports (a process referred to in nursing as scrubbing the hub) is a simple evidence-based intervention that has been established in clinical practice guidelines (CPGs) and hospital infection control policies as part of an insertion and maintenance care bundle to prevent IV catheter-associated bloodstream infections (CABIs) (Ista et al., 2016). However, unfortunately, IV CABIs still remain a significant source of morbidity and mortality among hospitalized patients (Ista et al., 2016). According to OGrady et al. (2011), approximately 250,000 bloodstream infections (BSIs) occur annually in the United States (US), resulting in preventable patient morbidity and mortality and costing the national healthcare system billions of dollars. The purpose of this DNP project is to determine anesthesia provider compliance with intraoperative IV injection port disinfection protocols. In addition to compliance, this DNP project aims to evaluate anesthesia provider attitudes regarding intraoperative IV injection port disinfection. For the purposes of this project, the term injection port is used interchangeably with needleless connector (NC), catheter hub, stopcock, or manifold, and refers to any IV-line access point through which medications can be administered intravenously. Background For the last several years, an increasing amount of evidence has linked intraoperative care with infection transmission. In a study conducted by Yogaraj, Elward, and Fraser (2002), transportation to the operating room (OR) was identified as an independent risk factor for the IV INJECTION PORT DISINFECTION 7 development of CABI. More recently, anesthesia care has been implicated as an independent risk factors for CABIs, suggesting that intraoperative IV-line care is instrumental in infection prevention (Martin, Kallile, Kanmanthreddy, & Zerr, 2017). Current literature suggests that anesthesia providers play a critical role in intraoperative bacterial transmission to patients throughout the course of an anesthetic (Loftus et al., 2008; Martin et al., 2017). Bacterially contaminated IV stopcocks have repeatedly been recognized as common culprits for bacterial transmission and subsequent infection development (Loftus et al., 2012c; Moureau & Flynn, 2015). In one study, 32% of stopcocks on patients IV tubing were found to have been contaminated with bacteria during the administration of an anesthetic (Loftus et al., 2012a). Culpatory sources for IV stopcock transmission come from contaminated anesthesia providers hands and a contaminated anesthesia workspace (Loftus et al., 2012b). Recently, IV stopcock contamination was found to be associated with an increase in postoperative infections and patient morbidity and mortality (Hopf, 2015). Current literature indicates that poor intraoperative IV-line care among anesthesia providers may play a contributory role in IV stopcock bacterial contamination and subsequent infection transmission (Munoz-Price et al., 2013; Martin et al., 2017). However, catheter hub/IV stopcock disinfection has been shown to reduce this contamination and attenuate the inadvertent injection of bacterial organisms during medication administration (Salzman, Isenberg, & Rubin, 1993; Moureau & Flynn, 2015). In fact, in a clinical systematic literature review conducted by Moureau and Flynn (2015), IV catheter hub disinfection was found to be the most important intervention in preventing IV catheter hub bacterial contamination and subsequent injection. Moreover, in a study conducted by Loftus et. al (2012a), reducing IV stopcock bacterial contamination was found to correlate with a reduction in postoperative infections. IV INJECTION PORT DISINFECTION Compliance with IV injection port disinfection individually has not been extensively studied; however, it is considered to be universally poor (as low as 10% in some studies) (Moureau & Flynn, 2015). As anesthesia providers frequently handle IV lines during the administration of general anesthesia (GA), it is important to determine their compliance with established infection control policies currently in place. With the recent outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus responsible for causing COVID19, stringent intraoperative infection control is arguably now even more critical (WHO, n.d.). Determining anesthesia provider compliance with IV injection port disinfection (i.e. scrubbing the hub) is necessary to further understand the role anesthesia providers play in infection transmission and prevention. In addition, it is essential prior to developing and implementing successive quality improvement (QI) initiatives to improve compliance (and overall intraoperative infection control practices). Moreover, examining providers attitudes regarding IV injection port disinfection will help determine which QI initiatives will be most constructive moving forward. Problem Statement To investigate the aforementioned clinical problem, the following PICO question was developed: Among anesthesia providers [at IU Health Arnett Hospital], what is the current compliance with IV injection port disinfection compared to that required by the facilitys policy? In order to properly assess this, IU Health Arnetts Hospital current Anesthesia Department IVline disinfection practices will be assessed using a program evaluation QI project approach. 8 IV INJECTION PORT DISINFECTION 9 Organizational Gap Analysis of Project Site A gap analysis is a tool often used in healthcare to define current healthcare issues (University of Toronto, n.d.). According to UCLA Health (2016), it is the method of identifying the difference between current knowledge, skills and/or practices and the desired best practice (p. 1). Clarifying this gap justifies the need for educational intervention and guides implementation of the appropriate teaching and evaluation methods (UCLA Health, 2016; University of Toronto, n.d.). The steps to conducting a gap analysis can be found in Figure 1 of Appendix A. The first step (i.e. Current State) involves identifying what is currently happening within a realm of healthcare (UCLA Health, 2016). This purpose of this DNP project is to identify the current state of compliance with IV injection port disinfection among anesthesia providers at the project site (IU Health Arnett Hospital). The second step involves defining what the best practice (i.e. Desired State) is in that realm of healthcare (UCLA Health, 2016). In general, best practice is determined by CPGs, which are specific practice recommendations grouped together, which have been derived from a methodologically rigorous review of the best evidence on a specific topic (Melnyk & Fineout-Overholt, 2019, p. 19). Best practice regarding anesthesia-related IV-line care at the project site is determined by CPGs published by the CDC and the American Association of Nurse Anesthetists (AANA), and the project sites infection control policies (CDC, 2011; AANA, 2015). For individual healthcare facilities, best practice is defined by a facilitys policies (although often times, facility policies are based on published CPGs). For this DNP project, best practice regarding anesthesia-related IV-line care will be determined by IU Health Arnett Hospitals relevant hospital policies. There is no current policy specific to intraoperative IV-line care at IU Health Arnett Hospital. However, there is a policy concerning IV therapy IV INJECTION PORT DISINFECTION 10 administration that applies to all staff responsible for IV therapy administrations within IU Health Arnett Hospital, which includes anesthesia staff during intraoperative care (IU Health Arnett Hospital, 2008, para 2). The policy states that anytime [an] IV is accessed, the needleless connector must be cleansed with alcohol for a minimum of 5 seconds and allowed to dry (IU Health Arnett Hospital, 2008, para 20). Per Sarah Norkus, the manager of the OR and ancillary perioperative services, this policy extends to stopcocks (and any other IV add-on device), which are routinely used in patient IV tubing in the OR (S. Norkus, personal communication, April 18, 2020). Current practice regarding anesthesia provider compliance with IV injection port disinfection has not yet been identified at IU Health Arnett Hospital, which demonstrates why this DNP project is appropriate for this site. Determining current practice is the first step in facilitating an appropriate continuing health education (CHE) endeavor to close the gap between current practice and best practice (UCLA Health, 2016). The remaining steps of the gap analysis (see Figure 1 of Appendix A) can be addressed through successive DNP projects and/or QI initiatives. Review of the Literature A review of the literature was conducted using several electronic search engines, including MEDLINE-Ebsco, MEDLINE-Ovid, CINAHL, and PubMed. Relevant search terms included central line-associated bloodstream infection, catheter-associated infection, catheter hub disinfection, scrub the hub, needless connector, stopcock contamination, anesthesia provider, anesthesia workspace, and intraoperative bacterial transmission. Several different combinations of these search terms ultimately yielded 48 relevant articles. Inclusion criteria included studies published within the last 10 years (unless they were IV INJECTION PORT DISINFECTION 11 considered to be landmark), and those pertaining directly to IV injection port disinfection and intraoperative (i.e. anesthesia) infection control. Exclusion criteria included articles older than 10 years (unless they were considered to be landmark). Ultimately, eight relevant articles were used, which included two systematic reviews (one with meta-analysis), one randomized, single-blinded control trial (RCT), one randomized, single-blinded controlled ex vivo study, three prospective randomized observational studies (one with retrospective analysis), and one prospective cohort study. In addition to these articles, CPGs published by the CDC and AANA, and IU Health Arnett Hospitals policies (all of which pertained to IV-line care) were used. Several themes emerged from the review of literature. To begin, healthcare-associated infections (HCAIs), and CABIs specifically, remain a significant source of morbidity and mortality in patients around the globe and cost healthcare systems billions of dollars (OGrady et al., 2011, Ista et al., 2016). In the United States alone, 250,000 CABIs are estimated to occur annually; however, only 80,000 of those occur in intensive care units (ICUs) (OGrady et al., 2011). In a landmark study conducted by Yogaraj et al. (2002), the OR was determined to be an independent risk factor for the development of CABIs. The authors concluded that this risk was attributed to processes of care, rather than existing patient acuity and underlying illness (Yogaraj et al., 2002). Moreover, anesthesia providers were found to significantly contribute to the transmission of bacteria to patients intraoperatively (Loftus et al., 2008; Loftus et al., 2012b; Hopf, 2015). The primary modes of bacterial transmission came from anesthesia providers hands, contaminated anesthesia workspaces, and the surrounding OR environment (Loftus et al., 2008; Loftus et al., 2012b). Intraoperative infection control practices, specifically hand hygiene practices, among anesthesia providers have consistently been poor (Loftus et al., 2008: Munoz-Price et al., 2019). IV INJECTION PORT DISINFECTION 12 Most studies attribute this poor compliance to the fast-paced work environment and professional culture associated with anesthesia (Loftus et al., 2008; Munoz-Price et al., 2019). Loftus et al. (2008) concluded that variable aseptic practice of anesthesia providers was found to lead to contamination of both patient IV tubing and the anesthesia workspace. Contamination to patient IV tubing, specifically to open-lumen IV stopcocks, frequently occurred during the administration of anesthesia (Loftus et al., 2008; Loftus et al., 2012a; Loftus et al., 2012b). Contaminated IV stopcocks (i.e. injection ports) were found to be culpatory sources for bacterial transmission (Loftus et al., 2008; Moureau & Flynn, 2015). Potentially pathogenic and multidrug-resistant organisms were transmitted through inadvertent bacterial injection occurring from medication administration during routine anesthesia care (Loftus et al., 2008; Loftus et al., 2012b). Examples of such organisms included methicillin-resistant Staphylococcus aureus (MRSA) and vancomycin-resistant enterococcus (VRE) (Loftus et al., 2008). Injection port disinfection was the most important factor in preventing bacterial contamination and subsequent injection (Loftus et al., 2012c; Moureau & Flynn, 2015). Recently, multiple studies have demonstrated an associative link between intraoperative bacterial contamination and postoperative infections (Loftus et al, 2008; Loftus et al., 2012a). Moreover, the reduction of IV stopcock contamination correlated with a reduction of postoperative infections (Loftus et al., 2012a). However, the statistical significance of these links has not yet been validated, highlighting apparent gaps in the literature (Loftus et al., 2012a). The implementation of intravenous catheter care bundles is effective in reducing CABIs in intensive care units (ICUs) and hospital wide (Ista et al., 2016). The interventions incorporated in bundles differ widely; however, catheter hub disinfection was commonly included (Ista et al., 2016). Healthcare professionals compliance with bundle protocols and guidelines is IV INJECTION PORT DISINFECTION 13 suboptimum and considered to be a universal problem in healthcare (Moureau & Flynn, 2015; Ista et al., 2016). As catheter care interventions are bundled together, catheter hub (i.e. injection port) disinfection as an independent intervention and its individual effectiveness on reducing CABIs has not been extensively studied. This was a sizeable gap found within the existing relevant literature. Moreover, healthcare provider compliance with catheter hub disinfection has not exclusively been studied. Despite this, current research suggests it is universally poor as low as 10% in some studies (Moureau & Flynn, 2015). With regard to CPGs, the CDC strongly recommends scrubbing all IV access ports with an appropriate antiseptic (CDC, 2011, p. 20). This recommendation (Category IA) is strongly supported by well-designed experimental, clinical, [and/or] epidemiologic studies (CDC, 2011, p. 8). The CDC acknowledges that IV access port (i.e. injection port) disinfection is essential to the prevention of transmission of microbes (CDC, 2011). It recognizes that disinfection time may be important but does not recommend a specific disinfection time (CDC, 2011). Current research recommends disinfecting IV access ports for 5-60 seconds (prior to IV access) (Moureau & Flynn, 2015). The AANA recommends anesthesia providers scrub IV injection ports (NC, stopcocks, etc.) with an appropriate antiseptic agent and allow to dry according to manufacturer recommendation[s] (AANA, 2015, p. 22). However, it should be noted that this recommendation is specifically for the access of central venous catheters (CVCs) (AANA, 2015). Currently, the AANA does not have published recommendations addressing the access of peripheral venous catheters (AANA, 2015). It should also be noted that the AANA recommends the CDCs Guidelines for the Prevention of Intravascular Catheter-Related Infections for complete guidance on intravascular catheter care (AANA, 2015). Most importantly, the AANA IV INJECTION PORT DISINFECTION 14 recommends that anesthesia providers (and other healthcare professionals) defer to their facilitys policy on infection control standard precautions (AANA, 2015, p. 3). IU Health Arnett Hospital has several policies pertaining to IV-line care. The facilitys Anesthesia Infection Control policy states that all peripheral IVs must be disinfected with alcohol prior to access (IU Health Arnett Hospital, 2020). The policy does not indicate a disinfection time (IU Health Arnett Hospital, 2020). Another facility policy, titled Hanging and Replacement of Intravascular Fluid Administrations Sets Adults and Pediatrics states that all NCs (i.e. injection ports) must be cleansed with alcohol for a minimum of 5 seconds and allowed to dry any time the IV catheter is accessed (IU Health Arnett Hospital, 2008, para 20). It should be noted that this specific policy uses the aforementioned CDC guidelines as a reference (in additional to multiple other sources). In order to investigate anesthesia provider compliance with infection prevention protocols, a separate literature review was conducted. Literature was searched for using the electronic databases MEDLINE-Ebsco, MEDLINE-Ovid, CINAHL, and PubMed. Different combinations of the following search terms were used: survey, compliance, anesthesia provider, infection, and intraoperative infection control. This search strategy ultimately yielded 36 relevant articles. Landmark studies and those published within the last 15 years were included. Studies older than 15 years (unless they were considered to be landmark) were excluded. After applying the inclusion and exclusion criteria, eight relevant articles were used (two of which were used in the previous literature review). These included one systematic literature reviews, five prospective randomized observational studies (three of which implemented covert observation techniques), one survey, and one questionnaire. In addition, the CDCs CPGs on hand hygiene was also used. IV INJECTION PORT DISINFECTION 15 Several themes were present in this literature review. First, healthcare provider compliance with infection prevention guidelines has widely been studied and consistently been shown to be poor (CDC, 2002). The CDC (2002) estimated overall hand hygiene compliance amongst all healthcare providers to be roughly 40% (with ranges from 5-81%). Healthcare provider compliance with catheter hub disinfection specifically is less studied, pointing to an apparent gap in the literature. Of the literature available, compliance was cited to be poor (Moureau & Flynn, 2015). In a systematic review conducted by Moureau and Flynn (2015), catheter hub disinfection compliance rates were found to be as low as 10%. However, in a large observational study conducted by Jardim, Lacerda, Soares, and Nunes (2013), compliance rates among all healthcare providers were found to be below 40%. These authors also demonstrated that catheter hub disinfection was the most frequently neglected component of IV-line care bundles (Jardim et al., 2013). Anesthesia providers in particular have been known to be especially noncompliant with infection prevention protocols (Biddle & Shah, 2012; Munoz-Price et al., 2013; Sahni, Biswal, Gandhi, & Yaddanapudi, 2015). Hand hygiene compliance among anesthesia providers has been estimated at 18% (and as low as 2%) (Krediet et al., 2011; Biddle & Shah, 2012). Most studies cite several barriers that contribute to noncompliance, including a fast-paced work environment, anesthesia culture, and a high rate of patient contact (Krediet et al., 2011; Sahni et al., 2015). Interestingly, there appears to be a difference in hand hygiene compliance between anesthesiologists and nurse anesthetists (Or et al., 2009). According to a large survey study conducted by Or et al. (2009), only 52.6% of anesthesiologists reported practicing hand hygiene, compared to 70.4% of nurse anesthetists. IV INJECTION PORT DISINFECTION 16 Compliance with catheter hub disinfection amongst anesthesia providers has not been studied exclusively, highlighting another existing gap in the relevant literature. When it was investigated, it was usually done so secondarily (to hand hygiene compliance or intraoperative bacterial transmission). One observational study by Munoz-Price et al. (2013) demonstrated that anesthesia providers only performed catheter hub disinfections 15% of the time during routine anesthesia administration. Moreover, in a separate study surveying the management of peripheral arterial catheters amongst anesthesia providers, 47% of respondents reported never disinfecting catheter access ports (Reynolds et al., 2013). In summary, CABIs are a persistent problem in healthcare, and intraoperative care specifically has been implicated as a risk for developing CABIs (Yogaraj et al., 2002; Ista et al., 2016). It has been shown that anesthesia providers transmit bacteria to patients through poor intraoperative infection control practices (Loftus et al., 2008; Loftus et al., 2012b; Hopf, 2015). Bacterial transmission to patient IV tubing, specifically IV injection ports (NCs, stopcocks, etc.) was demonstrated to occur frequently during routine anesthesia care (Loftus et al., 2008; Loftus et al., 2012c). Bacterially contaminated IV injection ports facilitated the inadvertent IV injection of bacteria to patients and was associated with an increase in postoperative infections (Loftus et al., 2008). In order to reduce bacterial contamination of IV injection ports and prevent subsequent transmission of microbes, CPGs published by the CDC and AANA recommend disinfecting IV injection ports prior to IV access (CDC, 2011; AANA, 2015). Currently, there is no gold standard for the appropriate disinfection technique and time (Moureau & Flynn, 2015). However, in a systematic review conducted by Moureau and Flynn (2015), scrubbing the hub with 70% alcohol for 5-60 seconds is most commonly recommended. IU Health Arnett Hospitals policies IV INJECTION PORT DISINFECTION 17 recommend disinfecting IV injection ports with alcohol for a minimum of 5 seconds and allowing time to dry (IU Health Arnett Hospital, 2008). Compliance with evidence-based practice (EBP) recommendations, particularly amongst anesthesia providers, has been grossly understudied. When compliance has been investigated, it has been shown to be universally poor (Krediet et al., 2011; Moureau & Flynn, 2015). Given that a reduction of bacterial IV injection port contamination (through disinfection) was correlated with a reduction in postoperative infections, more emphasis needs to be placed on intraoperative IV-line care (Loftus et al., 2012a). Establishing anesthesia provider compliance with current EBP recommendations and IU Health Arnett Hospitals policies is the first step in this process. This DNP project will determine anesthesia provider compliance with intraoperative IV injection port disinfection. Secondarily, it will determine anesthesia provider attitudes regarding intraoperative IV injection port disinfection, so that appropriate QI initiatives can be subsequently developed and implemented. Evidence-Based Practice: Verification of Chosen Option There is an abundance of research that supports the use of EBP in healthcare. According to Melnyk and Fineout-Overholt (2019), evidence-based practice enhances healthcare quality, improves patient outcomes, reduces costs, and empowers clinicians (p. 7). Based on a review of the literature pertaining to intraoperative IV-line care, a program evaluation QI approach will be implemented for this DNP project. Evidence-Based Practice Model The EBP model that underpins this DNP project is Stetlers Model of Research Utilization. Originally developed by Cheryl Stetler in 1994, and later revised in 2001, the model was one of the first used for EBP in nursing (White, Dudley-Brown, & Terhaar, 2016). The IV INJECTION PORT DISINFECTION 18 model uses a series of critical-thinking and decision-making steps that are designed to facilitate the effective use of research findings (Stetler, 2001, as cited in White et al., 2016, p. 8). At large, the model is used to guide the implementation of research evidence into practice (to ultimately affect change). It accomplishes this by focusing on the individual practitioner (rather than the organization) and promoting the use of internal data (from quality improvement projects, practitioner experience data, etc.) and external evidence (from primary research) (White et al., 2016). Stetlers Model of Research Utilization (see Figure 2 of Appendix A) employs five sequential phases of research utilization: preparation, validation, comparative evaluation/decision- making, translation/application, and evaluation (White et al., 2016). During the preparation phase, research to be considered for practice implementation is systematically searched for and selected (White et al., 2016). In the following phase, validation, the chosen research is critically appraised using a predetermined and precise methodology (White et al., 2016). The third phase (comparative evaluation/decision- making) involves deciding whether a practice change is achievable (White et al., 2016). According to White et. Al (2016), this decision is made using four criteria: (a) the substantiating evidence, (b) the fit for implementing the research findings in the setting, (c) the feasibility of implementation, and (d) the evaluation of current practice (p. 8). After a decision is made, the logistics of research-to-practice implementation are considered and the research is subsequently translated/applied implemented into practice (White et al., 2016). The last phase, evaluation, involves evaluating the implementation using various types and levels of evaluation measures (White et al., 2016). As mentioned previously, this model is used to guide the process of effectively integrating critically appraised research evidence into practice. Ultimately, this will improve the overall quality of IV INJECTION PORT DISINFECTION 19 healthcare (White et al., 2016). This DNP project incorporates the first two phases and portions of the third phase of Stetlers Model of Research Utilization. Preparation, the first phase, facilitates the search of applicable literature pertaining to the intraoperative IV injection port disinfection. Validation, the second phase, guides the appraisal and review of the selected literature. Two of the four criteria used for comparative evaluation/decision-making, the third phase, represent the program evaluation QI design. With this approach, the current practice is evaluated, and evidence is substantiated. As the program evaluation QI design does not involve an intervention, the remainder of the third phase and the final two phases of the model cannot be employed with this DNP project. However, they can be used to guide future DNP projects that expand upon this one. Goals, Objectives and Expected Outcomes The main objective of this project is to evaluate the compliance rates of IV injection port disinfection protocols among anesthesia providers at IU Health Arnett Hospital. This will be accomplished using a survey administered through Qualtrics, a well-known online survey software program (Qualtrics, n.d.). The secondary objective of this project is to assess the anesthesia providers attitudes regarding IV injection port disinfection. This will also be accomplished through the Qualtrics survey. The main goals of this DNP project are to evaluate 20 anesthesia provider surveys during Summer 2020 and present the findings to fellow DNP students and IU Health Arnett Hospital anesthesia staff in two separate PowerPoint presentations during August 2020. Although not extensively studied, compliance with IV injection port disinfection among all healthcare providers is universally poor (Moureau & Flynn, 2015). Compliance with IV injection port disinfection among anesthesia providers specifically has not been exclusively examined. IV INJECTION PORT DISINFECTION 20 However, anesthesia provider compliance with other intraoperative infection control protocols, particularly hand hygiene, has historically been low (Hopf, 2015). Given this, anesthesia provider compliance rates for IV injection port disinfection are expected to be low. Despite this, evaluating current compliance rates, whatever they may be, is necessary prior to facilitating QI initiatives. Project Design/Methods This DNP project will implement a program evaluation QI design. However, evaluation will come from self-assessment rather than objective observation (which is frequently used to conduct patient care compliance audits in hospitals today). Using a self-assessment survey administered through Qualtrics, practice behaviors and attitudes regarding IV injection port disinfection practices will be evaluated among anesthesia providers at IU Health Arnett Hospital. All actively practicing anesthesia providers at IU Health Arnett Hospital will be invited to participate via an e-mail composed by the DNP projects principal investigator (PI) (see Appendix C), and forwarded to them by Adrienne Merrick, Lead CRNA at IU Health Arnett Hospital (A. Merrick, personal communication, May 20, 2020). Participation will be entirely voluntary and anonymous. The survey will remain open for a two-week period, during which a reminder e-mail will be sent to all potential participants by Mrs. Merrick (A. Merrick, personal communication, May 20, 2020). All data will be collected and stored through Qualtrics. After the two-week period, the survey will close, and all collected data will then be summarized, analyzed, and presented under Data Analysis and Results. Project Site and Population The DNP project will take place at Indiana University (IU) Health Arnett Hospital. Part of the greater IU Health system, IU Health Arnett Hospital is a level III trauma center located in IV INJECTION PORT DISINFECTION 21 Lafayette, Indiana (Indiana State Department of Health, n.d.; Indiana University Health, n.d.). The hospital houses 191 inpatient beds and offers a full set of healthcare services ranging from pediatrics to orthopedics (Indiana University Health, n.d.). In addition to the main hospital, an ambulatory surgery center (ASC) and medical office building are located on the premises (IU Health, 2016). There are six technologically advanced ORs within the main hospital that can accommodate complex surgeries, such as open hearts, craniotomies, and DaVinci procedures (IU Health, 2016). There are six ORs within the ASC (although typically only four are in use) (S. Bormann, personal communication, November 29, 2019). In 2016, IU Health Arnett Hospital achieved Magnet status, which recognizes excellence in nursing and patient care (IU Health, 2016). Although IU Health Arnett Hospital primarily serves the surrounding rural communities, it services a diverse patient population (Indiana University Health, n.d.). Key stakeholders in the project include Dr. Stanley Weber, Medical Director of Anesthesia, and Adrienne Merrick, Lead CRNA. Dr. Weber will assure that the proper bureaucratic and legal channels are taken regarding this project, particularly when project findings are disseminated and future publication is discussed (Dr. S. Webber, personal communication, September 23, 2019). The participants of this DNP project will be consenting anesthesia providers at IU Health Arnett Hospital, which include both anesthesiologists and nurse anesthetists. Participants will be recruited through an e-mail invitation composed by the projects PI (see Appendix C). Currently, there are 11 anesthesiologists and 18 nurse anesthetists actively practicing at IU Health Arnett Hospital, including Dr. Weber and Mrs. Merrick (S. Bormann, personal communication, April 15, 2020). This group of anesthesia providers comprise both men and women of varying ages and levels of experiences (two months to 32 years) (S. Bormann, IV INJECTION PORT DISINFECTION 22 personal communication, April 15, 2020). IU Health Arnett Hospital employs a medically directed anesthesia care team (ACT) model (S. Bormann, personal communication, April 15, 2020). Anesthesiologists typically oversee 2-3 ORs simultaneously; however, they can oversee up to four (S. Bormann, personal communication, April 15, 2020). They are required to be present in the OR during critical times (i.e. induction and emergence of GA), and must remain available throughout the entirety of each case. Outside of this, CRNAs deliver anesthesia autonomously (but in close conjunction with the overseeing anesthesiologist). Both anesthesiologists and CRNAs are staffed in shifts, and OR assignments are based on patient volume, acuity, and throughput, available OR staff, and various other factors. There are several factors that will facilitate the implementation of this DNP project. There is considerable hospital interest in infection control and prevention, which is expected to increase as a result of the coronavirus pandemic currently unfolding (WHO, n.d.). In addition, there is engaged endorsement of the project from IU Health Arnett Hospital Anesthesia Department management (i.e. Dr. Weber and Mrs. Merrick). Despite these facilitators, there are certain constraints to this project. The main barrier to DNP project implementation will be a lack of willingness to participate by anesthesia providers. This will be addressed by creating a survey will be user-friendly, concise, and easily accessible (i.e. available via computer or mobile device), all of which have been shown to increase survey participation (Qualtrics, 2019). Additionally, all participants will be assured that no personally identifiable information will be collected, and any future publication will protect the identity of the healthcare facility (and subsequently, the anesthesia providers). IV INJECTION PORT DISINFECTION 23 Measurement Instruments In order to measure the outcomes of this DNP project, an online self-assessment survey was designed using Qualtrics. The survey has two categories of questions Practice Behaviors and Attitudes. The Practice Behaviors questions were designed using IU Health Arnett Hospitals policies pertaining to IV lines and the CDCs Audit Tool for Hemodialysis Injectable Medication Administration (see Figure 3 of Appendix A). This aforementioned audit tool is endorsed by CDC and currently used in hemodialysis (HD) catheter care compliance audits (CDC, n.d.). The Attitudes questions were developed from the themes identified from an extensive literature review. The survey used in this DNP project can be found in Appendix B. As mentioned previously, for the purposes of this DNP project, injection port refers to a NC, catheter hub, stopcock, manifold, or any other IV-line access point through which medications can be administered intravenously. Data Collection Procedures All potential participants will be invited to participate via an e-mail composed by the DNP projects PI (see Appendix C) and forwarded by Adrienne Merrick, Lead CRNA at IU Health Arnett Hospital (A. Merrick, personal communication, May 20, 2020). The e-mail will contain an anonymous link for an online self-assessment survey administered through Qualtrics. As mentioned previously, Qualtrics is a well-known online survey software program frequently used by companies and universities for research purposes (Qualtrics, n.d.). Participation will be entirely voluntary and confidential. For those anesthesia providers who choose to participate, upon survey completion, all survey responses will be sent to Qualtrics and stored in a password protected electronic format. In order to protect the professional reputations of the anesthesia IV INJECTION PORT DISINFECTION 24 providers and the Anesthesia Department at IU Health Arnett Hospital, no personally identifiable data will be collected, and all survey responses will remain anonymous. Ethical Considerations/Protection of Human Subjects Marian University Internal Review Board (IRB) approval was obtained prior to initiating this DNP project. Initially, the project was deemed non-exempt by the IRB and subsequently underwent an expedited review. The official IRB Determination Form was submitted as soon as the proposal was approved (see Appendix D). An amendment to the projects methodology was later submitted and approved by the IRB, which changed its review status to exempt (see Appendix E). An informed consent process was disclosed via e-mail (see Appendix C). No patient data will be collected, so the Health Insurance Portability and Accountability Act of 1996 (HIPPA) is not applicable to this project. No personally identifiable data will be collected from the anesthesia provider participants. In addition, all of the data collected in connection with this project will only be disclosed with permission from IU Health Arnett Hospital. There are no ethical concerns or risks associated with this project. Data Analysis and Results There are 29 anesthesia providers at IU Health Arnett Hospital who were eligible to participate in this DNP project. Of these 29 anesthesia providers, 12 completed the selfassessment survey, which translated to a survey completion rate of 41.38%. Demographic data was not collected from survey participants. However, the eligible participant sample size (i.e. anesthesia providers at IU Health Arnett Hospital) was diverse, including men and women anesthesiologists and nurse anesthetists of varying ages and levels of experiences (S. Bormann, personal communication, April 14, 2020). IV INJECTION PORT DISINFECTION 25 Data analysis was conveniently and automatically performed using Qualtrics. For the Practice Behaviors portion of the survey (see Appendix B), respondents estimated performing hand hygiene 67.00% of the time, on average, prior to IV-line access. Survey respondents estimated donning clean gloves 61.00% of the time, on average, prior to IV-line access. Survey respondents estimated scrubbing the IV injection port for 5 seconds with an appropriate disinfectant 68.83% of the time, on average, prior to IV-line access. Lastly, survey respondents estimated allowing drying time after scrubbing the IV injection port 51.33% of the time, on average, prior to IV-line access. These averages (i.e. means), along with their corresponding standard deviations and variances, can be found in Appendix F. Although IV injection port disinfection has not been exclusively investigated, compliance rates with certain infection control practices among anesthesia providers (i.e. hand hygiene, sterile technique, etc.) have consistently been shown to be poor (Biddle & Shah, 2012; Munoz-Price et al., 2013; Sahni, Biswal, Gandhi, & Yaddanapudi, 2015). Studies commonly cite barriers, such as the fast-paced workflow of anesthesia care and high rate of hand hygiene opportunities, that contribute to poor compliance (Krediet et al., 2011; Sahni et al., 2015). Despite these barriers, based on the survey responses, there is room for improvement among anesthesia providers in this area of intraoperative infection control. Regarding the Attitudes portion of the survey (see Appendix B), 83.33% of respondents agreed (i.e. somewhat agree, agree, or strongly agree) that IV injection ports should be disinfected prior to IV-line access. Of all the survey respondents, 75.00% agreed (i.e. somewhat agree, agree, or strongly agree) that intraoperative IV-line care contributes to IV injection port bacterial contamination. Only 49.99% of survey respondents agreed (i.e. somewhat agree, agree, or strongly agree) that intraoperative IV-line care contributes to bacterial IV INJECTION PORT DISINFECTION 26 transmission to patients. Furthermore, only 33.33% of survey respondents agreed (i.e. somewhat agree or agree) that intraoperative IV-line care contributes to the development of postoperative infections. In fact, 58.33% of survey respondents disagreed (i.e. somewhat disagree, disagree, and strongly disagree) with the aforementioned statement. Percentages of each survey response can be found in Appendix G. These responses suggest a possible rationale for poor compliance with IV injection port disinfection (in addition to the barriers mentioned in previous studies) during intraoperative anesthesia delivery. In other words, if anesthesia providers do not believe that intraoperative IVline care contributes to post-operative infection development in patients, this perhaps explains why they may not comply with IV-line care infection control protocols. Moreover, addressing this belief through specific education may serve as an intervention to improve compliance moving forward (i.e. changing anesthesia providers attitudes about intraoperative IV-line care may assist in changing their intraoperative practice behaviors surrounding it). Although current research has not demonstrated an explicit causative relationship between intraoperative IV-line care and postoperative infections, an increasing amount of evidence suggests an associative relationship (Loftus et al, 2008; Loftus et al., 2012a). Due to this increasingly suggestive relationship, intraoperative IV-line care is arguably even more important. As such, more emphasis should be placed on IV injection port disinfection. Limitations There are several limitations to this study. To begin, the studys sample size is small (n = 12) and comes from a single institution, both of which limit its generalizability. Future research should incorporate larger sample sizes from multiple institutions to determine if these results are consistent and generalizable amongst anesthesia providers. In addition, the data collected from IV INJECTION PORT DISINFECTION 27 this study came from self-assessment (subjective) rather than direct observation (objective). The accuracy of self-assessments, particularly within the healthcare realm, has long been in question (Kruger & Dunning, 1999). A psychological phenomenon, known as the Dunning-Kruger effect, shows that underperformers tend to overestimate their performance and overperformers tend to underestimate their performance (Kruger & Dunning, 1999). Based on this effect, the selfassessment survey responses (i.e. those in the Practice Behaviors section of the survey) may be inaccurate and/or over-inflated. Future research should focus on collecting objective, observational data to compare and contrast with the data collected in this project. Conclusion In conclusion, CABIs are a persistent and substantial problem in healthcare, resulting in patient morbidity and mortality and preventable healthcare systems costs (CDC, 2011; Ista et al., 2016). Intraoperative bacterial transmission to patient IV tubing, specifically IV injection ports (NCs, stopcocks, etc.), was demonstrated to occur frequently during routine anesthesia care as a result of poor infection control practices (Loftus et al., 2008; Loftus et al., 2012c; Munoz-Price et al., 2013). This bacterial contamination contributed to inadvertent bacterial transmission and subsequent infection development in patients (Loftus et al., 2008). Current clinical practice guidelines (CPGs) and hospital policies recommend disinfecting IV injection ports prior to IV access. In order to address this aforementioned clinical problem, intraoperative anesthesia provider practices and attitudes regarding IV injection port disinfection among anesthesia providers at IU Health Arnett Hospital were examined using an online survey administered through Qualtrics. All collected data was subsequently analyzed using Qualtrics. The completion rate for the survey was 41.38%. Compliance with intraoperative IV injection port IV INJECTION PORT DISINFECTION 28 disinfection among anesthesia providers was 68.83% (mean), suggesting room for improvement. In addition, only 33.33% of survey respondents agreed that intraoperative IV-line care contributes to the development of postoperative infections in patients. This belief among anesthesia providers may provide a justification for poor compliance with intraoperative IV-line infection control protocols. Given the link between IV injection port disinfection and postoperative infections, intraoperative IV-line care should be more heavily emphasized (Loftus et al., 2012a). Future work is needed to optimize intraoperative infection control practices, and specifically IV injection port disinfection, during anesthesia care. IV INJECTION PORT DISINFECTION 29 References American Association of Nurse Anesthetists (AANA). (2015). Infection prevention and control guidelines for anesthesia care [PDF file]. Retrieved from https://www.aana.com/docs/default-source/practice-aana-com-web-documents(all)/infection-prevention-and-control-guidelines- for-anesthesia-care.pdf Biddle, C., & Shah, J. (2012). Quantification of anesthesia providers' hand hygiene in a busy metropolitan operating room: What would Semmelweis think? AJIC: American Journal of Infection Control, 40(8), 756759. https://doi.org/10.1016/j.ajic.2011.10.008 Bonnel, W., & Smith, K. (Eds.). (2018). Proposal writing for clinical nursing and DNP projects (2nd ed.). New York, NY: Springer Publishing Company. Centers for Disease Control and Prevention (CDC). (2002). Guideline for hand hygiene in health-care settings: recommendations of the Healthcare Infection Control Practices Advisory Committee and the HICPAC/SHEA/APIC/IDSA Hand Hygiene Task Force. Morbidity and Mortality Weekly Report: Recommendations and Reports, 51(Rr16), 145. Centers for Disease Control and Prevention (CDC). (2011). Guidelines for the prevention of intravascular catheter-related infections [PDF file]. Retrieved from https://www.cdc.gov/hai/pdfs/bsi-guidelines-2011.pdf Centers for Disease Control and Prevention (CDC). (n.d.). Injection safety: Medication preparation & administration audit tool [PDF file]. Retrieved from https://www.cdc.gov/dialysis/PDFs/collaborative/Hemodialysis-InjectionSafetyObservations.pdf Davidson, J. U. (2003). Example knowledgebase development template. In Rankin, S. H., IV INJECTION PORT DISINFECTION 30 Dumas, M. A., & Reavis, C. (Eds.), Grantsmanship: Developing a program of research, Appendix B (p. 77-78). Washington, DC: National Organization of Nurse Practitioner Faculties. Hopf, H. (2015). Bacterial reservoirs in the operating room. Anesthesia and Analgesia, 120(4), 700-2. https://dx.doi.org/10.1213/ANE.0000000000000247 Indiana State Department of Health. (n.d.). Trauma centers. Retrieved from https://www.in.gov/isdh/24972.htm Indiana University (IU) Health Arnett Hospital. (2008). Hanging and replacement of intravascular fluid and administration sets Adults and pediatrics. Retrieved from https://iuhealth-arnett.policystat.com/policy/7294748/latest/ Indiana University (IU) Health Arnett Hospital. (2020). Anesthesia infection control. Retrieved from https://iuhealth-arnett.policystat.com/policy/7110211/latest/ Indiana University (IU) Health. (2016). Committed to our communities: 2016 Community benefit report [PDF File]. Retrieved from https://cdn.iuhealth.org/resources/09501019_Community_Benefit_Report_WEB.PDF?mtime=20180117144229 Indiana University (IU) Health. (n.d.). IU Health Arnett physicians. Retrieved from https://iuhealth.org/for-providers/join-us/iu-health-arnett-physicians Ista, E., Van, D., Kornelisse, R., Van, D., Vos, M., Boersma, E., & Helder, O. (2016). Effectiveness of insertion and maintenance bundles to prevent central-line-associated bloodstream infections in critically ill patients of all ages: A systematic review and metaanalysis. The Lancet. Infectious Diseases, 16(6), 724-734. https://dx.doi.org/10.1016/S1473-3099(15)00409-0 Jardim, J. M., Lacerda, R. A., Soares, N. J. D., & Nunes, B. K. (2013). Evaluation of practices IV INJECTION PORT DISINFECTION 31 for the prevention and control of bloodstream infections in a government hospital. Revista Da Escola De Enfermagem, 47(1), 3845. https://doi.org/10.1590/S0080-62342013000100005 Krediet, A. C., Kalkman, C. J., Bonten, M. J., Gigengack, A. C., & Barach, P. (2011). Handhygiene practices in the operating theatre: an observational study. British Journal of Anaesthesia, 107(4), 5538. https://doi.org/10.1093/bja/aer162 Kruger, J., & Dunning, D. (1999). Unskilled and unaware of it: how difficulties in recognizing one's own incompetence lead to inflated self-assessments. Journal of Personality and Social Psychology, 77(6), 112134. Loftus, R., Koff, M., Burchman, C., Schwartzman, J., Thorum, V., Read, M., . . . Beach, M. (2008). Transmission of pathogenic bacterial organisms in the anesthesia work area. Anesthesiology, 109(3), 399-407. https://dx.doi.org/10.1097/ALN.0b013e318182c855 Loftus, R., Brindeiro, B., Kispert, D., Patel, H., Koff, M., Jensen, J., . . . Brown, J. (2012a). Reduction in intraoperative bacterial contamination of peripheral intravenous tubing through the use of a passive catheter care system. Anesthesia and Analgesia, 115(6), 1315-23. https://dx.doi.org/10.1213/ANE.0b013e31826d2aa4 Loftus, R., Brown, J., Koff, M., Reddy, S., Heard, S., Patel, H., . . . Yeager, M. (2012b). Multiple reservoirs contribute to intraoperative bacterial transmission. Anesthesia and Analgesia, 114(6), 1236-48. https://dx.doi.org/10.1213/ANE.0b013e31824970a2 Loftus, R., Patel, H., Huysman, B., Kispert, D., Koff, M., Gallagher, J., . . . Brown, J. (2012c). IV INJECTION PORT DISINFECTION 32 Prevention of intravenous bacterial injection from health care provider hands: The importance of catheter design and handling. Anesthesia and Analgesia, 115(5), 1109-19. https://dx.doi.org/10.1213/ANE.0b013e31826a1016 Martin, L., Rampersad, S., Geiduschek, J., Zerr, D., & Weiss, G. (2013). Modification of anesthesia practice reduces catheter-associated bloodstream infections: A quality improvement initiative. Paediatric Anaesthesia, 23(7), 588-96. https://dx.doi.org/10.1111/pan.12165 Martin, L., Kallile, M., Kanmanthreddy, S., & Zerr, D. (2017). Infection prevention in pediatric anesthesia practice. Pediatric Anesthesia, 27(11), 1077-1083. https://dx.doi.org/10.1111/pan.13252 Melnyk, B. M., & Fineout-Overholt, E. (2019). Evidence-based practice in nursing and healthcare: A guide to best practice (4th ed.). Philadelphia, PA: Wolters Kluwer. Moureau, N. F., & Flynn, J. (2015). Disinfection of needleless connector hubs: Clinical evidence systematic review. Nursing Research and Practice, 2015, 1-20. https://dx.doi.org/10.1155/2015/796762 Munoz-Price, L. S., Lubarsky, D., Arheart, K., Prado, G., Cleary, T., Fajardo-Aquino, Y., . . . Birnbach, D. (2013). Interactions between anesthesiologists and the environment while providing anesthesia care in the operating room. AJIC: American Journal of Infection Control, 41(10), 922-924. https://dx.doi.org/10.1016/j.ajic.2013.03.306 Munoz-Price, L. S., Bowdle, A., Johnston, B. L., Bearman, G., Camins, B. C., Dellinger, E. P., . . . Birnbach, D. J. (2019). Infection prevention in the operating room anesthesia work area. Infection Control & Hospital Epidemiology, 40(1), 117. https://dx.doi.org/10.1017/ice.2018.303 IV INJECTION PORT DISINFECTION 33 O'Grady, N., Alexander, M., Burns, L., Dellinger, E., Garland, J., Heard, S., . . . Healthcare Infection Control Practices Advisory Committee. (2011). Guidelines for the prevention of intravascular catheter-related infections. American Journal of Infection Control, 39(4), 134. https://dx.doi.org/10.1016/j.ajic.2011.01.003 Or, R. C., Hsieh, T. K., Lan, K. M., Kang, F. C., Chen, Y. H., & So, E. C. (2009). Profile of anesthetic infection control in Taiwan: A questionnaire report. Journal of Clinical Anesthesia, 21(1), 138. https://doi.org/10.1016/j.jclinane.2008.06.014 Qualtrics. (2019). The Qualtrics handbook of question design [PDF file]. Retrieved from https://success.qualtrics.com/rs/542-FMF412/images/The%20Qualtrics%20handbook%20of%20question%20design.pdf Qualtrics. (n.d.). Online survey software. Retrieved from https://www.qualtrics.com/core-xm/survey-software/ Reynolds, H., Dulhunty, J., Tower, M., Taraporewalla, K., & Rickard, C. (2013). A snapshot of guideline compliance reveals room for improvement: A survey of peripheral arterial catheter practices in Australian operating theatres. Journal of Advanced Nursing, 69(7), 158494. https://doi.org/10.1111/jan.12018 Sahni, N., Biswal, M., Gandhi, K., & Yaddanapudi, S. (2015). Quantification of hand hygiene compliance in anesthesia providers at a tertiary care center in northern India. American Journal of Infection Control, 43(10), 11346. https://doi.org/10.1016/j.ajic.2015.05.035 Salzman, M., Isenberg, H., & Rubin, L. (1993). Use of disinfectants to reduce microbial contamination of hubs of vascular catheters. Journal of Clinical Microbiology, 31(3), 475-9. University of California Los Angeles (UCLA) Health. (2016). Course planning tip sheet: Gap IV INJECTION PORT DISINFECTION 34 analysis [PDF File]. Retrieved from https://www.uclahealth.org/nursing/workfiles/Education%20Courses/ContinuingEducatio n/ce-GapAnalysis-052016.pdf University of Toronto. (n.d.). Quick tips: Initiating a learning needs assessment Gap analysis of health care issues [PDF File]. Retrieved from https://www.cpd.utoronto.ca/newsletter/2009_fall/Quick_Tips_initiating_a_learning_nee ds_assesment__Gap_Analysis_of_health_care_issues.pdf White, K., Dudley-Brown, S., & Terhaar, M. (Eds.). (2016). Translation of evidence into nursing and health care (2nd ed.). New York, NY: Springer Publishing Company, LLC. World Health Organization (WHO). (n.d.). Naming the coronavirus disease (COVID-19) and the virus that causes it. Retrieved from https://www.who.int/emergencies/diseases/novelcoronavirus-2019/technical-guidance/naming-the-coronavirus-disease-(covid-2019)-andthe-virus-that-causes- it Yogaraj, J. S., Elward, A. M., Fraser, V. J. (2002). Rate, risk factors, and outcomes of nosocomial primary bloodstream infection in pediatric intensive care unit patients. Pediatrics. 110(3), 481-485. https://dx.doi.org/10.1542/peds.110.3.481 IV INJECTION PORT DISINFECTION 35 Appendix A Figure 1. Steps to Conducting a Gap Analysis. Taken from University of California Los Angeles (UCLA) Health. (2016). Course planning tip sheet: Gap analysis [PDF File]. Retrieved from https://www.uclahealth.org/nursing/workfiles/Education%20Courses/ContinuingEducation/ceGapAnalysis-052016.pdf Figure 2. Stetlers Model of Research Utilization. r, r, r, refers to risk factors, resources, and readiness of others to be involved. Taken from White, K., Dudley-Brown, S., & Terhaar, M. IV INJECTION PORT DISINFECTION (Eds.). (2016). Translation of evidence into nursing and health care (2nd ed.). New York, NY: Springer Publishing Company, LLC. Figure 3. CDCs Audit Tool for Hemodialysis Injectable Medication Administration. Taken from Centers for Disease Control and Prevention (CDC). (n.d.). Injection safety: Medication preparation & administration audit tool [PDF file]. Retrieved from https://www.cdc.gov/dialysis/PDFs/collaborative/Hemodialysis-InjectionSafetyObservations.pdf 36 IV INJECTION PORT DISINFECTION 37 Appendix B Qualtrics Survey IV INJECTION PORT DISINFECTION 38 Appendix C Informed Consent E-mail IV INJECTION PORT DISINFECTION 39 Appendix D Institutional Review Board Determination Form IV INJECTION PORT DISINFECTION 40 Appendix E IV INJECTION PORT DISINFECTION 41 Institutional Review Board Amendment Form IV INJECTION PORT DISINFECTION 42 Appendix F Standard Deviation, Mean, and Variance for Practice Behavior Survey Items Survey Item Mean Variance 67.00 Standard Deviation 39.41 Perform hand hygiene prior to IV-line access Don clean gloves prior to IV-line access Scrub the IV injection port (catheter hub, needleless connector, stopcock, etc.) for 5 seconds with an appropriate disinfectant prior to IV-line access Allow drying time after scrubbing the IV injection port prior to IV-line access 61.00 36.33 1320.17 68.83 34.66 1201.31 51.33 35.27 1244.06 1553.50 IV INJECTION PORT DISINFECTION 43 Appendix G Percentage Results for Attitude Survey Items Survey Item Strongly Disagree Disagree Somewhat Disagree Somewhat Agree Agree Strongly Agree 0% Neither Agree Nor Disagree 8.33% IV injection ports should be disinfected prior to IV-line access Intraoperative IV-line care contributes to IV injection port bacterial contamination Intraoperative IV-line care contributes to bacterial transmission to patients Intraoperative IV-line care contributes to the development of postoperative infections 0% 8.33% 8.33% 33.33% 41.67% 0% 16.67% 8.33% 0% 16.67% 33.33% 25.00% 0% 25.00% 0% 25.00% 8.33% 33.33% 8.33% 8.33% 33.33% 16.67% 8.33% 8.33% 25.00% 0%  ...

... Running head: Dexamethasone Administration Adherence to ERAS Protocol Marian University Leighton School of Nursing Submitted in partial fulfillment for the degree of Doctor of Nursing Practice Evaluating Anesthesia Providers Adherence to Enhanced Recovery After Surgery (ERAS) Protocols Dexamethasone Dosage Recommendations Ashley Sanchez Chair: Bradley Stelflug ________________________ (Signature) Committee members: Adrienne Merrick _________________________ (Signature) Date of Submission: January 12, 2021 1 Dexamethasone Administration Adherence to ERAS Protocol 2 Table of Contents Abstract3 Introduction......3 Background..4 Problem Statement...4 Organizational Gap Analysis of Project Site5 Review of the Literature..5 Evidenced-Based Practice: Verification of Chosen Option...10 Evidence-Based Practice Model....10 Goals, Objectives, and Expected Outcomes..11 Project Design11 Project Site.12 Methods..12 Measurement Instrument...13 Data Collection..13 Data Analysis.14 Ethical Considerations...14 Conclusion.14 References..17 Appendices.18 Appendix A19 Appendix B25 Dexamethasone Administration Adherence to ERAS Protocol 3 This project is submitted to the faculty of Marian University Leighton School of Nursing as partial fulfillment of degree requirements for the Doctor of Nursing Practice, Nurse Anesthesia track. Enhanced Recovery After Surgery (ERAS) protocols are a significant aspect of postoperative and anesthesia care. Dexamethasone administration, specifically, is an essential component of each protocol. The use of dexamethasone helps to prevent and combat common postoperative complications. However, an adequate dosage must be administered to reap the full benefits of its use. Each healthcare facility develops and establishes its own ERAS protocol for use by anesthesia providers and includes the specific amount of dexamethasone that should be provided. However, variations in dosage administration may occur. To investigate this issue, a retrospective chart review was conducted. The purpose of the review is to assess compliance by anesthesia providers to facility-specific ERAS protocol dexamethasone administration recommendations. Introduction ERAS protocols are evidence-based, patient-centered pathways that aim to facilitate postoperative recovery (AANA, 2019). They consist of both multimodal and multidisciplinary approaches (AANA, 2019). Dexamethasone administration is an important component of ERAS regimens, as it is a highly effective method for reducing postoperative nausea and vomiting (PONV) and pain. Dexamethasone dosages are weight-based and commonly range from 4 to 8 milligrams (mg). Although lower dosages have antiemetic effects, higher dosages of approximately 8 mg are shown to have more reliable antiemetic activity with the additional benefit of analgesia (Stanford Medicine, 2018). To fulfill its intended purpose, each ERAS protocol must be precisely adhered to. Dexamethasone Administration Adherence to ERAS Protocol 4 Since variation in this aspect of perioperative anesthesia care may exist, there is a need to examine for potential gaps in this specific portion of anesthesia care. This quality improvement project aims to assess anesthesia providers adherence to their facilitys ERAS protocol recommendations for dexamethasone administration. Background Complications such as PONV and poorly controlled pain commonly prolong the recovery process (Tan et al., 2014). Both anesthetic gases and opioids can cause PONV (Tan et al., 2014). Opioids can also produce additional detrimental side effects such as respiratory depression, urinary retention, ileus, and hyperalgesia (Tan et al., 2014). As a major component of ERAS protocols, dexamethasone administration can help to bypass these difficulties through its antiemetic and analgesic properties (Tan et al., 2014). However, to ensure efficacy, dexamethasone must be administered and dosed as instructed by the ERAS protocol. Higher dosages have been shown to more reliably prevent the occurrence of PONV and possess the analgesic effects that lower dosages do not (Waldron, Jones, Gan, Allen, & Habib, 2013). Problem Statement The following clinical question was used to guide research on this identified clinical problem: In patients receiving care under the facilitys ERAS protocol, is the recommended dexamethasone dosage for postoperative nausea, vomiting, and pain reduction being adhered to by anesthesia providers? Dexamethasone Administration Adherence to ERAS Protocol 5 Organizational Gap Analysis of Project Site At the designated project site, the ERAS protocol listed in Appendix A was developed for use in open bowel procedures. Compliance has not yet been evaluated for dexamethasone dosing as part of the protocol. If less than the recommended dosage is administered, the protocol may not be effective in producing the desired outcomes. Since ERAS protocols were developed to enhance postoperative outcomes and reduce PONV and pain, the inaccurate incorporation of even one component can defeat these essential purposes. A retrospective chart analysis was performed to determine if a care gap is present and to what extent. The ultimate goal was to promote adherence and consistency so that the efficacy of the protocol could be ensured. Review of the Literature A focused search of the available literature included the use of the following keywords: ERAS protocol, ERAS regimen, preoperative dexamethasone administration, dexamethasone and antiemesis, dexamethasone and analgesia, nausea and vomiting prophylaxis, postoperative nausea and vomiting, postoperative pain, and postoperative complications. Databases used include the Joanna Briggs Institute Evidence-Based Practice Database, the Cochrane Library, and the Cumulative Index of Nursing and Allied Health Literature (CINAHL). Articles that were included discussed dexamethasone as part of ERAS protocol use and those that consisted of higher- level evidence. Articles that did not discuss dexamethasone use as part of the ERAS protocol or were lower-level evidence were excluded. Five evidence-based literature sources were chosen, consisting of both primary and secondary sources, and are summarized in a literature review matrix (see Appendix A). The author found the inclusion of secondary sources, such as systematic reviews and meta-analyses, to be beneficial due to the comprehensive Dexamethasone Administration Adherence to ERAS Protocol 6 evaluation of available evidence that they provide. Each articles findings will be discussed below. A. Effect of intraoperative dexamethasone on pain scores and narcotic consumption in patients undergoing total knee arthroplasty In this retrospective chart review, a total of 102 patients who underwent a total knee arthroplasty were used as a sample for analysis (Samona et al., 2017). They were divided into two separate groups: one who received 8 mg of dexamethasone intraoperatively and one who did not receive it at all (Samona et al., 2017). A numeric pain scale was used to assess postoperative pain levels at 24 hours after their procedures (Samona et al., 2017). The authors found that the patients who received dexamethasone had significantly lower pain scores and opioid requirements than their counterparts during their standard 3-day hospital stay (Samona et al., 2017). B. Consensus guidelines for the management of postoperative nausea and vomiting This systematic review by Gan et al. (2014) found that the administration of 8 mg of dexamethasone preoperatively improves postoperative outcomes. These include nausea, pain, and fatigue (Gan et al., 2014). The authors determined that these benefits are dose-dependent (Gan et al., 2014). Doses greater than 0.1 mg/kg were found to be more effective in producing the maximum benefits (Gan et al., 2014). Patients who received a higher dose as opposed to a lower dose of 0.05 mg/kg reported less sore throat, nausea, pain, and difficulty sleeping at 24 hours after their operation (Gan et al., 2014). Thus, the authors stated that 8 mg is the recommended preoperative dose (Gan et al., 2014). One article examined in this systematic review examined twenty-four randomized controlled trials as part of a meta-analysis (De Oliveira, Almeida, Benzon, & McCarthy, Dexamethasone Administration Adherence to ERAS Protocol 7 2011). The trials consisted of 2,751 subjects collectively. The studies were then separated into three different groups based on the dosages that the subjects received: low, intermediate, and high (De Oliveira, Almeida, Benzon, & McCarthy, 2011). The low group received less than 0.1 mg/kg of dexamethasone, the intermediate received 0.11-0.2 mg/kg, and the high received greater than 0.21 mg/kg (De Oliveira, Almeida, Benzon, & McCarthy, 2011). The intermediate and high groups showed a decrease in opioid consumption, whereas the low group did not (De Oliveira, Almeida, Benzon, & McCarthy, 2011). The authors concluded that a dose greater than 0.1 mg/kg is consistently effective at producing analgesia in multimodal pain strategies than a lesser dose (De Oliveira, Almeida, Benzon, & McCarthy, 2011). A second article by Waldron et al. (2013) reviewed forty-five studies consisting of 5,796 patients who received dexamethasone in doses ranging from 1.25-20 mg (Waldron et al., 2013). A dose of 8 mg was the most commonly provided dose (Waldron et al., 2013). The authors found that dexamethasone appears to have analgesic effects in the postoperative period (Waldron et al., 2013). Both pain scores and opioid requirements were reported to be reduced at both 2 and 24 hours (Waldron et al., 2013). Furthermore, although blood glucose levels were higher postoperatively, there seemed to be no increase in the occurrence of infection or delayed wound healing (Waldron et al., 2013). C. Dexamethasone versus standard treatment for postoperative nausea and vomiting in gastrointestinal surgery: randomised controlled trial (DREAMS Trial) The goal of the DREAMS Trial was to determine if dexamethasone reduces the occurrence of nausea and vomiting in the postoperative period, and if other benefits are also linked to its administration (DREAMS Trial, 2017). A randomized, controlled trial was Dexamethasone Administration Adherence to ERAS Protocol 8 conducted with 1,350 participants (DREAMS Trial, 2017). The trial took place in 45 United Kingdom hospitals (DREAMS Trial, 2017). The authors found that a single dose of 8 mg dexamethasone upon induction of anesthesia significantly minimized postoperative nausea and vomiting (PONV) and rescue antiemetics in patients who received gastrointestinal surgery (DREAMS Trial, 2017). D. Optimizing pain management to facilitate Enhanced Recovery After Surgery pathways According to the authors of this article, current literature summarized in systematic reviews and meta-analyses indicates that although 4 mg is effective for PONV prophylaxis, 8 mg provides the additional benefit of opioid-sparing effects (Tan, Law, & Gan, 2014). This additional benefit expedites and enhances recovery without causing postoperative complications such as infection (Tan, Law, & Gan, 2014). As an important component of the ERAS pathway, dexamethasone helps to serve as another analgesic modality in managing postoperative pain (Tan, Law, & Gan, 2014). E. Enhanced recovery after surgery for primary hip and knee arthroplasty: a review of the evidence Soffin and YaDeau (2016) conducted a systematic review in which they found that dexamethasone increases blood glucose levels in patients with and without diabetes. However, they stated that there is little evidence showing an association with dexamethasone and complications (Soffin & YaDeau, 2016). The authors explained that the peak of hyperglycemia which may occur in diabetic patients is limited (Soffin & YaDeau, 2016). Furthermore, this rise in serum glucose can be adequately managed with perioperative insulin (Soffin & YaDeau, 2016). The article encouraged providers to use their judgment for forgoing dexamethasone administration in diabetic patients on an individual basis based on Dexamethasone Administration Adherence to ERAS Protocol 9 the risk of PONV and hyperglycemia (Soffin & YaDeau, 2016). A prospective randomized controlled study within this review was comprised of eightyfive subjects that were divided into either a diabetic (Type II) or non-diabetic group (Tien et al., 2016). The patients within the groups then randomly received either 8 mg of dexamethasone or 4 mg of ondansetron (Tien et al., 2016). At 2, 4, and 24 hours following the induction of anesthesia, blood glucose levels were measured in both groups. The researchers found that blood glucose levels were increased by all patients who received dexamethasone with no increased severity among diabetics compared to non-diabetics (Tien et al., 2016). Furthermore, they stated that there was no interaction between dexamethasone administration and diabetes, specifically (Tien et al., 2016). Another article included was a randomized controlled trial conducted by Abdelmalak et al. (2013), in which a total of 185 patients were randomly allocated into a group who received 8 mg of dexamethasone and a group who received a placebo (Abdelmalak et al., 2013). Both groups contained a nearly equal percentage of diabetic patients- 23% and 29%, respectively (Abdelmalak et al., 2013). The authors found that both groups experienced a slight increase in glucose levels; however, the hyperglycemia response appeared to be more closely related to surgical stress rather than dexamethasone administration (Abdelmalak et al., 2013). They concluded that the hyperglycemic effect is limited and should not be a sole reason to deny steroid prophylaxis to diabetic patients (Abdelmalak et al., 2013). Evidence-Based Practice: Verification of Chosen Option ERAS protocols are facility-specific and were developed based on the current evidencebased research and guidelines. Hence, it is essential to conduct a chart audit to ensure that anesthesia providers comply with the recommendations within them. Dexamethasone Administration Adherence to ERAS Protocol 10 Evidenced-Based Practice Model The Johns Hopkins Nursing Evidence-Based Practice Model (see Appendix A) aims to ensure that research findings and best practices are properly incorporated into patient care (JHM, n.d.). It consists of a three-step process, known as PET: practice question, evidence, and translation (JHM, n.d.). To utilize the model, a research question is formulated, the evidence is obtained, and the data is then analyzed for translation (JHM, n.d.). In regard to this project, the practice question is as follows: In patients receiving care under the Enhanced Recovery After Surgery Protocol, is the recommended dexamethasone dosage for postoperative nausea and vomiting and pain reduction being adhered to by anesthesia providers? The evidence includes the specific dexamethasone dosages that have been collected from patient charts. Lastly, the translation consists of evaluating the results of comparative analysis and determining whether or not the specific dexamethasone administration component of the ERAS protocol was adhered to. This information can then be used to formulate a plan for process improvement, such as provider education, if the findings indicate a need for such further action. Goals, Objectives, and Expected Outcomes The goal of this project was to determine if anesthesia providers within the chosen surgical facility have been adhering to the minimum preoperative dexamethasone dosage recommendations listed within the specific ERAS protocol established by the facility. The exact doses of dexamethasone that were administered to each of the patients within the audit sample were collected and compared to the dose that is recommended by the facility-specific protocol. This information was extracted from approximately 50 patient charts that met the chosen patient criteria. If a gap were to be identified, the findings could then be used to determine strategies for reducing care variation in ERAS protocol use and improving protocol compliance. Dexamethasone Administration Adherence to ERAS Protocol 11 Project Design A sample population was formed by identifying patients at the facility who have received anesthesia care under the ERAS regimen. The protocol was displayed as activated within their charts. Inclusion criteria included adult patients greater than 18 years old, having received the ERAS protocol for a surgical procedure within the past year (2019-2020), and having dexamethasone administration documented in the chart. Exclusion criteria included patients less than 18 years old, not having the ERAS protocol activated within the chart, procedures having occurred greater than 1 year ago, and no documentation of dexamethasone administration. A retrospective chart review was then performed to determine ERAS protocol dexamethasone administration compliance. Compliance was determined by comparing the amount of dexamethasone administered with the amount recommended. Since providers are more likely to alter the dose of dexamethasone if the patient has a history of diabetes, this was accounted for by identifying the patients as either diabetic or non-diabetic based on their listed medical history. Project Site This project was carried out at Indiana University Health (IU Health) Arnett Hospital, located in Lafayette, Indiana. The facility is a not-for-profit, full-service hospital that has an adjacent outpatient surgery center (IU Health, 2016). It has been designated as a Magnet Hospital, which acknowledges its nursing excellence and high-quality patient outcomes (IU Health, 2020). Arnett hospitals parent system, IU Health, prioritizes academia and breakthrough research (IU Health, 2020). In 2018, the system reported an annual total of 110,445 surgical cases, which consists of a wide range of specialties and complexity (IU Health, 2020). Lafayette, Indiana is the largest city in Tippecanoe County (STATSIndiana, n.d.). Tippecanoe County has a Dexamethasone Administration Adherence to ERAS Protocol 12 population of 195,732 citizens and is comprised of both rural and urban areas (STATSIndiana, n.d.). Methods Project review was completed by Marian Universitys Institutional Review Board (IRB) prior to data collection. A de-identified sample consisting of 50 patient charts was then created based on the inclusion and exclusion criteria previously mentioned. The adult patients within the sample all received the ERAS regimen and dexamethasone administration, specifically, as part of their surgical care from January 2019 to April 2020. Additionally, patients were identified as either diabetic or non-diabetic based on their available medical histories since this patient factor commonly contributes to the inadequate dosing of dexamethasone for PONV and analgesic purposes. The exact amount of dexamethasone in mg that was administered to each patient by the anesthesia provider was extracted and compiled. The data was then analyzed by the researcher for the diabetic group, non-diabetic group, and as a whole to determine if these amounts match the amount that is part of the ERAS protocol. Data analysis was conducted with the use of Statistical Package for Social Sciences (SPSS) Statistics software to produce descriptive statistics and conduct a T-test to determine result significance. Measurement Instrument The specific amount of dexamethasone that was administered to each patient in the sample was collected through the use of the facilitys EHR system with the assistance of a Quality and Safety staff member. The system displays which patients have had the ERAS protocol in place for their surgical care. The amount of preoperative dexamethasone that was Dexamethasone Administration Adherence to ERAS Protocol 13 administered by the anesthesia provider is recorded in the anesthesia record of each patients chart. Additionally, documented medical history was used to identify diabetic patients. Data Collection Using the Johns Hopkins Model for guidance, evidence was obtained through a retrospective chart review. The specific data that was collected is the dose of dexamethasone in mg that was administered to each patient within the sample who received care under the facilitys ERAS protocol. Medical histories were also reviewed to identify a history of diabetes. Next, the data was analyzed by SSPS software. Finally, the doses collected in the data set were compared to the facilitys ERAS protocol recommendations. Data Analysis Data analysis was conducted using SSPS software. The minimum dose that was administered was 4 mg, while the maximum dose was 20 mg. The most common dose was 8 mg, with the mean dose being 8.2 mg. Out of 50 patients examined, 45 (88%) received the minimum ERAS-recommended dose of 8 mg, 1 received 5 mg, and 5 received 4 mg (see Appendix B, Table 1). All patients with a history of diabetes received a dose of 8 mg (see Appendix B, Table 2). However, only 83% of patients without a history of diabetes received a minimum dose of 8 mg (see Appendix B, Table 3). A one-sample t-test was conducted, for which the test value was set to 8, representing the 8 mg dose that the ERAS protocol recommends. The P-value for significance was set to less than or equal to 0.03. Since the value produced by the test was 0.62, the results were deemed as statistically insignificant. Ethical Considerations IRB review was obtained from Marian University before the start of this project, which was determined to be exempt. No patient interventions were conducted and all patient-related Dexamethasone Administration Adherence to ERAS Protocol 14 information used was de-identified. The researcher obtained the data with the assistance of a member of the facilitys Quality and Safety Team, ensuring that all identifiable patient information remained protected. Conclusion The use of ERAS protocols has helped to make major strides in improving postoperative care and outcomes (AANA, 2019). They prioritize patient-centered, evidence-based, multimodal perioperative care and are intended to reduce surgical stress, optimize physiologic function, and enhance recovery (AANA, 2019). When followed precisely, ERAS protocols are highly effective in accomplishing these goals. Thus, it is crucial to ensure that anesthesia providers are closely adhering to the recommendations within them. The results produced by this retrospective chart analysis suggest that anesthesia providers are largely complying with ERAS protocol dexamethasone recommendations. A history of diabetes did not appear to correlate with under-dosing of the medication, contrary to what was anticipated (see Appendix B, Table 1). However, a gap in care is still present, as evidenced by the instances where dexamethasone was underdosed. Although the occurrence percentage of inadequate dosing was rare, this deviation certainly still has the potential to impact the quality of anesthesia care and patient experience. Anesthesia providers should continue aiming for 100% compliance so that all patients may fully receive the recovery-enhancing benefits provided by dexamethasone administration as part of ERAS protocol implementation. One limitation of this project was having a small sample size. In future assessments, the use of a larger sample size may produce different or more reliable results. Additionally, weights and preoperative blood glucose levels were not examined or obtained for each patient as part of the data collection process. A low body weight or significantly elevated blood glucose level may Dexamethasone Administration Adherence to ERAS Protocol 15 have contributed to providers administering a dose less than the 8 mg that the facilitys ERAS protocol calls for. The collection of this information in future studies may also be helpful in fully examining compliance. To improve compliance, educational briefings and reminders can be provided for anesthesia providers regarding the importance of adequate dexamethasone dosing. Some providers may not be aware of the additional benefits that a larger dose provides, especially for patients under the ERAS regimen. In the future, dexamethasone administration can be examined outside of the ERAS protocol, which may provide a broader view of dosing variation. Care consistency is an essential aspect of anesthesia care, along with evidence-based practice. Hence, variation reduction and protocol adherence should continue to be a top priority. Dexamethasone Administration Adherence to ERAS Protocol 16 References Abdelmalak, B. B., Bonilla, A. M., Yang, D., Chowdary, H. T., Gottlieb, A., Lyden, S. P., & Sessler, D. I. (2013). The hyperglycemic response to major noncardiac surgery and the added effect of steroid administration in patients with and without diabetes. Anesthesia and analgesia, 116(5), 11161122. American Association of Nurse Anesthetists. (2019). Enhanced Recovery after Surgery. Retrieved from https://www.aana.com/practice/clinical-practice-resources/enhancedrecovery-after-surgery DREAMS Trial Collaborators and West Midlands Research Collaborative (2017). Dexamethasone versus standard treatment for postoperative nausea and vomiting in gastrointestinal surgery: randomised controlled trial (DREAMS Trial). BMJ (Clinical research ed.), 357, 1455. De Oliveira, G. S. , Almeida, M. D. , Benzon, H. T. & McCarthy, R. J. (2011). Perioperative Single Dose Systemic Dexamethasone for Postoperative Pain. Anesthesiology, 115(3), 575588. Gan, T. J., Diemunsch, P., Habib, A. S., Kovac, A., Kranke, P., Meyer, T. A., ...Tramr, M. R. (2014). Consensus guidelines for the management of postoperative nausea and vomiting. Anesthesia and Analgesia, 118(1), 85-113. IU Health. (2020). IU Health Arnett Hospital - Lafayette. Retrieved from https://iuhealth.org/find-locations/iu-health-arnetthospital?gclid=CjwKCAjw1cX0BRBmEiwAy9tKHve9BHbJfzAmfDGctRDrN6VU99g WnBDkP2wo7zDrC5d5t2bkoTCzgxoCNH4QAvD_BwE IU Health. (2016). 2016 Community Benefit Report. Retrieved from https://cdn.iuhealth.org/resources/09501019_Community_Benefit_Report_WEB.PDF?mtime=20180117144229 JHM. (n.d.). Johns Hopkins Nursing Evidence-Based Practice Model. Retrieved from https://www.hopkinsmedicine.org/evidence-based-practice/ijhn_2017_ebp.html Samona, J., Cook, C., Krupa, K., Swatsell, K., Jackson, A., Dukes, C., & Martin, S. (2017). Effect of Intraoperative Dexamethasone on Pain Scores and Narcotic Consumption in Patients Undergoing Total Knee Arthroplasty. Orthopaedic surgery, 9(1), 110114. Soffin, E., & YaDeau, J. (2016). Enhanced recovery after surgery for primary hip and knee arthroplasty: A review of the evidence. British Journal of Anaesthesia, 117, 72. Stanford Medicine. (2018). Perioperative Opioid-Sparing Analgesia Strategies for Enhanced Dexamethasone Administration Adherence to ERAS Protocol 17 Recovery After Surgery. Retrieved from https://www.isaponline.org/application/files/4715/4239/1112/ISAP_18AM_Presentation_ Anderson.pdf STATSIndiana. (n.d.). Tippecanoe County, Indiana. Retrieved from http://www.stats.indiana.edu/profiles/profiles.asp?scope_choice=a&county_changer=181 57 Tan, M., Law, L.S., & Gan, T.J. (2014). Optimizing pain management to facilitate Enhanced Recovery After Surgery pathways. Canadian Journal of Anesthesia/Journal canadien danesthsie, 62, 203-218. Tien, M., Gan, T. J., Dhakal, I., White, W. D., Olufolabi, A. J., Fink, R., Mishriky, B. M., Lacassie, H. J., & Habib, A. S. (2016). The effect of anti-emetic doses of dexamethasone on postoperative blood glucose levels in non-diabetic and diabetic patients: a prospective randomised controlled study. Anaesthesia, 71(9), 10371043. Waldron, N.H., Jones, C.A., Gan, T.J., Allen, T.K., & Habib, A.S. (2013). Impact of perioperative dexamethasone on postoperative analgesia and side-effects: systematic review and meta-analysis. British journal of anaesthesia, 110 2, 191-200. Dexamethasone Administration Adherence to ERAS Protocol Appendix A 18 19 Dexamethasone Administration Adherence to ERAS Protocol Citation Variables of Interest (Keywords) Samona, J., Cook, C., Krupa, K., Swatsell, K., Jackson, A., Dukes, C., & Martin, S. (2017). Effect of Intraoperative Dexamethasone on Pain Scores and Narcotic Consumption in Patients Undergoing Total Knee Arthroplasty. Orthopaedic surgery, 9(1), 110114. Intraoperative dexamethasone Narcotic requirements Opioid requirements Postoperative pain Analgesia Literature Type Level of evidence: IV (retrospective study) Research Design & Sample Size Case series 102 patients Key Findings Patients who received dexamethasone required significantly less opioids than those who did not A single dose of dexamethasone given intraoperatively significantly decreases narcotic consumption and decreases pain scores 24 postoperatively. Dexamethasone Administration Adherence to ERAS Protocol Consensus guidelines for the management of postoperative nausea and vomiting: Erratum. (2015). Anesthes ia & Analgesia, 120( 2), 494-494. General anesthesia Anesthesia recovery period Postoperative pain Nausea Vomiting Postoperative nausea and vomiting Postoperative complications Preanesthetic medication Preoperative care Intraoperative care Level of evidence: I (systematic review) Review # of studies not explicitly stated 20 Preoperative dexamethasone 8 mg enhances the postdischarge quality of recovery in addition to reducing nausea, pain, and fatigue Dexamethasone has dose-dependent effects on quality of recovery At 24 hours, patients receiving dexamethasone 0.1 vs 0.05 mg/kg required less opioid and reported less nausea, sore throat, muscle pain, and difficulty falling asleep Doses >0.1 mg/kg are an effective adjunct in multimodal strategies to reduce postoperative pain and opioid consumption Dexamethasone Administration Adherence to ERAS Protocol DREAMS Trial Collaborators and West Midlands Research Collaborative (2017). Dexamethasone versus standard treatment for postoperative nausea and vomiting in gastrointestinal surgery: randomised controlled trial (DREAMS Trial). BMJ (Clinical research ed.), 357, 1455. Dexamethasone Postoperative nausea and vomiting Gastrointestinal surgery Level of evidence: II (pragmatic blinded multicenter randomized controlled trial) Randomized controlled trial 1,350 participants 45 U.K. hospitals 21 Addition of a single dose of 8 mg intravenous dexamethasone at induction of anaesthesia significantly reduces both the incidence of postoperative nausea and vomiting at 24 hours and the need for rescue antiemetics for up to 72 hours in patients undergoing large and small bowel surgery, with no increase in adverse events Dexamethasone Administration Adherence to ERAS Protocol Tan, M., Law, L.S., & Gan, T.J. (2014). Optimizing pain management to facilitate Enhanced Recovery After Surgery pathways. Cana dian Journal of Anesthesia/Jour nal canadien danesthsie, 62, 203-218. Enhanced recovery after surgery Multimodal Pain Analgesia Level of evidence: I (systematic review) Review # of papers reviewed not explicitly stated 22 Current literature supports a single prophylactic dose of dexamethasone 4 mg at induction for PONV prophylaxis, with 8 mg providing additional opioid-sparing effects and quicker recovery without postoperative complications such as infection, wound separation, and dehiscence Enhanced recovery pathways facilitate evidence-based comprehensive perioperative care, including postoperative pain management, with the aim of accelerating recovery and discharge after surgery The use of more than one analgesic modality to achieve effective pain control while minimizing the side effects of opioids that delay discharge has become the standard of care in ERAS protocols Postoperative pain continues to be undermanaged Dexamethasone Administration Adherence to ERAS Protocol Gan, T., Meyer, T., Apfel, C., Chung, F., Davis, P., Habib, A., . . . Society for Ambulatory Anesthesia. (2007). Society for ambulatory anesthesia guidelines for the management of postoperative nausea and vomiting. Anest hesia and Analgesia, 105( 6), 1615-28. Ambulatory anesthesia Postoperative nausea and vomiting Level of evidence: I (evidence-based clinical practice guidelines) Review Recommendati ons were made only if they were supported by randomized trials and systematic reviews of randomized trials # reviewed not explicitly stated 23 Dexamethasone is recommended at a prophylactic dose of 4 5 mg IV for patients at increased risk for PONV Dexamethasone Administration Adherence to ERAS Protocol Appendix B Table 1. Frequency of individual dexamethasone dosages administered. Table 2. Dexamethasone dosages administered to diabetic patients. 24 Dexamethasone Administration Adherence to ERAS Protocol Table 3. Dexamethasone dosages administered to non-diabetic patients. 25 Dexamethasone Administration Adherence to ERAS Protocol 26  ...

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Two-Handed Ventilation 44 SIMULATION-BASED TRAINING FOR BMV Appendix D Demographics Questionnaire 45 SIMULATION-BASED TRAINING FOR BMV 46 Appendix E Student Satisfaction and Self-Confidence in Learning Student Satisfaction and Self-Confidence in Learning Instructions: This questionnaire is a series of statements about your personal attitudes about the instruction you receive during your simulation activity. Each item represents a statement about your attitude toward your satisfaction with learning and self-confidence in obtaining the instruction you need. There are no right or wrong answers. You will probably agree with some of the statements and disagree with others. Please indicate your own personal feelings about each statement below by marking the numbers that best describe your attitude or beliefs. Please be truthful and describe your attitude as it really is, not what you would like for it to be. This is anonymous with the results being compiled as a group, not individually. Mark: 1 = STRONGLY DISAGREE with the statement 2 = DISAGREE with the statement 3 = UNDECIDED - you neither agree or disagree with the statement 4 = AGREE with the statement 5 = STRONGLY AGREE with the statement Satisfaction with Current Learning SD D UN A SA 1. The teaching methods used in this simulation were helpful and effective. 1 2 3 4 5 2. The simulation provided me with a variety of learning materials and activities to promote my learning the medical surgical curriculum. 1 2 3 4 5 3. I enjoyed how my instructor taught the simulation. 1 2 3 4 5 4. The teaching materials used in this simulation were motivating and helped me to learn. 1 2 3 4 5 5. The way my instructor(s) taught the simulation was suitable to the way I learn. 1 2 3 4 5 Self-confidence in Learning 6. I am confident that I am mastering the content of the simulation activity that my instructors presented to me. SD D UN A SA 1 2 3 4 5 4. The teaching materials used in this simulation were motivating and helped me to learn. 1 5. The way myE instructor(s) taught the simulation was suitable to the way I learn. Appendix cont. 1 2 3 4 SIMULATION-BASED TRAINING FOR BMV Self-confidence in Learning 5 47 2 3 4 5 SD D UN A SA 6. I am confident that I am mastering the content of the simulation activity that my instructors presented to me. 1 2 3 4 5 7. I am confident that this simulation covered critical content necessary for the mastery of medical surgical curriculum. 1 2 3 4 5 8. I am confident that I am developing the skills and obtaining the required knowledge from this simulation to perform necessary tasks in a clinical setting 1 2 3 4 5 9. My instructors used helpful resources to teach the simulation. 1 2 3 4 5 10. It is my responsibility as the student to learn what I need to know from this simulation activity. 1 2 3 4 5 11. I know how to get help when I do not understand the concepts covered in the simulation. 1 2 3 4 5 12. I know how to use simulation activities to learn critical aspects of these skills. 1 2 3 4 5 13. It is the instructor's responsibility to tell me what I need to learn of the simulation activity content during class time.. 1 2 3 4 5 Copyright, National League for Nursing, 2005 Revised December 22, 2004 "Student Satisfaction and Self-Confidence in Learning Questionnaire," by National League for Nursing, 2004 (http://www.nln.org/docs/default-source/default-document-library/instrument2_satisfaction-and-self-confidence-in-learning.pdf?sfvrsn=0). Copyright 2005 by National League for Nursing Inc. Reprinted with permission. SIMULATION-BASED TRAINING FOR BMV Appendix F Modified Student Satisfaction and Self-Confidence in Learning 48 SIMULATION-BASED TRAINING FOR BMV Appendix F cont. 49 SIMULATION-BASED TRAINING FOR BMV Appendix G Knowledge Assessment Questionnaire 50 SIMULATION-BASED TRAINING FOR BMV Appendix H IRB Exemption Form 51 SIMULATION-BASED TRAINING FOR BMV 52 Appendix I Table 8 Helpfulness of Teaching Methods Used in Stimulation Helpfulness of Teaching Methods Pre-Test Control Group Strongly Disagree Disagree Undecided Agree Strongly Agree n % 0 3 4 5 0 0.0 25.0 33.3 41.7 0.0 Post-Test Control Group Strongly Disagree Disagree Undecided Agree Strongly Agree 0 1 2 6 3 0.0 8.3 16.7 50.0 25.0 Pre-Test Experimental Group Strongly Disagree Disagree Undecided Agree Strongly Agree 0 1 2 7 2 0.0 8.3 16.7 58.3 16.7 Post-Test Experimental Group Strongly Disagree Disagree Undecided Agree Strongly Agree 0 0 0 4 8 0.0 0.0 0.0 33.3 66.7 Note. n=12 SIMULATION-BASED TRAINING FOR BMV 53 Appendix I cont. Table 9 Promotion of Learning within the CRNA Curriculum through the Learning Materials and Activities Provided by the Simulation Promotion of Learning Pre-Test Control Group Strongly Disagree Disagree Undecided Agree Strongly Agree Post-Test Control Group Strongly Disagree Disagree Undecided Agree Strongly Agree Pre-Test Experimental Group Strongly Disagree Disagree Undecided Agree Strongly Agree Post-Test Experimental Group Strongly Disagree Disagree Undecided Agree Strongly Agree Note. n=12 n % 0 2 3 6 1 0.0 16.7 25.0 50.0 8.3 0 2 3 6 1 0.0 16.7 25.0 50.0 8.3 0 1 3 7 1 0.0 8.3 25.0 58.3 8.3 0 0 0 4 8 0.0 0.0 0.0 33.3 66.7 SIMULATION-BASED TRAINING FOR BMV 54 Appendix I cont. Table 10 Enjoyment of Simulation Teaching Methods Enjoyment of Simulation Teaching Methods Pre-Test Control Group Strongly Disagree Disagree Undecided Agree Strongly Agree n % 0 5 2 4 1 0.0 41.7 16.7 33.3 8.3 Post-Test Control Group Strongly Disagree Disagree Undecided Agree Strongly Agree 0 1 3 6 2 0.0 8.3 25.0 50.0 16.7 Pre-Test Experimental Group Strongly Disagree Disagree Undecided Agree Strongly Agree 0 1 4 6 1 0.0 8.3 33.3 50.0 8.3 Post-Test Experimental Group Strongly Disagree Disagree Undecided Agree Strongly Agree 0 0 0 3 9 0.0 0.0 0.0 25.0 75.0 Note. n=12 SIMULATION-BASED TRAINING FOR BMV 55 Appendix I cont. Table 11 Teaching Materials in Simulation Were Motivating and Helped Students Learn Motivating and Helpful Pre-Test Control Group Strongly Disagree Disagree Undecided Agree Strongly Agree Post-Test Control Group Strongly Disagree Disagree Undecided Agree Strongly Agree Pre-Test Experimental Group Strongly Disagree Disagree Undecided Agree Strongly Agree Post-Test Experimental Group Strongly Disagree Disagree Undecided Agree Strongly Agree Note. n=12 n % 1 2 2 6 1 8.3 16.7 16.7 50.0 8.3 0 1 2 6 3 0.0 8.3 16.7 50.0 25.0 0 2 4 5 1 0.0 16.7 33.3 41.7 8.3 0 0 0 4 8 0.0 0.0 0.0 33.3 66.7 SIMULATION-BASED TRAINING FOR BMV 56 Appendix I cont. Table 12 Simulation Teaching Methods Suitable to Learning Style Simulation Teaching Methods Suitable to Learning Style Pre-Test Control Group Strongly Disagree Disagree Undecided Agree Strongly Agree Post-Test Control Group Strongly Disagree Disagree Undecided Agree Strongly Agree Pre-Test Experimental Group Strongly Disagree Disagree Undecided Agree Strongly Agree Post-Test Experimental Group Strongly Disagree Disagree Undecided Agree Strongly Agree Note. n=12 n % 0 5 0 6 1 0.0 41.7 0.0 50.0 8.3 0 3 0 6 3 0.0 25.0 0.0 50.0 25.0 0 1 4 6 1 0.0 8.3 33.3 50.0 8.3 0 0 0 4 8 0.0 0.0 0.0 33.3 66.7 SIMULATION-BASED TRAINING FOR BMV 57 Appendix I cont. Table 13 Confidence in Mastering the Content of the Simulation Activity Confidence in Mastering the Content of the Simulation Activity Pre-Test Control Group Strongly Disagree Disagree Undecided Agree Strongly Agree n % 0 1 4 7 0 0.0 8.3 33.3 58.3 0.0 Post-Test Control Group Strongly Disagree Disagree Undecided Agree Strongly Agree 0 1 2 8 1 0.0 8.3 16.7 66.7 8.3 Pre-Test Experimental Group Strongly Disagree Disagree Undecided Agree Strongly Agree 0 1 4 6 1 0.0 8.3 33.3 50.0 8.3 Post-Test Experimental Group Strongly Disagree Disagree Undecided Agree Strongly Agree 0 0 0 4 8 0.0 0.0 0.0 33.3 66.7 Note. n=12 SIMULATION-BASED TRAINING FOR BMV 58 Appendix I cont. Table 14 Confidence that the Simulation Covered Critical Content Confidence that Simulation Covered Critical Content Pre-Test Control Group Strongly Disagree Disagree Undecided Agree Strongly Agree Post-Test Control Group Strongly Disagree Disagree Undecided Agree Strongly Agree Pre-Test Experimental Group Strongly Disagree Disagree Undecided Agree Strongly Agree Post-Test Experimental Group Strongly Disagree Disagree Undecided Agree Strongly Agree Note. n=12 n % 0 2 4 6 0 0.0 16.7 33.3 50.0 0.0 0 0 2 10 0 0.0 0.0 16.7 83.3 0.0 0 0 4 7 1 0.0 0.0 33.3 58.3 8.3 0 0 0 4 8 0.0 0.0 0.0 33.3 66.7 SIMULATION-BASED TRAINING FOR BMV 59 Appendix I cont. Table 15 Confidence in Developing the Skills and Obtaining the Knowledge from the Simulation to Perform Necessary Tasks in a Clinical Setting Confidence in Developing Skills and Obtaining Knowledge Pre-Test Control Group Strongly Disagree Disagree Undecided Agree Strongly Agree Post-Test Control Group Strongly Disagree Disagree Undecided Agree Strongly Agree Pre-Test Experimental Group Strongly Disagree Disagree Undecided Agree Strongly Agree Post-Test Experimental Group Strongly Disagree Disagree Undecided Agree Strongly Agree Note. n=12 n % 0 2 3 6 1 0.0 16.7 25.0 50.0 8.3 0 1 3 7 1 0.0 8.3 25.0 58.3 8.3 0 1 3 7 1 0.0 8.3 25.0 58.3 8.3 0 0 0 3 9 0.0 0.0 0.0 25.0 75.0 SIMULATION-BASED TRAINING FOR BMV 60 Appendix I cont. Table 16 Helpfulness of Resources Instructors Used to Teach Simulation Helpfulness of Resources Instructors Used to Teach Simulation Pre-Test Control Group Strongly Disagree Disagree Undecided Agree Strongly Agree n % 1 2 2 6 1 8.3 16.7 16.7 50.0 8.3 Post-Test Control Group Strongly Disagree Disagree Undecided Agree Strongly Agree 0 1 3 6 2 0.0 8.3 25.0 50.0 16.7 Pre-Test Experimental Group Strongly Disagree Disagree Undecided Agree Strongly Agree 0 1 4 6 1 0.0 8.3 33.3 50.0 8.3 Post-Test Experimental Group Strongly Disagree Disagree Undecided Agree Strongly Agree 0 0 0 4 8 0.0 0.0 0.0 33.3 66.7 Note. n=12 SIMULATION-BASED TRAINING FOR BMV 61 Appendix I cont. Table 17 Student Responsibility to Learn from the Simulation Activity Student Responsibility to Learn from the Simulation Activity Pre-Test Control Group Strongly Disagree Disagree Undecided Agree Strongly Agree n % 0 2 1 7 2 0.0 16.7 8.3 58.3 16.7 Post-Test Control Group Strongly Disagree Disagree Undecided Agree Strongly Agree 1 1 1 5 4 8.3 8.3 8.3 41.7 33.3 Pre-Test Experimental Group Strongly Disagree Disagree Undecided Agree Strongly Agree 0 0 1 6 5 0.0 0.0 8.3 50.0 41.7 Post-Test Experimental Group Strongly Disagree Disagree Undecided Agree Strongly Agree 0 0 0 2 10 0.0 0.0 0.0 16.7 83.3 Note. n=12 SIMULATION-BASED TRAINING FOR BMV 62 Appendix I cont. Table 18 Knowing How to Get Help with Confusing Concepts Knowing How to Get Help with Confusing Concepts Pre-Test Control Group Strongly Disagree Disagree Undecided Agree Strongly Agree n % 0 0 0 8 4 0.0 0.0 0.0 66.7 33.3 Post-Test Control Group Strongly Disagree Disagree Undecided Agree Strongly Agree 0 0 0 8 4 0.0 0.0 0.0 66.7 33.3 Pre-Test Experimental Group Strongly Disagree Disagree Undecided Agree Strongly Agree 0 0 1 6 5 0.0 0.0 8.3 50.0 41.7 Post-Test Experimental Group Strongly Disagree Disagree Undecided Agree Strongly Agree 0 0 0 3 9 0.0 0.0 0.0 25.0 75.0 Note. n=12 SIMULATION-BASED TRAINING FOR BMV 63 Appendix I cont. Table 19 Using Simulation Activities to Learn Critical Aspects of Skills Using Simulation Activities to Learn Critical Aspects of Skills Pre-Test Control Group Strongly Disagree Disagree Undecided Agree Strongly Agree n % 0 0 2 6 4 0.0 0.0 16.7 50.0 33.3 Post-Test Control Group Strongly Disagree Disagree Undecided Agree Strongly Agree 0 1 1 7 3 0.0 8.3 8.3 58.3 25.0 Pre-Test Experimental Group Strongly Disagree Disagree Undecided Agree Strongly Agree 0 1 1 8 2 0.0 8.3 8.3 66.7 16.7 Post-Test Experimental Group Strongly Disagree Disagree Undecided Agree Strongly Agree 0 0 0 5 7 0.0 0.0 0.0 41.7 58.3 Note. n=12 SIMULATION-BASED TRAINING FOR BMV 64 Appendix I cont. Table 20 Responsibility of Instructors to Educate on Simulation during Class Responsibility of Instructors to Educate on Simulation Pre-Test Control Group Strongly Disagree Disagree Undecided Agree Strongly Agree Post-Test Control Group Strongly Disagree Disagree Undecided Agree Strongly Agree Pre-Test Experimental Group Strongly Disagree Disagree Undecided Agree Strongly Agree Post-Test Experimental Group Strongly Disagree Disagree Undecided Agree Strongly Agree Note. n=12 n % 0 0 0 9 3 0.0 0.0 0.0 75.0 25.0 0 0 1 8 3 0.0 0.0 8.3 66.7 25.0 0 0 2 7 3 0.0 0.0 16.7 58.3 25.0 0 0 0 5 7 0.0 0.0 0.0 41.7 58.3 SIMULATION-BASED TRAINING FOR BMV 65 Appendix J cont. Table 21 Correct Factors that Have Been Identified as Predictors for Difficulty with BMV Correct Factors Selected n % Correct Age Greater than 55 Correct - Control Correct Experimental 12 12 100 100 Mallampati Score III Correct - Control Correct - Experimental 7 8 58.3 66.7 No Teeth Correct - Control Correct - Experimental 12 11 100 91.7 Short Thyromental Distance Correct - Control Correct - Experimental 9 9 75.0 75.0 Note. n=24 SIMULATION-BASED TRAINING FOR BMV 66 Appendix J cont. Table 22 Incorrect Factors that Have Been Identified as Predictors for Difficulty with BMV Incorrect Factors Selected n % Correct Age Younger than 12 Incorrect - Control Incorrect - Experimental 3 0 25.0 0.0 Long Thyromental Distance Incorrect - Control Incorrect- Experimental 1 2 8.3 16.7 Mallampati Score II Incorrect - Control Incorrect - Experimental 0 0 0.0 0.0 Short Interincisor Distance Incorrect - Control Incorrect Experimental 4 4 33.3 33.3 Note. n=24  ...

... BEST PRACTICES FOR GENERAL INDUCTION SIMULATION 1 Marian University Leighton School of Nursing Doctor of Nursing Practice Using of Simulation Best Practices for Building Knowledge and Confidence for General Anesthesia Induction Aaron Michael Mojica Submitted in partial fulfillment of degree requirements for the Doctor of Nursing Practice Nurse Anesthesia Marian University Leighton School of Nursing Chair: Dr. Bradley Stelflug _________________________ (Signature) (Date) Committee members: Dr. Christina Pepin _________________________ (Signature) (Date) Christina Pepin (Mar 15, 2022 10:53 EDT) Dr. Guang Xu 3/9/2022 _________________________ (Signature) (Date) Date of Submission: Mar 15, 2022 Mar 15, 2022 BEST PRACTICES FOR GENERAL INDUCTION SIMULATION 2 Using of Simulation Best Practices for Building Knowledge and Confidence for General Anesthesia Induction The primary purpose of graduate nurse anesthesia programs is to develop and achieve nurses' complete professional competence to deliver individualized, safe, and effective anesthesia care. In preparing and developing Student Registered Nurse Anesthetists (SRNAs) to become Certified Registered Nurse Anesthetists (CRNAs), simulation-based learning has become a fundamental component to nursing education, bridging didactic learning into real-life clinical experience. Simulation practice amplifies realistic experiences through guided understandings that imitate an actual clinical setting (Lateef, 2010). By completing simulation-based learnings, SRNAs may incorporate the skills and lessons learned from the simulation experience and integrate skill competencies into their clinical setting (Brown, 2017). For SRNAs to be deemed competent to perform a skill, anesthesia checklists must be completed that demonstrate a students' knowledge and ability to perform a task (Lateef, 2010). A skillset measured in SRNAs includes completing general anesthesia (GA) through an induction technique often referred to as GA induction (Council on Accreditation of Nurse Anesthesia Educational Programs (COA), 2019). This competency can be achieved by combining medications that place the patient in a sleep-like state during which they are unarousable (Freeman & Berger, 2014; King, 2018). Pharmacologic agents in achieving GA induction can be done through intravenous or inhalation agents or combination (King, 2018). Patients who undergo GA often cannot maintain independent ventilation and require assistance in maintaining a patent airway through the placement of an endotracheal tube (Freeman & Berger, 2014). The goals for GA induction are to rapidly and efficiently produce safe conditions while maintaining hemodynamic stability and ventilation (King, 2018). When performing GA BEST PRACTICES FOR GENERAL INDUCTION SIMULATION 3 inductions, CRNAs must balance anesthesia conditions such as amnesia, hypnosis, analgesia, akinesia, and attenuation of autonomic nervous system response (Freeman & Berger, 2014; King, 2018). As stated by the American Association of Nurse Anesthetists (2019), CRNAs administer more than 49 million anesthetic agents to patients yearly. As SRNAs are expected to build on foundational skills, the importance of GA induction becomes increasingly invaluable. They are expected to utilize learnings from this technique to manage difficult airways. In approximately 1 of 5,000 to 10,000 GA inductions, a rare and high-risk event may occur as a "cannot ventilate, cannot intubate" scenario and require emergent surgical airway management (Scott-Herring et al., 2020). As simulation-based experiences are purposefully designed to prepare SRNAs for principal competencies, it is crucial that SRNAs thoroughly understand the importance of a GA induction technique that will later prepare them for these acute high-risk settings. Creating a focused simulation design promotes an essential structure needed to support SRNA confidence and performance success on GA inductions. Additionally, the successful completion of simulation designs is consistent with graduate nurse anesthesia institutional outcomes and program goals (International Nursing Association for Clinical Simulation Learning (INACSL) Standards Committee, 2016). At Marian University, the current state in which SRNAs are expected to complete GA induction competency does not fully meet or follow the necessary standards set forth by the INACSL board. Identifying this weakness on how GA induction competencies are conducted, the purpose of this project is to examine if providing SRNAs a standardized simulation training video with detailed instructions affects their knowledge and confidence of anesthesia induction technique, compared to current simulation practice. BEST PRACTICES FOR GENERAL INDUCTION SIMULATION 4 Background In the late 1920s, the first successful use of simulation began with aviation with the link trainer's development (Aebersold, 2016). This first flight simulator consisted of a small wooden airplane on a universal joint that simulated the motion of flying (Aebersold, 2016). In nursing, Martha Jenkins Chase built the first mannequin in 1911 to teach nurses how to dress, turn, and transfer patients (Aebersold, 2016). It was not until the 1960s when the Sierra Engineering Company developed its first high-fidelity simulator known as Sim One (Aebersold, 2016). Sim One was used to train anesthesia providers in completing endotracheal intubations and GA inductions (Lateef, 2010). In 1986, further modifications were made to develop the comprehensive anesthesia simulation environment by modifying a commercially available mannequin (Aebersold, 2016). Additional comprehensive anesthesia simulation environments were formed in the early 1990s to incorporate MedSim and Medical Education Technologies Inc. (Aebersold, 2016; Lateef, 2010). In 2000, Laerdal released a high-fidelity mannequin named SimMan that had many previous design features but at a lower purchase cost (Torsher, 2018). Today, simulation-based training has evolved to produce highly sophisticated mannequins that meet the objective learnings required by many national board and certification programs. As many medical specialty board programs see the importance of moving away from content-based education, numerous competency-based teaching programs have been initiated, as seen through simulation learning (Torsher, 2018). Since 2002, simulated-based assessment has been a component of the Israeli Anesthesia board certification due to its ability to measure distinctive strengths and weaknesses in the simulation exercises that could not be assessed in the written or oral examinations (Torsher, 2018). For CRNA programs, the governing body of nurse anesthesia program authorization is known as COA. It is a nationally recognized accrediting BEST PRACTICES FOR GENERAL INDUCTION SIMULATION 5 agency for nurse anesthesia programs that award doctoral-level degrees in the United States. Its mission is to grant public recognition to nursing anesthesia programs that meet nationally established academic quality (COA, 2020). Curriculum standards set forth by the COA require simulated clinical experiences to be incorporated into the program (COA, 2019). The importance of simulation-based education on SRNAs is to support them in preparing for completing the required clinical curriculum set by the COA. The clinical curriculum then provides SRNAs with real-life experiences that promote their development to become CRNAs. To successfully meet graduation requirements, the COA requires SRNAs to complete a minimum of 400 general anesthetics (COA, 2019). This requirement may not be satisfied by the use of simulated mannequins (COA, 2019). INACSLs standards demonstrate a commitment to improving patient care and best practices of healthcare education (INACSL, 2018). The importance of implementing INACSLs best practice for simulation helps SRNAs prepare to complete clinical curriculum requirements (INACSL Standards Committee, 2016). Therefore, incorporating INACSLs standards for best practice for simulation should be implemented to support SRNAs in completing GA induction competencies. At Marian University, current simulation practice evaluates SRNAs skill aptitudes to complete a GA induction using simulated intravenous drugs with or without inhalation agents before endotracheal tube placement through skill checkoffs. The explanation for completing a GA induction is discussed during anesthesia didactic with additional student resources under the interrelated simulation course. Specific resources available to SRNAs include anesthesia textbooks, YouTube videos on GA induction, lectured course information, and controlled simulation time. SRNAs are expected to prepare for GA induction competency with provided resources before attending skill checkoffs. Current SRNA checkoffs for GA are measured BEST PRACTICES FOR GENERAL INDUCTION SIMULATION 6 through a skill checklist created by Marian University faculty. Overlooked best practices from this simulation include a simulation prebriefing, a GA induction checklist that encompasses alternative medications, and a detailed video instruction on GA induction (Roh et al., 2018; Wiggins et al., 2018). Negative consequences for not having a more structured approach to simulation can lead to misrepresentation of information, further confusion on basic requirements, and inadvertent omission of information (INACSL, 2016). The inability to implement INACSL best practices may result in SRNA participants' failure to meet expected outcomes of simulation-based experiences that support COA clinical curriculum requirements (INACSL Standards Committee, 2016). Current simulation challenges include heightened anxiety from SRNA inexperience, uncoordinated simulation time, and unclear explanation of anesthesia-assisted tasks. Further potential consequences for not following INACSL standards may lead to further ambiguity, unintended outcomes, and skewed assessment and evaluation results (INACSL Standards Committee, 2016). By implementing a structured simulation strategy, future challenges introduced into this refined environment may minimize simulation variability and improve simulation reliability (Yauger et al., 2020). The approach to generating a consistent simulation design for GA induction aims to achieve an objective and dependable experience for each SRNA to perform GA skill competence that supports clinical requirements. Furthermore, standardized simulation practices promote effective learning methodologies that improve cognitive knowledge outcomes, enhanced clinical performance, and student self-confidence (Eyikara & Baykara, 2017). Problem Statement BEST PRACTICES FOR GENERAL INDUCTION SIMULATION 7 The COA supports the importance of sufficiently preparing SRNAs to complete GA inductions as it influences the completion of clinical experiences required to become a CRNA (COA, 2019). The inability to intubate or mask ventilate is one of the most feared complications, anesthesia respiratory events account for 27% of closed claims reports due to difficult intubations (Steadman et al., 2017). In a 2007 to 2014 review of ambulatory surgery center claims, injuries most common to anesthesia include endotracheal intubations at 29% (Ranum et al., 2017). The Operating Room noted that 67% of difficult intubations occur during GA induction (Steadman et al., 2017). This percentage includes individuals assessed to have a normal airway, and for this reason, it is unrealistic to expect SRNAs to succeed in first attempt intubations (Steadman et al., 2017). It is important to note that persistent attempts to complete endotracheal intubations commonly result in airway losses with brain damage or even death (Steadman et al., 2017). A sufficient understanding of this technique is undoubtedly critical beyond an academic setting as it directly impacts patients' health during the perioperative period and high-risk scenarios. Optimization of simulation practices for GA induction is crucial for SRNA academic success and preventing complications and improving patient outcomes. Organizational Gap Analysis of Project Site The current state in which SRNAs are expected to complete GA induction competency does not fully encompass the use of INACSLs best practice for simulation. SRNAs are expected to prepare for simulation through self-guided readings and class resources provided by Marian faculty. Additionally, students can practice GA induction during open simulation laboratory times provided by Marian faculty. SRNAs are not prebriefed on GA induction checkoffs before completing this competency. SRNAs are evaluated on the succession of GA induction through direct observation by Marian faculty either through the adjacent observation room or in the BEST PRACTICES FOR GENERAL INDUCTION SIMULATION 8 simulation laboratory. Successful completion of simulation experience is dependent on the successful demonstration of GA induction competency and understanding of its clinical application. Gaps identified in the current simulation design include systematic simulation standardization, prebriefing practices, and purposeful preparatory GA induction activities. As a result, this has led to suboptimal utilization of simulation resources and ineffective assessment of participants to achieve outlined objectives. Furthermore, due to the lack of a structured simulation design, SRNAs are disadvantaged from having an optimized simulation-based experience that supports their confidence and knowledge for completing GA induction simulation. A revised simulation design was created at Marian University using INACSL standards of best practice for simulation utilizing the National League for Nursing (NLN) Jeffries framework as the theoretical outline. Review of the Literature Search Methodology Databases to conduct this literature search included MEDLINE, CINAHL Plus, and ERIC. Additional scholarly articles were explored using Google Scholar. Several Boolean searches were completed using the following phrases: simulation AND CRNA; reliability AND healthcare simulation; reliability AND simulation best practice; competency confidence AND nursing simulation; simulation AND prebriefing. From this search, 161 records were identified for potential relevancy and applicability to this project. Specific exclusion criteria applied to these records included the lack of an abstract, irrelevancy to the research question, and inconclusive data to its research findings. As a result, 103 articles were excluded from the initial BEST PRACTICES FOR GENERAL INDUCTION SIMULATION 9 screening. The remaining 58 articles were closely reviewed for relevancy to the research question. Inclusion criteria consisted of articles in the English language, peer-reviewed, content related to the research question, and articles published within the last five years (2015-2020). Although the literature search yielded significant results about anesthesia and simulation, no such articles specifically discussed GA induction with simulation practices for SRNAs. Encouraged by the currently available literature on simulation benefits, a holistic search strategy was completed in finding articles to support INACSL's best practices. Selected articles were reviewed based on relevancy in prebriefing practices within a nursing simulation, improved selfconfidence and nursing simulation competency, and reliability in standardized simulations. The level of evidence ranged from level I to VII, with no discriminatory selection to the required level of evidence. Based on inclusion criteria, 18 articles were selected for a full review, and upon closer review, 13 articles were retained within Literature Review (Appendix A). Prebriefing Practices within Nursing Simulation According to the standards of best practices by INACSL, prebriefing is described as an informational or orientation period conducted before the start of a simulation experience (INACSL Standards Committee, 2016). Minimally, prebriefing should include the following practices: Providing students background information regarding the simulation; specify detailed instructions and expectations about the simulation-based experience; allotting sufficient time for participants to prepare for simulation; and giving an orientation to the simulation environment (INACSL Standards Committee, 2016). Prebriefing practices should consider the participant's level of knowledge, specific learning needs, and previous experiences (Page-Cutrara, 2015). BEST PRACTICES FOR GENERAL INDUCTION SIMULATION 10 The intent of integrating prebriefing into practice is to provide a safe learning environment and support participants in completing simulation goal competencies (Kim et al., 2017; INACSL Standards Committee, 2016). Simulations structured around prebriefing offer critical support in nursing performances and allows students the opportunity to find deeper meaning in their simulation experiences (Page-Cutrara, 2015). Prebriefing activities that incorporate the described elements could encourage reflection-before-action that further support the post-simulation process (Page-Cutrara, 2015; McDermott, 2017). For new learners who do not have the experience or skills to perform a required competency, imbedding prebriefing into simulation experience supports the participants metacognition (Page-Cutrara, 2015; Page-Cutrara & Turk, 2017; Chamberlin, 2017). Kim et al. (2017) reports that nursing students who receive prebriefing interventions demonstrate high practice flow and participant satisfaction scores. In another study that examined 76 baccalaureate nursing students, structured prebriefing effects on a students decision-making, skill performance, and overall awareness demonstrated statistical significance (p < 0.001) in all categories (Page-Cutrara & Turk, 2017). In reviewing a modified electronic Delphi method study design, a board of 59 Certified Simulation Educators reached an understanding (100%) that prebriefing was a crucial component for competency realization (McDermott, 2017). Similar findings were identified in a study of 281 undergraduate nursing students, yielding a higher team psychological safety and skill performance when provided prebriefing preparatory material (Roh et al., 2018). It is imperative that simulation-based learning encompass prebriefing practices to support learners in obtaining successful competency outcomes. Providing prior knowledge and familiarity before a simulation BEST PRACTICES FOR GENERAL INDUCTION SIMULATION 11 experience decreases nursing students anxiety and allows for full simulation participation (Sharoff, 2015). Improved Confidence and Competency with Prebriefing The DNP student completed a comprehensive review to identify commonalities among confidence, skill competency, and prebriefing practices. The DNP student did this to evaluate the simulation participant's perceptions and confidence to perform measured skills. According to Sharoff (2015), prebriefing practice enhanced 81 undergraduate nursing students' clinical judgment and overall confidence. Major themes from this mix-methods study included improvements in the participants' critical thinking ability and aptitude to complete simulated problem-solving (Sharoff, 2015). It is necessary to provide simulation participants with the supplies, space, and time to prepare for simulation-based experiences. In a quasi-experimental study, 207 junior and senior undergraduate nursing students exposed to prebriefing activities demonstrated an increased selfconfidence and clinical competency (Kim et al., 2017). Comparably, a study that included prebriefing practices reviewed 63 undergraduate nursing students ability to recognize and respond to deteriorating patients exhibited meaningful improvement in clinical confidence and competency (Goldsworthy et al., 2019). In efforts to promote simulation standardization, Wiggins et al. (2018) describe the use of pre-course didactic content used to assess 49 CRNAs knowledge, skill, and attitude in regional anesthesia administration. Outcomes from this simulation demonstrated substantial increases in CRNA knowledge and confidence (Wiggins et al., 2018). Additionally, a quasi-experimental design study compared outcomes among 119 undergraduate nursing students and identified higher perceptions of overall knowledge, self-confidence, and simulation efficacy with the use of BEST PRACTICES FOR GENERAL INDUCTION SIMULATION 12 prebriefing (Chamberlin, 2017). From these articles, it can be assumed that prebriefing activities will improve participants' education and simulation experience (Chamberlin, 2017; Kim et al., 2017; Sharoff, 2015; Wiggins et al., 2018). Reliability and Simulation Efficiencies through Standardization Reliability is the ability to consistently replicate a simulation in which participants are exposed to the same environmental conditions (Yauger et al., 2020). In a systematic review of both quantitative and qualitative studies, Yauger et al. (2020) reviewed 50 articles and evaluated the impact of creating a simulation with a consistent simulation design. Article findings suggest that having a consistent simulation design will eliminate setting errors that will negatively impact a learners competency and improve efficiency in the participant learnings through simulation standardization (Yauger et al., 2018). Implementing prebriefing in a standardized manner supports simulation outcomes that will enhance learner satisfaction, participation, and effectiveness of the simulation experience (Chamberlin, 2015). Due to its adaptability and organized approach to developing learning objectives, Staun et al. (2020) utilized the Objective Structured Clinical Experience as a tool to develop three simulation stations for 23 SRNAs in the preparation of their cardiac rotation. Due to the consistent simulation design, SRNAs were able to cycle through each workstation multiple times that supported productive learning, skill competency, and overall self-confidence (Staun et al., 2020). According to Aebersold et al. (2018), 50 reviewers agreed that simulation standardization was of particular importance in prebriefing in meeting simulation objectives and measurable outcomes. The use of thoughtful simulation design from these articles demonstrates the ability to promote skill acquisition that is reliable and efficacious to new learning. Theoretical Framework BEST PRACTICES FOR GENERAL INDUCTION SIMULATION 13 The theoretical framework used to support this DNP project is the NLN Jeffries framework (Appendix B). This framework was developed to define simulation education variables and is used as an organizational guide to include six core elements context, background, design, educational practices, simulation experience, and outcomes (Cowperthwait, 2020). The dissemination of this model was first done in 2005 and has undergone several iterations between 2005-2012 (Cowperthwait, 2020). The NLN Jeffries framework provides the necessary structure for developing a simulation design (Bottone-Post, 2016; Cowperthwait, 2020). The NLN Jeffries framework acknowledges interactions between the participants and facilitators of the simulation by establishing a trusting relationship (Cowperthwait, 2020). It supports simulation activities such as prebriefing, authenticating the simulation experience, simulation progression, and engagement (Cowperthwait, 2020; Jeffries et al., 2015). Concepts of particular interest to the research are elements that make up the simulation design include approaches to prebriefing/debriefing, conceptual fidelity, student role assignments, learning objectives, and simulation flow (Cowperthwait, 2020; Jeffries et al., 2015). This frameworks utilization allowed the DNP student to use the core elements of this framework as a guide to develop and evaluate a sophisticated simulation design (Jeffries et al., 2015). Specifically, the NLN Jeffries framework assisted in designing a simulation that incorporates INACSL's best practices to evaluate SRNA competency in GA induction (Cowperthwait, 2020). This framework is appropriate because it encompasses student preparation practices and facilitator simulation preplanning to define learning objectives that support SRNA knowledge and self-confidence (Cowperthwait, 2020; INACSL Standards Committee, 2016). Additionally, this framework allowed the DNP student to become more aware of participant variables that could negatively impact current or future simulations. The BEST PRACTICES FOR GENERAL INDUCTION SIMULATION 14 DNP student had the opportunity to identify simulation themes, understand learning gaps, and measure participant outcomes highlighted in the NLN Jeffries framework (Cowperthwait, 2020). Goals, Objectives, and Expected Outcomes This project had two specific aims: 1.) Improve SRNA knowledge on GA induction and 2.) Improve SRNA self-confidence when completing GA induction competency. This project aimed to assist SRNAs who are exposed to prebriefing practices to have more knowledge and self-confidence on GA inductions for anesthesia practices. Expected outcomes for this project assumed that SRNAs who are provided prebriefing material will have enhanced knowledge and self-confidence when performing GA inductions with minimal to no errors. An effective simulation design used the NLN Jeffries framework and INACSL best practices to facilitate consistent outcomes that improved overall SRNA understandings and certainty when completing GA induction. According to the INACSL Standards Committee (2016), simulation-based experiences should begin with prebriefing. Under the NLN Jeffries framework's guidance, the desired state for simulation-based experiences includes creating an environment of integrity, trust, and respect (INACSL Standards Committee, 2016). The integration of these concepts supports INACSL standards of practice and creates a learning environment that is psychologically safe and conducive to SRNA learning (INACSL Standards Committee, 2016; Cowperthwait, 2020). The core elements outlined in the NLN Jeffries framework were appropriate in creating a new simulation-based experience at Marian University. It includes strategies for prebriefing and preparatory material that highlights essential concepts seen through INACSL standards of best practice (Cowperthwait, 2020). BEST PRACTICES FOR GENERAL INDUCTION SIMULATION 15 Project Setting Marian University is located in the metropolitan area of Indianapolis, Indiana. During the time this project was executed, Marian University had three concurrent classes enrolled in the nurse anesthesia program. Upon admission, SRNAs are expected to progress as a unit with class sizes ranging from 22 to 24 students. The simulation laboratory is available to all students who wish to practice anesthesia skills during specific open hours. Open simulation times are currently managed by Marian faculty. There is not a set requirement for SRNAs to attend simulation laboratory except for completing skill checkoffs. Attending the simulation laboratory is highly encouraged during open hours. The number of simulations completed in Marian's anesthesia program is variable and dependent on each course instructor. Specific anesthesia simulations are created based on skills required to be completed in the real-life clinical setting. The simulation room itself uses a Computer-Aided Engineering (CAE) Human Patient Simulator (HPS) to complete GA induction checkoffs. The simulation facilitator can control the HPS through a computer station adjacent to the central simulation laboratory (simulation observation room). This HPS is specifically designed to support anesthesia practices and interface with the patient monitors, ventilator, and students in the simulation laboratory (Computer-Aided Engineering (CAE) Human Patient Simulator (HPS), 2018). Additional equipment found in this laboratory includes the anesthesia machine (Drager Apollo) and simulated inhalation agents used for GA induction. Specific anesthesia equipment unique to GA induction includes the use of an endotracheal tube, laryngoscope, simulated induction syringes, monitoring equipment, face mask, medical tape, and intravenous fluids. Students are given all the necessary supplies to complete GA induction competency, except that SRNAs are expected to bring their stethoscopes. BEST PRACTICES FOR GENERAL INDUCTION SIMULATION 16 Project Sample A total of 24 SRNAs in their third semester were included in this project. This convenience sample group's inclusion criteria required current enrollment in the first Anesthesia Principles didactic with the associated simulation course. Exclusion criteria included participants who did not meet this requirement. Students who failed to comply with the verbal agreement for completing necessary prebriefing simulation activities were not included in this project but still completed the simulation per course expectations. Project Design A new simulation design was developed using INACSLs prebriefing practices and NLN Jefferies framework with preparatory material developed by the DNP student. This project used a quality improvement with a quantitative evaluation approach to assess and evaluate SRNA knowledge barriers and self-confidence for GA induction. Preexisting quantitative tools were modified and used as part of data collection procedures. Methods This project was facilitated by the DNP student with oversight from one CRNA faculty member. This project was evaluated by randomizing SRNAs into two groups: The experimental group (n=12) and the control group (n=12). The experimental group was expected to review a detailed competency checklist and complete an instructional video on GA induction before attending the simulation. This group was given a brief orientation to the simulation environment with the DNP student outlining the competency objectives, knowledge expectations, and simulation limitations. The DNP student confirmed that the experimental group completed all BEST PRACTICES FOR GENERAL INDUCTION SIMULATION 17 preparatory activities before starting the simulation. The control group was given the current simulation design that may have encompassed aspects described in the experimental group. Before starting the simulation, a verbal attestation that the experimental group has completed all prebriefing activities was required. SRNAs who had not fulfilled this activity were asked to complete missed prebriefing material before starting the simulation. SRNAs who failed or declined to follow these instructions were excluded from this project but still completed the simulation per course expectations. The intent of confirming that the experimental group had completed the assigned prebriefing material strengthened the validity and actions represented by the participants who completed the preparatory material. SRNAs who were a part of the control group were provided the same preparatory material as the experimental group after data collection was completed. Student participation and results for this quality improvement project were not affected by the pass or failure of this anesthesia course. The simulation experience for each SRNA was completed with one participant completing the GA induction competency at a time. 24 SRNAs signed up for 30-minute time slots to complete GA induction checkoffs. No student was permitted to observe another when completing the simulation to prevent bias. As part of the verbal attestation for student academic honesty, students were reminded to not share information with others about the simulation-based experience. Measurement Instruments Modified Student Satisfaction and Self-Confidence in Learning Survey The SSSL survey is a 13-item instrument designed by the NLN that measures student satisfaction and self-confidence in instructions with simulation (Zapko et al., 2018). Within the instrument itself, it contains two subscales that measure satisfaction with the current learning (5 BEST PRACTICES FOR GENERAL INDUCTION SIMULATION 18 items) and self-confidence in learning (8-items) (Unver et al., 2017). Responses are rated using a five-point Likert scale that ranges from 1 (strongly disagree) to 5 (strongly agree) (Unver et al., 2017). Supported by Cronbach's alpha measurement of reliability, this tool received a value of 0.94 for satisfaction and 0.87 for self-confidence (Bottone-Post, 2016; National League of Nursing, 2005; Unver et al., 2017). Higher results scored from this survey indicate higher satisfaction and self-confidence (Unver et al., 2017). For this project, the SSSL survey was modified to measure student self-confidence variables specific to SRNA and GA induction competency. Quantitative data was obtained through the pretest/posttest NLN survey (Appendix C). Simulation Posttest Knowledge Assessment SRNA learning is an essential outcome as it assesses changes in student knowledge, skills, and attitudes of a simulation (Jeffries et al., 2015). To measure SRNA knowledge on GA induction, a posttest questionnaire was used to assess knowledge of this competency. This knowledge assessment tool consists of five questions that focus solely on the GA induction technique and interventional concepts. This five-question knowledge assessment tool was generated by the DNP student using the GA induction checklist as a framework to develop this assessment tool. The questionnaire was designed to measure SRNA knowledge of how to perform GA induction safely in a clinical setting. The generated test questions underwent face and content validity assessment through the feedback and recommendations of two GA induction experts. Quantitative data was gathered through posttest knowledge assessment results (Appendix D). GA Induction Checklist and Duration BEST PRACTICES FOR GENERAL INDUCTION SIMULATION 19 The GA induction checklist (Appendix E), provided by Marian University faculty, was used for this project as a rubric to test SRNAs succession of this competency. Additionally, this checklist captured the number of errors or omissions by SRNAs during the simulated experience. The DNP student also compared durations among the experimental and control groups to complete the GA induction checklist. The duration in which the DNP student evaluated the time for the SRNA to complete the GA induction competency was recorded when Marian faculty confirmed the SRNA's readiness to begin and end their evaluation. Quantitative data was monitored by the DNP student through observed errors and omissions from the GA induction checklist. Additionally, the duration for each SRNA to complete skill competency was recorded. Data Collection Procedures Simulation Experience and Facilitation Before starting the simulation, both the experimental and control groups were asked to complete the same pretest SSSL survey. The pretest survey was provided to SRNAs by the DNP student through email using Qualtrics and reviewed for completion. Once completed, the DNP student continued with the GA induction simulation. During the simulation experience, SRNAs were evaluated by the DNP student and Marian faculty using the same rubric for GA induction for both the experimental and control groups. Specifically, the DNP student and Marian faculty assessed the number of errors or omissions using the GA induction checklist. Any deviations from this competency checklist were tallied and compared against these two groups. For the experimental group, the DNP student and Marian faculty did not intervene or correct any departures from the GA induction checklist until the simulation was over. For the control group, Marian faculty was able to complete GA induction competency checkoffs as part of routine evaluation. BEST PRACTICES FOR GENERAL INDUCTION SIMULATION 20 The overall time to complete GA induction competency was recorded and compared against these two groups using an apple iPhone stopwatch. Marian faculty verbally announced the start and stop time through the participants readiness to complete this simulation. After the SRNA completed the GA induction simulation, the same posttest SSSL survey and posttest knowledge assessment were provided to each SRNA via email link through Qualtrics. The DNP student reviewed the posttest SSSL survey and posttest knowledge assessment responses through Qualtrics. From there, the DNP student confirmed all surveys and posttest knowledge assessments were completed by each SRNA before leaving the simulation. Ethical Considerations/Protection of Human Subjects The Marian Internal Review Board (IRB) deemed this project as exempt before initiating the DNP project. The IRB approval process ensures that participants are provided with sufficient information and complete comprehension of this project before participating. SRNA participation was entirely voluntary and without any foreseeable risks. There were no financial gains or incentives for participating in this simulation experience. Participants were able to withdrawal from this DNP project at any point participants deemed necessary. As part of this DNP project, the privacy of student educational records was guided through the Family Educational Rights and Privacy Act (FERPA) by the DNP student (Gilliard, 2020). Additionally, the DNP student utilized the principles founded in the Standards of Care when conducting this project at Marian University (HG.org Legal Resources, n.d.). Data Analysis The purpose of this quality improvement project was to determine if prebriefing practices improved SRNA self-confidence and knowledge for GA induction competency. All surveys were completed electronically using Qualtrics and inputted into the International Business Machines BEST PRACTICES FOR GENERAL INDUCTION SIMULATION 21 (IBM) Statistical Package for the Social Sciences (SPSS) software (Version 28). For all categorical and numerical data, the DNP student placed this data into tables for all SSSL survey responses and reported posttest knowledge assessment results for both groups. For data that was a continuous level of measurement, additional descriptive statistics were calculated included measures of frequency, measures of central tendency, and measures of variability. In evaluating student self-confidence in completing GA induction, individual responses were summed and evaluated for both the pretest and posttest surveys. Frequencies and percentages were reported for these 13-question surveys as they are considered categorical variables. The statistical test used to compare two related samples and assess if population ranks differed was completed using the Wilcoxon Signed Rank test (Heavy, 2015). In determining the statistical difference in the SSSL survey responses for the control and experimental group, an independent t-test was used. For the posttest knowledge assessment, the final score of this 5-item questionnaire yielded continuous variables as it was converted to a percentage before completing the independent t-test. The purpose of this test is to determine if there was a statistical difference between the two groups (Heavy, 2015). Additionally, the time to complete GA induction competency followed the same statistical procedures and was completed using the independent ttest to assess this continuous variable. In evaluating the number of omissions or errors, the Chisquare test was be used to compare differences for the experimental and control groups. Results Prior Student Knowledge on General Anesthesia Induction SRNAs who participated in this DNP project did not have prior experience in the clinical setting and were limited to only the simulation laboratory. All SRNAs did have prior exposure to BEST PRACTICES FOR GENERAL INDUCTION SIMULATION 22 the simulation room and were familiar with interacting with the CAE HPS. All simulation participants had less than one year of nurse anesthesia didactic completed during the execution of this project. None of the SRNA participants reported having completed a GA induction on an actual patient. Student Demographics Of the 24 participants, there were a total of 16 females (67.00%) and 8 males (33.00%) who completed the GA induction competency. The primary age range of this group was reported to be approximately 26-30 years of age (58.33%). Additionally, the average years of nursing experience were reported to be between 3-6 years (45.83%) for simulation participants. Student Participation in the Simulation In this DNP project, 24 (100%) participated in this quality improvement project including the completion of the SSSL pretest survey, post-knowledge test, and SSSL posttest survey. In asking the experimental group for the completion of all prebriefing activities 11 (92%) students stated completion of all prebriefing activities. One student stated they did not watch the instructional video provided by the DNP student but watched previous videos on how to complete GA induction provided by the course instructor. For this reason, this student was not excluded as part of this project. All SRNA participants completed the GA induction checkoffs under the specific requirements outlined in Appendix E and were evaluated by only the course instructor and not the DNP student. Student Self-Confidence Pretest to Posttest Results within the Groups BEST PRACTICES FOR GENERAL INDUCTION SIMULATION 23 In reviewing the pretest SSSL frequencies within the control group, pretest results showed higher selections for undecided and agree for the pretest (selection frequency 75.00%) and higher scores for agree and strongly agree in the posttest test (selection frequency 94.87%). The overall difference in this selection increased by 59 responses for agree and strongly agree in improvement for student self-confidence within the control group. In performing the Wilcoxon Signed Rank test for this group, it was determined to be not statistically significant (p > 0.05). Pretest responses for the experimental group demonstrated higher scores in disagree and agree (selection frequency 67.31%) and higher scores in agree and strongly agree (selection frequency 78.85%) for the posttest. The difference in this groups selection for agree and strongly agree increased by 39 responses for improvement in student self-confidence for prebriefing practices in the experimental group. Performing the Wilcoxon Signed Rank test, this also was not statistically significant (p > 0.05). Comparing Pretest Results between the Two Groups In evaluating the pretest results between the two groups, the control group had higher scores for undecided and agree (selection frequency 75.00%) in comparison to the experimental group that had higher scores in disagree and agree (selection frequency 67.13%). The response rate for agree and strongly agree between these two groups resulted in a difference of five responses, which favored the simulation experience within the control group where prebriefing was not used. Performing the Wilcoxon Signed Rank test, this finding was determined to be not statistically significant (p > 0.05). Comparing Posttest Results between the Two Groups For the posttest survey results, the control group demonstrated significantly higher results in agree and strongly agree (selection frequency 94.87%) for a total response value of 148 BEST PRACTICES FOR GENERAL INDUCTION SIMULATION 24 responses. The experimental group also showed higher scores in agree and strongly agree (selection frequency 78.85%) for a total response value of 123. The difference between these two groups resulted in a value of 25 which favored the simulation experience within the control group. Similar findings, using the Wilcoxon signed-rank test, resulted in a value of p > 0.05 that was not statistically significant. Analysis of each Question and Subtheme The range of mean scores for satisfaction items ranged from 3.17 to 3.67 for the pretest SSSL survey and 4.42 to 4.58 for the posttest SSSL survey of the control group. All satisfaction item mean scores were higher in the posttest group. The satisfaction item means scores were summed, with 25 being the highest possible mean summed score. The control group for the posttest showed higher satisfaction results at 22.66 compared to the pretest value of 16.92. Scores indicated that the control group was more satisfied in the posttest result in comparison to the pretest result of the same group. The participants were more satisfied after completing their simulation on GA induction competency. This proved to be statistically significant (p = 0.043) in the summation of satisfaction scores. See Table 1.1 for satisfaction results. Table 1.1 SSSL satisfaction with current learning pretest vs. posttest for the control group Item Satisfaction 1 Satisfaction 2 Satisfaction 3 Satisfaction 4 Satisfaction 5 Summed satisfaction Control group pretest mean (SD) 3.25 (0.87) 3.17 (0.83) 3.33 (0.65) 3.50 (0.67) 3.67 (1.15) 16.92 (0.20) Control group posttest mean (SD) 4.42 (0.67) 4.50 (0.67) 4.58 (0.51) 4.58 (0.90) 4.58 (0.90) 22.66 (0.07) Mean difference p-Value +1.17 +1.33 +1.25 +1.08 +0.91 +5.74 0.001* 0.004* 0.002* 0.005* 0.031* 0.043* *Note. Using the Wilcoxon Signed Rank test, a statistically significant change at p <0.05. BEST PRACTICES FOR GENERAL INDUCTION SIMULATION 25 The range of mean scores for the 8-item self-confidence questions ranged from 3.25 to 4.42 for the pretest and 4.33 to 4.67 for the posttest of the control group. All of the posttest results scored higher in comparison to the pretest results. The highest possible summed score for this 8-item self-confidence survey was 40. Posttest summative scores indicated higher scores of self-confidence in learning after the simulation (35.75) than before the simulation (29.50). This finding was statistically significant with p = 0.011. See Table 1.2 for the self-confidence results of the control group. Table 1.2 SSSL self-confidence in learning pretest vs. posttest for the control group Item Confidence 1 Confidence 2 Confidence 3 Confidence 4 Confidence 5 Confidence 6 Confidence 7 Confidence 8 Summed confidence Control group pretest mean (SD) Control group posttest mean (SD) Mean difference p-Value 3.25 (0.87) 3.42 (0.90) 3.33 (0.79) 3.33 (0.89) 4.42 (0.51) 4.17 (0.83) 3.83 (0.83) 3.75 (0.87) 29.50 (0.43) 4.42 (0.67) 4.42 (0.67) 4.50 (0.52) 4.33 (0.65) 4.58 (0.51) 4.67 (0.49) 4.33 (0.65) 4.50 (0.52) 35.75 (0.19) +1.17 +1.00 +1.17 +1.00 +0.16 +0.50 +0.50 +0.75 +6.25 0.001* 0.008* 0.003* 0.008* 0.157 0.034* 0.063 0.033* 0.011* *Note. Using the Wilcoxon Signed Rank test, a statistically significant change at p <0.05. The mean score range for the pretest in the experimental group ranged from 2.92 to 3.42. By comparison, the posttest in this same group ranged from 3.67 to 3.92. All satisfaction scores yielded higher scores in the posttest group with two statistically significant items. Findings were similar to that of the control in which summative scores in the posttest (19.09) were higher than pretest (15.50) for the experimental group. Participants had higher scores of satisfaction in the current learning after the simulation was completed than before starting the simulation. This BEST PRACTICES FOR GENERAL INDUCTION SIMULATION 26 finding was statistically significant (p = 0.043). See Table 2.1 for SSSL satisfaction results for the experimental group. Table 2.1 SSSL satisfaction with current learning pretest vs. posttest for the experimental group Item Satisfaction 1 Satisfaction 2 Satisfaction 3 Satisfaction 4 Satisfaction 5 Summed satisfaction Experimental group pretest mean (SD) 3.00 (1.13) 2.92 (1.08) 3.08 (1.24) 3.42 (0.90) 3.08 (1.08) 15.50 (0.19) Experimental group posttest mean (SD) 3.92 (0.90) 3.75 (0.97) 3.92 (0.67) 3.83 (1.03) 3.67 (1.15) 19.09 (0.11) Mean difference p-Value +0.92 +0.83 +0.84 +0.41 +0.59 +3.59 0.048* 0.033* 0.090 0.160 0.150 0.043* *Note. Using the Wilcoxon Signed Rank test, a statistically significant change at p <0.05. The range of mean scores for the 8-item self-confidence items were 3.17 to 4.08 for the pretest and 3.67 to 4.08 for the posttest in the experimental group. All of the scores in the posttest yielded higher values or a net value of zero in comparison to the pretest scores. One item resulted in a statistically significant individual p-value. Additionally, the summative selfconfidence scored higher in the posttest results (31.25) than the pretest (28.25). Participants in the experimental group showed higher self-confidence in learning after completing the simulation than before starting the simulation. This finding was statistically significant for a pvalue of 0.018. See Table 2.2 for self-confidence results in the experimental group. Table 2.2 SSSL self-confidence in learning pretest vs. posttest for the experimental group Item Confidence 1 Confidence 2 Experimental group pretest mean (SD) Experimental group posttest mean (SD) Mean difference p-Value 3.17 (1.03) 3.75 (0.97) 3.83 (0.58) 3.92 (0.90) +0.66 +0.17 0.046* 0.914 BEST PRACTICES FOR GENERAL INDUCTION SIMULATION Confidence 3 Confidence 4 Confidence 5 Confidence 6 Confidence 7 Confidence 8 Summed confidence 3.42 (0.90) 3.33 (0.98) 3.42 (1.44) 4.08 (0.67) 3.50 (1.00) 3.58 (1.00) 28.25 (0.28) 3.92 (0.51) 3.75 (0.97) 3.67 (1.23) 4.08 (0.51) 4.08 (0.29) 4.00 (0.60) 31.25 (0.15) +0.50 +0.42 +0.25 0.00 +0.58 +0.42 +3.00 27 0.063 0.357 0.396 1.00 0.059 0.393 0.018* *Note. Using the Wilcoxon Signed Rank test, a statistically significant change at p <0.05. The satisfaction range of mean scores for pretest in the control group ranged from 3.17 to 3.67. The satisfaction range of mean scores for the experimental group resulted in lower values by comparison for a range of 2.92 to 3.42. The highest possible summative satisfaction score was 25 in which the control group resulted in larger quantities (16.92) in comparison to the experimental group (15.50). Participants involved in the control group had higher scores of satisfaction in the current learning than participants in the experimental group who were provided prebriefing material. Using an independent t-test, this finding was statistically significant (p = 0.049). See Table 3.1 for results on pretest satisfaction for the control and experimental group. Table 3.1 Pretest SSSL satisfaction with current learning control group vs. experimental group Item Satisfaction 1 Satisfaction 2 Satisfaction 3 Satisfaction 4 Satisfaction 5 Summed satisfaction Control group pretest mean (SD) 3.26 (0.87) 3.17 (0.83) 3.33 (0.65) 3.50 (0.67) 3.67 (1.15) 16.92 (0.20) Experimental group pretest mean (SD) 3.00 (1.13) 2.92 (1.08) 3.08 (1.24) 3.42 (0.90) 3.08 (1.08) 15.50 (0.19) Mean difference p-Value -0.26 -0.25 -0.25 -0.08 -0.59 -1.42 0.549 0.533 0.543 0.800 0.215 0.049* *Note. Using the Independent T-test, a statistically significant change at p <0.05. BEST PRACTICES FOR GENERAL INDUCTION SIMULATION 28 The self-confidence range of mean scores for the pretest in the control group was 3.25 to 4.42. The self-confidence range of mean scores for the experimental group was 3.17 to 4.08. The summative scores resulted in higher scores in the control group (29.50) than in the experimental group (28.25). Participants involved in the control group had higher scores of self-confidence in learning with one item showing statistical significance. However, there was not a statistically significant difference in the summed self-confidence between these two groups. See Table 3.2 for results on pretest self-confidence for the control and experimental group. Table 3.2 Pretest SSSL self-confidence in learning control group vs. experimental group Item Confidence 1 Confidence 2 Confidence 3 Confidence 4 Confidence 5 Confidence 6 Confidence 7 Confidence 8 Summed confidence Control group pretest mean (SD) Experimental group pretest mean (SD) Mean difference p-Value 3.25 (0.87) 3.42 (0.90) 3.33 (0.79) 3.33 (0.89) 4.42 (0.51) 4.17 (0.83) 3.83 (0.83) 3.75 (0.87) 29.50 (0.43) 3.17 (1.03) 3.75 (0.97) 3.42 (0.90) 3.33 (0.98) 3.42 (1.44) 4.08 (0.67) 3.50 (1.00) 3.58 (1.00) 28.25 (0.28) -0.08 +0.33 +0.09 0.00 -1.00 -0.09 -0.33 -0.17 -1.25 0.832 0.391 0.811 1.00 0.034* 0.790 0.385 0.666 0.406 *Note. Using the Independent T-test, a statistically significant change at p <0.05. Mean scores for the control group ranged from 4.42 to 4.58 in the control group and from 3.67 to 3.92 in the experimental group. All individual mean scores were higher in the control group with three items showing statistical significance. The summed satisfaction scores for the control group resulted in a total of 22.66 and 19.09 for the experimental group. Results demonstrated higher scores of satisfaction in the control group than in the experimental group. This was a statistically significant difference (p < 0.001) in that the control group had higher BEST PRACTICES FOR GENERAL INDUCTION SIMULATION 29 levels of satisfaction in learning than the experimental group. See Table 4.1 for satisfaction results. Table 4.1 Posttest SSSL satisfaction with current learning control group vs. experimental group Item Satisfaction 1 Satisfaction 2 Satisfaction 3 Satisfaction 4 Satisfaction 5 Summed satisfaction Control group posttest mean (SD) 4.42 (0.67) 4.50 (0.67) 4.58 (0.51) 4.58 (0.90) 4.58 (0.90) 22.66 (0.07) Experimental group posttest mean (SD) 3.92 (0.90) 3.75 (0.97) 3.92 (0.67) 3.83 (1.03) 3.67 (1.15) 19.09 (0.11) Mean difference p-Value -0.50 -0.75 -0.66 -0.75 -0.91 -3.57 0.137 0.038* 0.012* 0.071 0.042* <0.001* *Note. Using the Independent T-test, a statistically significant change at p <0.05. The range of mean scores for the 8-item self-confidence survey was 4.33 to 4.67 in the control group and 3.67 to 4.08 in the experimental group. All individual mean item scores were higher in the control group, with four items showing a statistically significant difference. Also, the total summed scores were higher in the control group (35.75) than in the experimental group (31.25). Participants in the control group showed higher scores of self-confidence in the GA induction simulation learning than in the experimental group. This was a statistically significant difference (p < 0.001) in that the control group had higher levels of self-confidence after completing their simulation on GA induction than the experimental group. Table 4.2 Posttest SSSL self-confidence in learning control group vs. experimental group Item Confidence 1 Confidence 2 Confidence 3 Control group posttest mean (SD) Experimental group posttest mean (SD) Mean difference p-Value 4.42 (0.67) 4.42 (0.67) 4.50 (0.52) 3.83 (0.58) 3.92 (0.90) 3.92 (0.51) -0.59 -0.50 -0.58 0.032* 0.137 0.012* BEST PRACTICES FOR GENERAL INDUCTION SIMULATION Confidence 4 Confidence 5 Confidence 6 Confidence 7 Confidence 8 Summed confidence 4.33 (0.65) 4.58 (0.51) 4.67 (0.49) 4.33 (0.65) 4.50 (0.52) 35.75 (0.19) 3.75 (0.97) 3.67 (1.23) 4.08 (0.51) 4.08 (0.29) 4.00 (0.60) 31.25 (0.15) -0.58 -0.91 -0.59 -0.25 -0.50 -4.50 30 0.097 0.026* 0.010 0.237 0.041* <0.001* *Note. Using Independent T-test, a statistically significant change at p <0.05. Student Knowledge Students were given a post-knowledge test and scores were compared between the control and experimental group. The control groups post-knowledge assessment scores ranged from 40-100% with a mean of 76.67% (SD 16.70). The experimental groups post-knowledge assessment scores ranged from 60-100% with a mean of 83.33% (SD 11.55). While the experimental group demonstrated a mean difference of +6.66%, the result was not statistically significant (t = -1.14, p = 0.27). The most missed questions for the control group included knowing to check for the loss of an eyelid reflex after administering propofol (question 3) and appropriate endotracheal tube depth for an average adult (question 5). The experimental groups commonly missed posttest knowledge questions included knowing the duration in which a paralytic agent takes maximum effect (question 2) and knowing the best indicator for correct endotracheal tube placement (question 4). Recorded Omission, Errors, and Time Elapsed During the simulation, the DNP student observed each SRNA complete their simulation and monitored for any deviations (i.e., omission and/or errors) from the GA induction checklist. The time elapsed was also recorded and compared against these two groups. The control groups total number of deviations from the GA induction checklist was 22 and seven for the experimental group for a total of 29 deviations between the two groups. The two most frequently BEST PRACTICES FOR GENERAL INDUCTION SIMULATION 31 missed items from the GA induction checklist in the control group were to check the eyelid reflex and to turn on the vaporizer. For the experimental group, the two most frequently missed items included verification of ventilation after checking the eyelid reflex and assessing neuromuscular function (no twitches). Despite the experimental group having 15 deviations less than the control group, a Chi-square test determined that this finding was not statistically significant (p = 0.484). The time elapsed to complete the GA induction competency for the control group had a mean score of 11 minutes and 53 seconds (SD 3.68). The experimental group resulted in a mean score of 11 minutes and 12 seconds (SD 2.93). Participants in the experimental group completed the GA induction sooner than those in the control group. This finding was determined to not be statistically significant (p = 0.77). Discussion In this quality improvement project, INACSL best practices and guidelines were used in creating a new simulation design on GA induction. All participants reported their self-confidence and demonstrated their knowledge to perform GA induction based on the provided preparatory material specific to each group. For the experimental group, additional prebriefing material on GA induction included an outline of the simulation objectives, a review of the simulation expectations, stated simulation limitations, and a detailed GA induction instructional video. All participants were evaluated using a posttest knowledge assessment, pretest/posttest SSSL surveys, observed deviations, and recorded time to complete this skill competency. Results demonstrated that participants in the control group had higher scores of satisfaction and self-confidence in comparison to the experimental group for posttest survey BEST PRACTICES FOR GENERAL INDUCTION SIMULATION 32 responses (p <0.001). Focusing on the control group, summative mean differences improved in both satisfaction (+5.74) and self-confidence (+6.25) which proved to be statistically significant (p < 0.05). Prior research has shown students perceptions of overall simulation effectiveness, learning, and self-confidence are significantly higher when using prebriefing practices (Chamberlain, 2017). Prebriefing is an essential component for learners success as it aims to achieve simulation competency outcomes (Kim et al., 2017). In this DNP project, this finding did not correlate with other completed studies on this use of prebriefing practice for simulationbased learning. Evaluating the pretest to posttest results for the experimental group, there was a statistical significance for improved satisfaction and self-confidence. Summative mean scores resulted in a +3.59 improvement for satisfaction and +3.00 improvement for self-confidence. While this summative mean score was not as great as the control group's results, this finding does correlate with other research in stating that completion of simulation competency has improved scores in self-confidence and knowledge in those exposed to prebriefing practices (Kim et al., 2017). Future studies are needed to provide additional insight into the full benefits of prebriefing practices for simulation-based learning. Although scores were higher in satisfaction and self-confidence in the control group, posttest knowledge assessment scores did show a +6.66% improvement in the experimental group. Despite this finding not being statistically significant, other literature states that those who are exposed to prebriefing practice demonstrated higher scores in overall performance in simulation-based education (Roh et al., 2018). Research has found significant increases in knowledge, skills performance, and self-confidence when integrating prebriefing practices into a simulation design (Goldsworthy et al., 2019). BEST PRACTICES FOR GENERAL INDUCTION SIMULATION 33 The time elapsed to complete the simulation competency improved by a mean difference score of +0.41 in the experimental group. The observed deviations from the rubric checklist also improved in the experimental group by 51.72%. Despite these two improvements within the experimental group, these findings were determined to be not statistically significant. Findings from this process improvement project are suggestive that participants involved with prebriefing practices may have been influenced by this intervention and final results. Literature indicates significant improvements in learning engagement and competency completion in students who participated in prebriefing activities (Chamberlin, 2017). Literature findings align with project results in that the use of prebriefing practices increases perceptions of self-confidence and learning outcomes (Chamberlin, 2017; Goldsworthy et al., 2019). The use of prebriefing practices may affect SRNA confidence and knowledge when completing GA induction competency. Receiving information about an upcoming simulation along with the execution of an improved simulation process may contribute to the concept of psychological safety in that participants may have improved self-confidence and knowledge. Therefore, implementing INACSLs best practices for prebriefing can be a useful adjunct in improving areas of self-confidence and anesthesia knowledge in the simulation environment. Further study is required on the different types and exposure of prebriefing strategies on learning outcomes of knowledge and self-confidence for simulation-based learning. A repeated study with an increased number of participants should be considered. Strengths and Limitations Strengths of this project included the creation of a well-structured simulation for GA induction, generation of GA induction resources for future SRNAs, the introduction of BEST PRACTICES FOR GENERAL INDUCTION SIMULATION 34 INACSL's best practices for prebriefing in the simulation environment, and positive feedback from students on the GA induction instructional video. A limitation of this project included having a small sample size, unmonitored open simulation lab time, uncontrolled communication between students on the GA induction competency, control group utilization of outside resources to prepare for checkoffs, and SRNA distraction of upcoming exam that may have affected full student engagement. Additionally, six other DNP students completed their DNP project on simulation improvements that may have led to participant fatigue and affected project results. In this instance, it is difficult to demonstrate that this quality improvement intervention may have influenced student confidence and knowledge for GA induction. Future Project Recommendations In this project, students reported overall higher scores in the control group that did not involve the use of prebriefing practices. During the execution of this DNP project, participants were simultaneously preparing for an upcoming exam that may have affected results. To prevent outside influences on future simulation improvement projects, it is recommended that future studies involving prebriefing material be condensed. In doing this, it would hopefully alleviate future participant stress and allow for full engagement in future quality improvement interventions. If future DNP projects should be completed on simulation improvements involving a single SRNA cohort, it is recommended that multiple DNP projects be limited to prevent participant fatigue. Conclusion Prebriefing is required to support learners by supplementing additional resources to support limited knowledge and experiences before a simulation. The current simulation design BEST PRACTICES FOR GENERAL INDUCTION SIMULATION 35 does not include a standardized method for simulation execution, use of prebriefing practices, or use of purposeful preparatory GA induction activities. As a result, this may negatively impact the knowledge and self-confidence of SRNAs completing GA induction competency. This projects results revealed that SRNAs in the experimental group revealed improved scores in their posttest knowledge test and overall performance in GA induction competency though these results were not statistically significant. In reviewing the results generated by the SSSL survey, the control group had a higher individual self-confidence rating in both the pretest and posttest when compared to the experimental group. There was a statistical significance between the control and experimental group for posttest results for self-confidence but not for pretest results for self-confidence. As a result, the DNP student determined this project results to be inconclusive in determining if the use of a standardized simulation training video with detailed instructions affects their knowledge and confidence of anesthesia induction technique, compared to current simulation practice. It is important that simulation-based learning design and the implementation of learning orientation use prebriefing activities to foster and improve knowledge and self-confidence for SRNAs. Further exploration in the use of prebriefing in simulation-based learning is needed to ensure SRNAs are given effective simulation experiences that promote self-confidence and knowledge that can be transferred to a clinical setting and their practice. BEST PRACTICES FOR GENERAL INDUCTION SIMULATION 36 References Aebersold, M. (2016). The history of simulation and its impact on the future. AACN Advanced Critical Care, 27(1), 5661. https://doi.org/10.4037/aacnacc2016436 Aebersold, M., Mariani, B., & Cherara, L. (2018). Do experts and novices agree? A qualitative review of the 2016 INACSL standards of best practice: SimulationSM reviewer feedback. Clinical Simulation in Nursing, 25, 15. https://doi.org/10.1016/j.ecns.2018.09.007 American Association of Nurse Anesthetists. (2019, August 8). Certified registered nurse anesthetists fact sheet. https://www.aana.com/membership/become-a-crna/crna-fact-sheet Bottone-Post, C. (2016) Postpartum hemorrhage (PPH) simulation project [DNP Project, Regis University]. All Regis University Theses. https://epublications.regis.edu/cgi/viewcontent.cgi?article=1740&context=theses Brown, D. K. (2017). Simulation before clinical practice: The educational advantages. Audiology Today, 29(5), 1624. CAE HPS. (2018). Modeled patient physiology you can count on. https://caehealthcare.com Chamberlain, J. (2015). Prebriefing in nursing simulation: A concept analysis using Rodgers methodology. Clinical Simulation in Nursing, 11(7), 318322. https://doi.org/10.1016/j.ecns.2015.05.003 Chamberlain, J. (2017). The impact of simulation prebriefing on perceptions of overall effectiveness, learning, and self-confidence in nursing students. Nursing Education Perspectives, 38(3), 119125. https://doi.org/10.1097/01.NEP.0000000000000135 Council on Accreditation of Nurse Anesthesia Educational Programs (COA). (2020). About COA. https://www.coacrna.org/about-coa/ BEST PRACTICES FOR GENERAL INDUCTION SIMULATION 37 Council on Accreditation of Nurse Anesthesia Educational Programs (COA). (2019, October 11). Standards of accreditation of nurse anesthesia programsPractice doctorate https://www.coacrna.org/wp-content/uploads/2020/01/Standards-for-Accreditation-ofNurse-Anesthesia-Programs-Practice-Doctorate-revised-October-2019.pdf Cowperthwait, A. (2020). NLN/Jeffries simulation framework for simulated participant methodology. Clinical Simulation in Nursing, 42, 1221. Eyikara, E. & Baykara, G., Z. (2017). The importance of simulation in nursing education. World Journal on Educational Technology: Current Issues. 9(1), 02-07. Freeman, B. S., & Berger, J. S. (2014). Anesthesiology core review: Part one basic exam (1st ed). McGraw-Hill Education. Gilliard, K. W. (2020). Students privacy rights: Where HIPAA and FERPA intersect. APTA Magazine, 12(8), 10-15 Goldsworthy, S., Patterson, J. D., Dobbs, M., Afzal, A., & Deboer, S. (2019). How does simulation impact building competency and confidence in recognition and response to the adult and paediatric deteriorating patient among undergraduate nursing students? Clinical Simulation in Nursing, 28, 2532. https://doi.org/10.1016/j.ecns.2018.12.001 Heavey, E. (2015). Statistics for nursing a practical approach (2nd edition). Jones & Bartlett Learning, LLC. HG.org Legal Resources (n.d.) Standards of care in nursing. https://www.hg.org/legalarticles/standards-of-care-in-nursing- BEST PRACTICES FOR GENERAL INDUCTION SIMULATION 38 6237#:~:text=Standards%20of%20care%20or%20standards,result%20in%20certain%20l egal%20implications. INACSL International Association for Clinical Simulation & Learning. (2018). Standards of best practice: Simulation. https://member.inacsl.org/i4a/pages/index.cfm?pageID=3407 INACSL Standards Committee (2016, December). INACSL standards of best practice: Simulation SM Simulation design. Clinical Simulation in Nursing, 12(S), S5-S12. http://dx.doi.org/10.1016/j.ecns.2016.09.005. Jeffries, P. R., Rodgers, B., & Adamson, K. (2015). NLN Jeffries simulation theory: Brief narrative description. Nursing Education Perspectives, 36(5), 292293. Kim, Y.-J., Noh, G.-O., & Im, Y.-S. (2017). Effect of step-based prebriefing activities on flow and clinical competency of nursing students in simulation-based education. Clinical Simulation in Nursing, 13(11), 544551. https://doi.org/10.1016/j.ecns.2017.06.005 King, A. (2018, October 9). Induction of general anesthesia: Overview. UpToDate. Retrieved October 17, 2020, from https://www.uptodate.com/contents/induction-of-generalanesthesia-overview Lateef, F. (2010). Simulation-based learning: Just like the real thing. Journal of Emergencies, Trauma & Shock, 3(4), 348352. https://doi.org/10.4103/0974-2700.70743 McDermott, D. S. (2016). The prebriefing concept: A delphi study of CHSE experts. Clinical Simulation in Nursing, 12(6), 219227. https://doi.org/10.1016/j.ecns.2016.02.001 National League for Nursing. (2005). Description of available instruments. http://www.nln.org/professional-development-programs/research/tools-andinstruments/descriptions-of-available-instruments BEST PRACTICES FOR GENERAL INDUCTION SIMULATION 39 Page-Cutrara, K. (2015). Prebriefing in nursing simulation: A concept analysis. Clinical Simulation in Nursing, 11(7), 335340. https://doi.org/10.1016/j.ecns.2015.05.001 Page-Cutrara, K., & Turk, M. (2017). Impact of prebriefing on competency performance, clinical judgment and experience in simulation: An experimental study. Nurse Education Today, 48, 7883. https://doi.org/10.1016/j.nedt.2016.09.012 Ranum, D., Beverly, A., Shapiro, F. E., & Urman, R. D. (2017). Leading causes of anesthesiarelated liability claims in ambulatory surgery centers. Journal of Patient Safety. https://doi.org/10.1097/PTS.0000000000000431 Roh, Y. S., Ahn, J.-W., Kim, E., & Kim, J. (2018). Effects of prebriefing on psychological safety and learning outcomes. Clinical Simulation in Nursing, 25, 1219. https://doi.org/10.1016/j.ecns.2018.10.001 Scott-Herring, M., Morosanu, I., Bates, J., & Batoon, B. (2020). Cut to air. AANA Journal, 88(2), 116120. Sharoff, L. (2015). Simulation: Pre-briefing preparation, clinical judgement, and reflection. What is the connection?. Journal of Contemporary Medicine, 5(2), 88-101. https://doi.org/10.16899/ctd.49922 Steadman, J., Catalani, B., Sharp, C., & Cooper, L. (2017). Life-threatening perioperative anesthetic complications: Major issues surrounding perioperative morbidity and mortality. Trauma Surgery & Acute Care Open, 2(1), e000113. https://doi.org/10.1136/tsaco-2017-000113 Torsher, L. C. (2018). History of anesthesia simulation. International Anesthesiology Clinics, 56(2), 94106. https://doi.org/10.1097/AIA.0000000000000188 BEST PRACTICES FOR GENERAL INDUCTION SIMULATION 40 Unver, V., Basak, T., Watts, P., Gaioso, V., Moss, J., Tastan, S., Iyigun, E., & Tosun, N. (2017). The reliability and validity of three questionnaires: The student satisfaction and selfconfidence in learning scale, simulation design scale, and educational practices questionnaire. Contemporary Nurse, 53(1), 6074. https://doi.org/10.1080/10376178.2017.1282319 Wiggins, L. L., Morrison, S., Lutz, C., & ODonnell, J. (2018, April). Using evidence-based best practices of simulation, checklists, deliberate practice, and debriefing to develop and improve a regional anesthesia training course. AANA Journal, 86(2), 119-126. https://www.aana.com/docs/default-source/aana-journal-web-documents-1/usingevidence-based-best-practices-of-simulation-checklists-deliberate-practice-anddebriefing-to-develop-and-improve-a-regional-anesthesia-training-course-april2018.pdf?sfvrsn=c2505fb1_8 Yauger, S. J., Konopasky, A., & Battista, A. (2020). Reliability in healthcare simulation setting: A definitional review. Cureus, 12(5), e8111. https://doi.org/10.7759/cureus.8111 Zapko, K. A., Ferranto, M. L. G., Blasiman, R., & Shelestak, D. (2018). Evaluating best educational practices, student satisfaction, and self-confidence in simulation: A descriptive study. Nurse Education Today, 60, 2834. https://doi.org/10.1016/j.nedt.2017.09.006 BEST PRACTICES FOR GENERAL INDUCTION SIMULATION Appendix A Reference in APA format Level of Evidence Aebersold, M., Mariani, B., & Cherara, Level V evidence L. (2018). Do experts and novices agree? A qualitative review of the 2016 INACSL standards of best practice: SimulationSM reviewer feedback. Clinical Simulation in Nursing , 25 , 15. https://doi.org/10.1016/j.ecns.2018.09.0 07 41 Literature Review Matrix Variables Sample Instruments Consistency, Expert reviewers (n=25) and Electronic feedback and theory/objectives, novice reviewers (n=25) questionnaire (four open-ended reliability/validity, clearly questions) defining expectations, and excellence Studies (n=23); population of interest nursing prebriefing methodology No instruments were used Results Novices and experts had similar themes but differences in details. Both types of feedback are important to obtain because users of the standards are both experts and novices. Both types of feedback are important to obtain because users of the standards are both experts and novices. Chamberlain, J. (2015). Prebriefing in Level V evidence nursing simulation: A concept analysis using Rodgers methodology. Clinical Simulation in Nursing , 11 (7), 318322. https://doi.org/10.1016/j.ecns.2015.05.0 03 Identification of prebriefing, surrogate terms, attributes, model case, antecedent and consequences, related concepts To support the rigors of simulation research and development, prebriefing needs to be conducted in a consistent manner. Performing a concept analysis is the first step in ensuring clear under- standing and application of the prebriefing components. It is essential that nursing programs provide high-quality simulation with faculty who are educated in the pedagogy including the prebriefing phase. Chamberlain, J. (2017). The impact of Level III evidence simulation prebriefing on perceptions of overall effectiveness, learning, and selfconfidence in nursing students. Nursing Education Perspectives (Wolters Kluwer Health) , 38 (3), 119125. https://doi.org/10.1097/01.NEP.000000 0000000135 No prebrief, Undergraduate nursing Learning/Orientation students (n=119) activities, Orientation only, Learning only, overall confidence score, overall learning score, overall simulation effectiveness score Simulation Effectiveness Tool Perceptions of overall simulation effectiveness, learning, and self(SET). SET includes 13 items with confidence was significantly higher with prebriefing (p=0.000) a 3-point Likhert-scale compared to no prebriefing. No significant distinction (p>0.05) was found among the prebriefing activities. Findings from this study support the use of learning engagement and orientation activities during prebriefing in order to enhance overall simulation effectiveness. Goldsworthy, S., Patterson, J. D., Level III evidence Dobbs, M., Afzal, A., & Deboer, S. (2019). How does simulation impact building competency and confidence in recognition and response to the adult and paediatric deteriorating patient among undergraduate nursing students? Clinical Simulation in Nursing , 28 , 2532. https://doi.org/10.1016/j.ecns.2018.12.0 01 Knowledge assessement BSN nursing students in related to each of the six their final year (n=63) deteriorating patient simulation cases and reliability of the clinical self-efficacy (CSE) scale Pulse/delta methodology for A high internal consistency (0.91) in all items on the Clinical selfdebriefing, high-fidelity simulator, efficacy tool was seen in the treatment group after the and CSE tool intervention. No significant improvement in any of the Clinical self-efficacy items in the control group (p value > 0.05). Further multistate research is needed to further explore the significance of the simulation intervention. BEST PRACTICES FOR GENERAL INDUCTION SIMULATION 42 Kim, Y.-J., Noh, G.-O., & Im, Y.-S. Level III evidence (2017). Effect of step-based prebriefing activities on flow and clinical competency of nursing students in simulation-based education. Clinical Simulation in Nursing , 13 (11), 544551. https://doi.org/10.1016/j.ecns.2017.06.0 05 Age, school year, GPA, Undergraduate nursing overall GPA in the prior students (junior/senior) semester, flow in practice, (n=207) clinical competency: learner's evaluation, clinical competency: instructor's evaluation, satisfaction, selfconfidence 10-item flow short scale, clinical competency self-reported tool and evaluation tool, instructor checklist, two numeric rating scales measuring satisfaction and selfconfidence McDermott, D. S. (2016). The Level VII evidence prebriefing concept: A delphi study of CHSE experts. Clinical Simulation in Nursing , 12 (6), 219227. https://doi.org/10.1016/j.ecns.2016.02.0 01 Gender, age, residence, Certified as a Healthcare length of time using SBL Simulation Educator in education, highest (CHSE) (n=59) academic degree, types of simulation used, members of professional organizations, organizational setting where SBL used, incorporate INACSL standards of practice Electronic Qualtrics survey and five- Certified Healthcare Simulation Educators (expert panel) reached point Likert scale consensus (>70%) on 83 statements about prebriefing. Findings from this review suggest prebriefing is an important three-phase process of simulated-based learning (SBL). Findings of this study may be used to develop guidelines to for successful SBL experience. Page-Cutrara, K. (2015). Prebriefing in Level II evidence nursing simulation: A concept analysis. Clinical Simulation in Nursing , 11 (7), 335340. https://doi.org/10.1016/j.ecns.2015.05.0 01 Use of the concept, model case, contrary case, related case, antecedents and consequences, empirical referents Page-Cutrara, K., & Turk, M. (2017). Level I evidence Impact of prebriefing on competency performance, clinical judgment and experience in simulation: An experimental study. Nurse Education Today , 48 , 7883. https://doi.org/10.1016/j.nedt.2016.09.0 12 Competency performance; Undergraduate nursing clinical judgment; student (n=76) perceptions of prebriefing experience; competency performance, clinical judgment, and prebriefing experience Studies (n= 31); population No instruments were used of interest included relevance to nursing student education Creighton Competency Evaluation Instrument (CCEI), Clinical Judgement subscale (CCEI-CJ), and Prebriefing Experience Scale (PES) The age, year, clinical training GPA, and overall GPA in the previous semester among the study groups showed no significant differences, thereby supporting homogeneity among the groups (pvalue unspecified). Flow in experimental group 2 was the highest and significantly higher than the other two groups (F=8.14, p=0.001). Learner's self-evaluated clinical competency score of experimental group 2 was signficantly higher (F=5.19, p=0.006). Satisfaction higher in the experimental group 2 (F=4.25, p=0.016), self-confidence was significantly higher in experimental group 2 (F=7.65, p=0.016). This concept analysis suggests that research specifically focusing on prebriefing could strengthen prebriefings contribution to the simulation experience, provide a clearer, evidence-based understanding of the activity, and address questions about optimal delivery of simulation for learning A statistically significant difference was demonstrated between group for competency performance (p<0.001), clinical judgment (p<0.001) and prebriefing experience (p<0.001). No relationship was found between perception of prebriefing experience and students' simulation performance. BEST PRACTICES FOR GENERAL INDUCTION SIMULATION Roh, Y. S., Ahn, J.-W., Kim, E., & Level III evidence Kim, J. (2018). Effects of prebriefing on psychological safety and learning outcomes. Clinical Simulation in Nursing, 25 , 1219. https://doi.org/10.1016/j.ecns.2018.10.0 01 Study variables between Undergraduate nursing experimental and control students in their fourth year groups: team of simulation (n=281) psychological safety, academic safety (i.e., academic safety/comfort and anxiety), satisfaction with debriefing, and team performances. Sharoff, L. (2015). Simulation: PreLevel IV evidence briefing preparation, clinical judgement, (mix-method studies) and reflection. What is the connection?. Journal of Contemporary Medicine, 5 (2), 88-101. https://doi.org/10.16899/ctd.49922 Confidence, skill competency, anxiety levels, preceived critical thinking skills and problem-solving. Staun, J. (2020). Anesthesia simulation Level I evidence in cardiac surgery (ASICS). AANA Journal , 88 (3), 183189. male-to-female ratio, SRNA participants (n=23) current age, race, years of employment as a RN, time in the critical care, type of ICU 43 Korean version of the Edmondson (1999)'s Team Psychological Safety scale. Academic safety was assessed using the academic safety tool. Satisfaction with debriefing was assessed using the Korean version of Debriefing Assessment for Simulation in HealthcareStudent Version (DASH-SV). Video-recorded group performance was rated using the Korean version of Advanced Cardiovascular Life Support Skills Checklist. Undergraduate junior/senior Surveys, PMS-SS, PMS-IS, nursing students (n=81) and LCJR/Scoring Sheet, Guide for instructors (n=9) Simulation Reflection (qualitative survey) Objective Structured Clinical Examination (OSCE), National League of Nursing surveys, 5-point Likert scale, "agreement" and "importance" Likert scale, student satisfaction and self-confidence in learning survey, educational practices questionnaire Nursing students in the experimental group showed higher team psychological safety (t=2.754, p=0.008) and cardiopulmonary resuscitation performance (t=7.488, p<0.001). However, there were no differences in overall academic safety (t=1.67, p=0.103)or satisfaction with debriefing scores (t=1.202, p=0.238). Prebriefing strategies that incorporate the fiction contract and concept mapping could help nursing students to improve team psychological safety and performance. Pre-briefing preparatory material for both students and instructors provided essential and adequate information for an effective and enhanced simulation learning experience. Students felt their ability to grasp the experience; understand the simulation situation; provide effective nursing actions; and reflect upon their experience was enhanced with the utilization of the pre-briefing preparatory material. Instructors felt more confident and prepared after reading the preparatory material. Using simulation activities scored high in agreement and importance which validated the ASICS implementation into the SRNA's curriculum. SRNAs agreed that skills they learned from ASICS could transfer over into their cardiac rotations. Through the ASICS, SRNAs gained a greater sense of self-efficacy and clinical competence. BEST PRACTICES FOR GENERAL INDUCTION SIMULATION Wiggins, L. L., Morrison, S., Lutz, C., Level V evidence & ODonnell, J. (2018, April). Using evidence-based best practices of simulation, checklists, deliberate practice, and debriefing to develop and improve a regional anesthesia training course. AANA Journal, 86 (2), 119-126. https://www.aana.com/docs/defaultsource/aana-journal-web-documents1/using-evidence-based-best-practices-ofsimulation-checklists-deliberate-practiceand-debriefing-to-develop-and-improvea-regional-anesthesia-training-courseapril-2018.pdf?sfvrsn=c2505fb1_8 Yauger, S. J., Konopasky, A., & Battista, A. (2020). Reliability in healthcare simulation setting: A definitional review. Cureus, 12 (5), e8111. https://doi.org/10.7759/cureus.8111 Average length of time as CRNAs enrolled in the a CRNA (years), no NARAT course (n=49) preexisting regional skills, experience with placement of epidural or spinal block, Had place 1-5 "spinals" and "epidurals" as a CRNA, performed 15 sterile procedures a month, performed 6-10 sterile procedures a month, right-hand dominant, last time any participant had placed a spinal or epidural block (months) Level IV evidence Demographic summary of Studies (n=50); population (mix-method studies ) setting attributes, articles of interest is mixed published by year, word frequency search for terms related to reliability, Ontology of simulation setting reliability, constructs influencing simulation setting reliability, reliability terms used by authors, training simulated patients for performance standardization using a standardized training approach 44 Best practices and guidelines for simulation course deployment, development and improvement were used. These included use of the Delphi method/Angoff method for checklists, deliberate practice, realistic simulation, and debriefing and structured feedback. Additionally, NARAT precourse survey and precourse attitude surveys were administered, the skills assessment/checklists were completed during the hands-on component, and a NARAT postcourse survey. Postcourse scores for comfort and confidence level for both spinal (4.14) and epidural (4) blocks were improved compared with precourse results. An important incidental observation was that age seems to be a factor in the anesthesia provider's comfort level of performing spinal or epidural. Essential elements are important during development of a blended course help CRNAs improve knowledge, attitude, and skills needed for competent regional anesthesia practice. QSR International's NVivo data analysis software was used to support search processes, data collection efforts, and data management. The Standards for Reporting Qualitative Research serves as the reporting guidelines. Experience of the simulation is influenced by the social characters of simulations. Reliability in healthcare simulation is dependent on an simulated participant's consistency to achieve specific performance tasks. Reliable simulations should provide the same stimuli to participants to ahcieve their goal-orientated activities. Inconsistencies and setting errors resulted in stimulated participants performance that exposed them to unequal conditions that influenced competency achievement. BEST PRACTICES FOR GENERAL INDUCTION SIMULATION 45 Appendix B Note. From NLN Jeffries Simulation Theory: Brief Narrative Description, by P. R. Jeffries, B. Rodgers, and K. Adamson, 2015, Nursing Education Perspectives, 36(5), 292293. Copyright 2015 by the National League for Nursing. BEST PRACTICES FOR GENERAL INDUCTION SIMULATION Appendix C Note. From Description of Available Instruments, by National League for Nursing, 2005 (http://www.nln.org/professional-development-programs/research/tools-and-instruments/descriptions-ofavailable-instruments). In the public domain. 46 BEST PRACTICES FOR GENERAL INDUCTION SIMULATION Appendix D 47 BEST PRACTICES FOR GENERAL INDUCTION SIMULATION 48 Appendix E General Anesthetic Induction Checklist Rubric Name: ______________________________________________________________ MSMAID Successful Unsuccessful Machine Suction Monitors Airway equipment IV works Drugs ready Induction Successful Unsuccessful Apply 100% oxygen by mask Apply monitors Obtain first set of vital signs Administer narcotic Administer lidocaine Administer induction agent Check eyelid reflex Verify that you can ventilate Administer paralytic Tape eyes closed Assess neuromuscular function (no twitches) Perform laryngoscopy and insert endotracheal tube Inflate pilot balloon Connect ETT to breathing circuit and give one breath Verify ETT placement with EtCO2 and Bilateral breath sounds Tape ETT in place Turn on vaporizer Turn on ventilator Recheck vital signs __________________________________________ Instructor Signature _______________________________ Date  ...

... SIMULATION FOR ANESTHESIA MACHINE SET-UP Marian University Leighton School of Nursing Doctor of Nursing Practice Final Project for Students Graduating in May 2022 Simulation-Based Training for Anesthesia Machine Set-up Nicholas Keesler Leighton School of Nursing, Marian University Chair: Bradley Stelflug, DNAP, CRNA ___________________________ (Signature) Committee Member(s): Christina Pepin, Ph. D., RN, CNE ___________________________ (Signature) Date of Submission: November 4th, 2021 1 SIMULATION FOR ANESTHESIA MACHINE SET-UP Table of Contents Acknowledgements ..............................................................................................................6 Abstract ................................................................................................................................7 Introduction .........................................................................................................................9 Background ....................................................................................................................9 Problem Statement .......................................................................................................10 Gap Analysis ................................................................................................................10 Review of the Literature ....................................................................................................11 Search Methodology ....................................................................................................12 Simulation ....................................................................................................................12 Knowledge ...................................................................................................................13 Confidence ...................................................................................................................15 Debriefing ....................................................................................................................15 Literature Review: Conclusion ..........................................................................................16 DNP Theoretical Framework .............................................................................................17 Goals, Objectives, and Expected Outcomes ......................................................................18 Project Proposal .................................................................................................................18 Project Design ..............................................................................................................18 Project Site ...................................................................................................................19 2 SIMULATION FOR ANESTHESIA MACHINE SET-UP Methodology ................................................................................................................19 Measurement Instrument ............................................................................................20 Data Collection ............................................................................................................20 Analysis........................................................................................................................21 Timeline .......................................................................................................................21 Findings and Results ..........................................................................................................22 Satisfaction and Self-Confidence with Current Learning ............................................23 Post-Test overall Satisfaction with Current Learning between Control Group and Subjects .......................................................................................................................23 Post-Test Overall Self-Confidence in Learning between Control Group and Subjects .......................................................................................................................24 Pin Index Safety System Configuration for Oxygen ...................................................24 Safe Delivery of Anesthesia Care ................................................................................24 Piped Gas Pressure.......................................................................................................25 Knowing When Breathing Circuit System pressure and Leak Test Should be Conducted 29 Ensuring Anesthesia Equipment ..................................................................................30 Qualitative Findings .....................................................................................................25 Discussion .........................................................................................................................26 3 SIMULATION FOR ANESTHESIA MACHINE SET-UP Strengths ......................................................................................................................27 Limitations and Recommendations..............................................................................27 Implications for Future Practice ........................................................................................28 References ..........................................................................................................................29 Appendices.........................................................................................................................32 Appendix A Literature Review Matrix .....................................................................32 Appendix B Jeffries Simulation Model ....................................................................37 Appendix C Student Satisfaction and Self-Confidence in Learning ........................38 Appendix D Demographics Questionnaire ...............................................................39 Appendix E Modified Student Satisfaction and Self-Confidence in Learning ........40 Appendix F Knowledge Assessment Questionnaire .................................................43 Appendix G IRB Exemption Form ...........................................................................44 Appendix H Tables ...................................................................................................45 4 SIMULATION FOR ANESTHESIA MACHINE SET-UP Dedication The anesthesia profession is based in medical science, but is practiced as an art. As anesthesia has evolved and developed so has the training of nurse anesthetists. In keeping with the evolving progress of nurse anesthetist training, I chose to further develop the process of the anesthesia machine set-up training. Student nurse anesthetists have little to no exposure to the anesthesia machine prior to beginning their training, but it is a machine that they will use every day. I feel that it is a strong foundation is paramount to nurse anesthesia training. This DNP project is dedicated to my loving wife, Caitlin Keesler. She has been my foundation providing love and support. 5 SIMULATION FOR ANESTHESIA MACHINE SET-UP 6 Acknowledgement I would like to express my gratitude to my Chair Dr. Stelflug for his help in developing and guiding this project. Also, for encouraging me to continue to follow this path of improving Nurse anesthesia education through simulation. I also want to express my thanks to my Committee Member Dr. Pepin, who has patiently and kindly guided me throughout this project from the beginning to the end. SIMULATION FOR ANESTHESIA MACHINE SET-UP 7 Abstract Background and Review of Literature: The presurgical anesthesia machine check is a critical procedure that all anesthesia providers must be capable of performing. Failing to perform an anesthesia machine check increases morbidity and mortality during patient care. Simulation training for anesthesia providers is a safe and effective educational pathway allowing providers to improve their skills prior to beginning clinical practice. Optimizing learning by modifying simulation practices to follow INACSL practice standards will enhance the learning for future anesthesia providers. Purpose: This DNP project was a quality improvement project to examine the effect on SRNA knowledge, confidence, and satisfaction of learning when comparing anesthesia machine checklist versus simulation-based training. Methods: This DNP project utilized a quality improvement design and was evaluated by the Student Satisfaction and Self-confidence in Learning instrument and a post-test knowledge assessment. Implementation Plan/Procedure: A total of 24 SRNAs enrolled in Marian Universitys nurse anesthesia simulation course were divided into 2 groups. Group one participated in the current practice of an anesthesia machine checkoff, while group two participated in the current practice and the anesthesia machine set-up simulation. Both groups underwent the same checkoff for the current anesthesia machine checkoff. Both groups were given a post-test of the Student Satisfaction and Self-confidence in Learning instrument. Finally, both groups also completed a post-test knowledge assessment. Implications/Conclusion: Overall, the participants in the experimental group scored higher in agreement in the satisfaction (U = 28.0 , p = 0.032 ) and self-confidence of student SIMULATION FOR ANESTHESIA MACHINE SET-UP 8 learning (U = 43.5 , p = 0.273). The experimental group scored higher in the knowledge section. If current practice is modified to incorporate INACSL best practice standards for simulation, nurse anesthesia educational programs will be able to increase confidence and safety of SRNAs as they progress through their anesthesia training. Keywords: SRNA, Simulation, INACSL, Jeffries Simulation Model, Anesthesia Machine SIMULATION FOR ANESTHESIA MACHINE SET-UP 9 Doctoral of Nursing Practice Project: Simulation Based Training for Anesthesia This project is submitted to Marian University Leighton School of Nursing faculty as partial fulfillment of degree requirements for the Doctor of Nursing Practice, Nurse Anesthesia Track. This was an evidenced-based study into the effects of anesthesia machine setup simulation on student knowledge and confidence. Current International Nursing Association for Clinical Simulation and Learning (INACSL) simulation frameworks and methodologies were used to assess and evaluate the current anesthesia machine set-up practices and then implemented ways to improve the knowledge, confidence, and clinical judgment needed for a successful anesthesia machine setup. The goal of this project was to provide the first-year student registered nurse anesthetist (SRNA) students with simulation experiences that would positively impact their learning and behavior, preparing them for their future as Certified Registered Nurse Anesthetists (CRNA). Background The beginning of any clinical experience should start with a firm foundation in knowledge and learning. Knowledge and learning occur through the combination of experiences and reflection (INACSL Standards Committee, 2016a). In the case of anesthesia machine setup, it is essential to instill a foundational learning experience that individuals may carry to all facets of an individuals anesthesia practice. With the continual use of simulation as a teaching tool, researchers are continually advancing to discover the best evidence-based practices. INACSL is at the forefront of simulation design, implementation. INACSL standards for simulation provide in-depth guidelines allowing for standardization of language regarding the development, research use, and teaching applications (Sittner et al., 2016). A focused simulation design promotes a process that SIMULATION FOR ANESTHESIA MACHINE SET-UP 10 increases consistency with the institutional mission and program goals for teaching and education (INACSL Standards Committee, 2016b). Through the inclusion of the INACSL standards of best practice in this research study, it is possible to develop an improved institutional framework to apply to simulation, specifically anesthesia machine setup. The lack of follow-through with INACSL standards of best practice may lead to a poor simulation (INACSL Standards Committee, 2016b). Problem Statement A student registered nurse anesthesia (SRNA) is an expert in their nursing practice before applying and accepting a placement into a nurse anesthetist training program. With adult learners, it is necessary to develop a training method that is purposeful and systematic, but flexible (INACSL Standards Committee, 2016b). With first-year anesthesia students, it is vital to incorporate their knowledge and experiences, but also to expand upon that knowledge in a way that is consistent with adult learning strategies and graduate level teachings. The question posed by this Doctoral of Nursing practice project is whether SRNA students participation in anesthesia machine set-up simulation improves knowledge and confidence compared to basic anesthesia machine set-up checkoff. Gap Analysis This project was implemented at Marian University in the CRNA program curriculum. The current practice for anesthesia machine set-up is a student check-off which is completed under the supervision of the simulation course instructor. Current practice requires the SRNA to complete an anesthesia machine checkoff. SRNAs go through an anesthesia machine checkoff on an Apollo Drager anesthesia machine, which are currently used in many clinical sites students use for their clinical training. SIMULATION FOR ANESTHESIA MACHINE SET-UP 11 The anesthesia machine checkoff does not increase a SRNAs critical thinking skills to address critical issues during an anesthesia machine set-up. However, an anesthesia machine set-up simulation with a debrief, which has been shown to benefit learners, would better prepare SRNAs for their clinical experiences (Ballister, 2018; Cannon-Diehl et al., 2012; Fletcher, 1995; Grota & Oneal, 2020; Hanshaw & Dickerson, 2020; INACSL Standards Committee, 2016-a, 2016-b; McMullen et al., 2016). This site is appropriate for project implementation due to its curriculum requirements and the availability of a high-fidelity simulation lab. Additionally, simulation-based training is becoming a modern mainstay in the education of SRNA students and is encouraged by the National Board of Certification and Recertification for Nurse Anesthetists (NBCRNA). Accrediting organizations acknowledge that simulations help develop critical thinking skills and provide experiential learning opportunities for SRNA students (Council on Accreditation, 2020). Review of Literature The practice of nursing within the healthcare realm is constantly evolving and our education strategies are continually changing to meet the demands of the times. A recent trend in medical education has been the use of simulation-based training to develop critical thinking and psychomotor development (Kim et al., 2016). Students engaging in simulation training can develop technical skills and receive prompt feedback. This immediate feedback provides a pathway to improving comprehension and overall performance. A key benefit of simulation is that it allows learners to take their simulation experiences into the real world to improve healthcare quality (Hegland et al. 2017). SIMULATION FOR ANESTHESIA MACHINE SET-UP 12 Search Methodology PubMed, CINAHL, Google Scholar, and the American Association of Nurse Anesthetists (AANA) webpage were utilized for the search of literature regarding anesthesia and nursing education and simulation. Keywords with the Boolean phrases and and or were used during the search. Keywords included: simulation, simulation-based, training, education, confidence, knowledge, performance, nursing education, nurse anesthetist, anesthetist, CRNA, training, and debrief. Inclusion criteria required articles to be published within the last five years, published in the English language, and contain keywords used in the search. Studies from before 2015 were excluded from the literature search. However, that exclusion criteria was waived for one article, Fletcher (1995) considering the classical importance to anesthesia simulation, stating that simulation accelerates understanding and confidence early in nurse anesthesia training. Additionally, articles needed to establish a measurement of knowledge, confidence, or performance based on a simulation training experience. The International Nursing Association for Clinical Simulation and Learning (INACSL) was also incorporated into the discovery of articles related to simulation. After a thorough search through 28 full text articles, six journal articles levels I-VII of the nursing hierarchy of evidence were found and used to write this review of the literature. (Appendix A) Simulation Utilizing simulation as an educational technique allows educators to assess competency and has been shown to decrease medical errors in the healthcare environment (Cannon-Diehl et al., 2012). An example of the utilization of simulation to decrease medical errors would be the American Heart Associations (AHA) use of manikins with audible cues for the correct depth of chest compressions during basic and advanced life support training. The AHAs manikin SIMULATION FOR ANESTHESIA MACHINE SET-UP 13 simulation training allows students to practice their chest compression technique and instructors to provide feedback. The development and use of INACSL standards for simulation allow for a standardization of simulation design, language, and behaviors (Sittner et al., 2015). Teaching through simulation provides a safe and controlled environment for knowledge and learning to occur, by combining experiences and reflection (INACSL Standards Committee, 2016a). During an anesthesia provider's education, they will undoubtedly come across an anesthesia machine, and it is fundamental to understand its purpose and function. Simulation can be used as a steppingstone in developing a deeper understanding of how-to set-up and operate anesthesia machines. As educators continue to adopt and invest in new simulation practices, they should try to incorporate INACSL simulation standards to incorporate a uniform teaching method (INACSL Standards Committee, 2016a). Integration of these standards will improve the development of critical thinking skills protecting future patients. Knowledge Before any simulation, it is paramount to assess the knowledge, perceptions, and attitudes of the participants (Cannon-Diehl et al., 2012). The anesthesia machine provides various uses in the operating theatre during its use for patient care. Anesthesia providers need to differentiate between these uses of an anesthesia machine. The anesthesia machine delivers anesthetic vapors to the patient at exact concentrations along with other gases such as oxygen, nitrous, and air. Developing an understanding of the mechanics and physiology of delivering anesthesia is central to providing patient care (Shields & Gentry, 2020). An example of this is Shields & Gentrys (2020) research, which discusses that transesophageal echocardiography simulation was a net positive with a P value of less than 0.1 with first-year SRNA students due to their improved image interpretation and pathology SIMULATION FOR ANESTHESIA MACHINE SET-UP 14 identification. Shields and Gentrys (2020) study showed that initial pretest scores were similar for online and simulation groups averaging 33.5 for the online group and 35.6 for the simulation group. Post test scores were higher for simulation with an average of 69.4 compared to online with an average of 42.3 (Shields & Gentry, 2020) Simulation may allow students to develop a stronger knowledge base and learn about the anesthesia machine before their transition from didactic to clinical practice. Moving from didactic to clinical practice could prove to be a difficult transition with little previous hands-on experience. Kim et al. (2016) found that simulation is an effective strategy to enhance baccalaureate nursing students psychomotor skills. They also found that educational outcomes improved through the implementation of a variety of learning methods in congruence with simulation (Kim et al., 2016). This becomes especially important when performing anesthesia machine set-up. While understanding how to prepare the anesthesia machine is an often-routine activity, if done incorrectly patient care outcomes may suffer through unrecognized machine faults monitoring oxygen, carbon dioxide, and inadequate suction (Fletcher, 2015). Additionally, Hustad et al. (2019) reported simulation experiences improved clinical skills and judgment. They wrote that simulation provided learning in a safe environment, allowing participants to close the knowledge gap between theory and clinical practice (Hegland et al., 2017). A common consensus between multiple studies showed that simulation training is a beneficial tool to be utilized throughout the didactic and clinical curriculum to further critical thinking skills and knowledge development (Ballister, 2018; Cannon-Diehl et al., 2012; Fletcher, 1995; Grota & Oneal, 2020; Hanshaw & Dickerson, 2020; INACSL Standards Committee, 2016-a, 2016-b; McMullen et al., 2016). Students need to be exposed to simulation consistently to form a foundational knowledge base which can then be transitioned into clinical practice. SIMULATION FOR ANESTHESIA MACHINE SET-UP 15 Confidence Through the use of simulation-based training, it is possible to build the confidence of students before and during their interactions during clinical practice (Hanshaw & Dickerson, 2020). Additionally, Hanshaw & Dickerson (2020) summarized, through a review of multiple studies, that there was an increase in participant self-confidence after participating in a simulation scenario. The transition from didactic to clinical practice setting often results in performance anxiety, which may be decreased through simulation training (Ballister, 2018). Two studies also reported that along with increasing confidence gained by the use of simulation as a teaching method, it also decreased anxiety (Ballister, 2018; Hanshaw & Dickerson, 2020). Debriefing A simulation debriefing reflects the actions performed which can lead to new interpretations by students and cognitive reframing which enhances learning and self-awareness (INACSL Standards Committee, 2016a ). Hanshaw and Dickerson (2020) wrote a comprehensive literature review using twenty studies and identified debriefing as the most crucial aspect of simulation learning. Debriefings provide an avenue for students to align theory with clinical practice and further develop their critical thinking skills (Ballister, 2018; CannonDiehl et al., 2012; Fletcher, 1995; Grota & Oneal, 2020; Hanshaw & Dickerson, 2020; INACSL Standards Committee, 2016-a, 2016-b; McMullen et al., 2016). The importance of debriefing sessions demands that they be incorporated throughout simulation-based curriculums. INACSL (2016a) presents a debriefing protocol for simulation-based education and with the constant push to evolve simulation training, having protocols in place to standardize debriefing benefits both educators and students (INACSL Standards Committee, 2016a). This SIMULATION FOR ANESTHESIA MACHINE SET-UP 16 also presents an opportunity for the student to reflect on the simulation and appraise their actions or inactions (Hanshaw & Dickerson, 2020). Additionally, the debriefing session after simulation-based training allows for further development of the participating students, preparing them for clinical practice (Hegland et al., 2017). A key difference McMullen et al. (2016) included in their study was that participants were able to pause the scenario and debrief at the moment. McMullen et al. (2016) wrote that the student may feel overwhelmed and stressed during a simulation leaving them unable to absorb information. Pausing the simulation was an action that provided an opportunity for the student to relax and reflect, which improved performance (McMullen et al., 2016). Review of Literature: Conclusion Simulation-based educational training sessions can improve the overall performance, knowledge, and confidence of participants. Through the experiences and knowledge gained through simulation training, students are better prepared for entering clinical practice (Ballister, 2018). This preparation allows for a smoother transition into clinical practice (Ballister, 2018). Although there is not much research specifically related to anesthesia machine set up, the articles presented in this review of literature discuss the benefits of simulation on student learning. Additionally, the simulation debriefing session allows the knowledge and the necessary skills to be tied together, forming a solid foundation for the learning experience. Finally, through gained confidence and knowledge future anesthesia providers will ultimately be able to increase the quality of patient care provided. DNP Theoretical Framework The Jeffries Simulation Theory was selected as the theoretical framework for this project. The Jeffries Simulation Theory model was developed in 2005 and was meant as a simulation SIMULATION FOR ANESTHESIA MACHINE SET-UP 17 design template (Jeffries, 2005). This theory has five major components of designing a simulation: objectives, fidelity, complexity, cues, and debriefing (Jeffries et al, 2015). Simulations are an effective method of teaching and developing critical thinking skills in a safe environment (Jeffries, 2005). Through using Jeffries Simulation theory this projects objective was to develop a simulation that will teach and test first year SRNA students critical thinking skills for an anesthesia machine set-up. The experimental group had three objectives to complete within a twenty-minute time frame. Secondly, fidelity will involve a functioning Apollo Drager anesthesia machine, which students will see in clinical practice. Jeffries Simulation Theory indicates that some simulations may be complex to new learners and cues help students progress through the simulation (Jeffries, 2005). Finally, perhaps one of the most important components for learning is debriefing . Debriefing allows the learner to review and discuss their actions throughout the simulation. Debriefing also allows the teacher to evaluate and discuss the learners decision making and how to approach similar scenarios in the future (Jeffries, 2015). In studying the importance of debriefing Kim et al., (2016) found that debriefing helps improve cognitive learning and improved critical thinking skills. Using the Jeffries Simulation Theory model with a focus on objectives, cues, and debriefing, this project design guided learning and problem solving during an anesthesia machine set-up simulation. Reflection of the simulation simultaneously allows the educators to continue to develop and improve simulation for future learners. This theory allows the examination of learners knowledge outcomes, skills, competency, critical thinking, and self-confidence, which are of interest to this project. SIMULATION FOR ANESTHESIA MACHINE SET-UP 18 Goals, Objectives, and Expected Outcomes This project was designed to increase the knowledge and confidence of first-year SRNA students through anesthesia machine set-up simulation prior to their first clinical experience. The goals of this project were to 1) evaluate the knowledge of anesthesia machine set-up through a post-test; 2) evaluate the confidence level after anesthesia machine set-up simulation; 3) evaluate if the addition of debriefing increases SRNA knowledge of anesthesia machine set-up. These goals were assessed through a posttest assessment and evaluation. The objective of this project was to give first year SRNA students high-fidelity and high-quality simulation-based training for anesthesia machine setup which will enhance their learning and ability to perform a successful anesthesia machine setup. The expected outcomes of this project were to improve learning and anesthesia machine setup through the use of INACSL best practices for simulation. Project Proposal Project Design This project is designed to be a quality improvement intervention for anesthesia machine set-up simulation. This project compared current check-off based training for anesthesia machine setup utilized at Marian University to anesthesia machine setup using INASCL best practices for simulation. Through comparing the practices, it was to be determined if this project is an improvement over Marian Universitys current anesthesia machine setup checkoff using posttests to assess confidence levels and knowledge. Site Marian University is home to Indianas first Doctorate of Nursing Practice CRNA program with an average class size of twenty students. This project made use of the CNRA programs high-fidelity simulation lab located at Marian University. Marian Universitys CRNA SIMULATION FOR ANESTHESIA MACHINE SET-UP 19 simulation lab contains an Apollo Drager anesthesia machine with oxygen, nitrogen, and carbon dioxide gas connections. Methodology This project focused on a quality improvement process to the enhance the teachings of the anesthesia machine set-up at Marian University. This project involved the use of a fully functional anesthesia machine for simulation purposes. This quality improvement project promoted the use of simulation in combination with a checkoff list versus a standard checkoff list. First-year SRNA students were evenly and randomly assigned to either the control or experimental group. The control group participated in the current training for anesthesia machine set-up check off. The control group participated in the standard practice anesthesia machine set-up checkoff with the instructor. The experimental group went through a basic anesthesia machine set-up checkoff first. Next, a set of two first-year SRNAs left the room and the machine was rendered faulty. The faults used for this simulation were a failure of a low-pressure test due to a circuit leak, a cracked suction canister that needs exchanged, and an empty oxygen cylinder on the back of the anesthesia machine that needed exchanged. Interventions required the students to problem-solve through the machine set-up to identify and remedy the fault within a twentyminute time frame. Immediately after the completion of the simulation by the two first-year SRNAs the author conducted a debriefing session of up to twenty minutes. The debriefing session was used to assess the student insights about their problem solving and decision-making skills required for successful anesthesia machine set-up. Once the debriefing session was completed the students SIMULATION FOR ANESTHESIA MACHINE SET-UP 20 were asked to complete a post-test to assess confidence and knowledge immediately after the simulation. Measurement This project measured the SRNA students knowledge and confidence with a post-test. A competency evaluation was performed at the completion of each simulation. A post-test developed by the National League for Nursing and modified for this project was used to measure confidence for both groups (National League of Nursing, 2021). A knowledge-based post-test comprised of questions written to establish if the SRNA student understands the importance of anesthesia machine setup developed using Nagelhouts 6th edition Nurse Anesthesia, 2018. Faults were decided with CRNA faculty and author of this project. Students were given twenty minutes to correct the faults, before the senior SNRA intervenes to assist in the correction of faults with prompt progression into debriefing session. Data Collection Data from this quality improvement intervention was collected during first-year SRNA skills checkoffs in March 2021. Information was gathered electronically through Qualtrics, a third-party vendor, and stored on the cloud with an encryption key. Data from the post-test and competency evaluation was collected with anonymous response reporting to maintain confidentiality. A limitation to this quality improvement project was the small sample size of SRNA first-year anesthesia students. Another limitation of this study was the restriction of group sizes. These limitations may be overcome by scheduling small group sizes for the simulation in the future. SIMULATION FOR ANESTHESIA MACHINE SET-UP 21 Analysis After the ordinal data was collected, it was analyzed using a non-parametric test. In this case a Mann Whitney U test was used to compare the control and simulation group results. Data was compiled and synthesized to evaluate the dependent variable (check-off) and independent variable (simulation) through comparing the mean score of the two samples and related confidence intervals. A statistical package for social sciences software was used to conduct the statistical analysis. Timeline This project proposal was completed in December 2020. Institutional Review Board (IRB) submission occurred in January 2021. Upon approval from the IRB, the implementation and data collection commenced on March 8th, 2021, and was completed by the end of April 2021. Final data analysis was completed by May 2021. Findings and Results A total of 24 first-year Marian SRNAs qualified for this study. The students were randomly divided into the control group and the subject group. A total of 10/12 students in the control group completed the post-survey with knowledge test (83% response rate). All twelve students in the subject group participated in and completed the post-survey (100% response rate). Most respondents (77%) were between the ages of 26-35 and have been working as a registered nurse for at least 4 years (82%). All respondents, (100%) do not have any previous experience setting up an anesthesia machine prior to beginning CRNA school. Please refer to Table 1 to view the demographics of all survey respondents. Table 1 Demographics and Background Characteristics of All Survey Respondents SIMULATION FOR ANESTHESIA MACHINE SET-UP 22 Characteristics Experience Setting Up an Anesthesia Machine Prior to Beginning CRNA School Yes No n % 0 22 0 100 Age Group 20-25 years 26-30 years 31-35 years 36-40 years 40+ years 1 12 5 2 2 4.5 54.5 22.7 9.1 9.1 4 9 3 6 18.2 41.0 13.6 27.2 Years Working as a Registered Nurse 1-3 years 4-6 years 7-11 years 12+ years Note. n=22 Satisfaction and Self-Confidence with Current Learning Questions one through five on the questionnaire were specifically measuring overall satisfaction with current learning. Questions six through thirteen on the questionnaire were specifically measuring self-confidence with current learning. All questions were analyzed separately by their reported levels of agreement and compared amongst groups. Please see Appendix I to view table 2-14. Post-Test Overall Satisfaction with Current Learning between Control Group and Subjects To determine if satisfaction with current learning differed in a statistically significant way, students reported levels of satisfaction on a 5-point scale that ranged from 1 to 5. The results of a Mann Whitney U test indicated that those in the post-test subject group were more satisfied with current learning (Mdn=4.8, range 3.6-5.0) than those in the post-test control group (Mdn=3.8, range 2.4-5.0). This difference in satisfaction was statistically significant (U= 28.0, SIMULATION FOR ANESTHESIA MACHINE SET-UP 23 p=.032). In addition to the summed values, four individual items in the subscale were statistically significant. See table 15 for satisfaction results. Table 15 Results of 5-items to measure satisfaction with current learning in post-test. Control group Subject group Mean mean (SD) mean (SD) difference Satisfaction 1 3.80 (1.03) 4.75 (0.45) +0.95 +0.27 Satisfaction 2 3.90 (0.88) 4.17 (0.94) +1.17 Satisfaction 3 3.50 (1.08) 4.67 (0.49) Satisfaction 4 3.50 (1.18) 4.50 (0.67) +1.00 +0.78 Satisfaction 5 3.80 (0.92) 4.58 (0.67) +4.17 Summed satisfaction 18.50 (4.40) 22.67 (2.77) *Note. Using Mann Whitney U test, statistically significant change at p <0.05. Item p-Value 0.008* 0.389 0.007* 0.032* 0.030* 0.032* Post-Test Overall Self-Confidence in Learning between Control Group and Subjects To determine if self-confidence in learning differed in a statistically significant way, students reported levels of satisfaction on a 5-point scale that ranged from 1 to 5. The results of a Mann Whitney U test indicated that there was not a statistically significant difference in selfconfidence in learning (U= 43.5, p=.273) between post-test subject group (Mdn=4.2, range 3.754.9) and control group participants (Mdn=4.0, range 3.5-5.0). Likewise, zero items in the subscale were statistically significant. See table 16 for satisfaction results. SIMULATION FOR ANESTHESIA MACHINE SET-UP 24 Table 16 Results of 8-items to measure self-confidence in learning in post-test. Control group Subject group Mean mean (SD) mean (SD) difference Self-Confidence 6 4.00 (0.67) 3.92 (0.79) -0.08 -0.02 Self-Confidence 7 4.10 (0.74) 4.08 (0.52) +0.33 Self-Confidence 8 4.00 (0.67) 4.33 (0.49) Self-Confidence 9 3.70 (1.25) 4.42 (0.52) +0.72 Self-Confidence 10 4.20 (0.92) 4.33 (0.65) +0.13 Self-Confidence 11 4.40 (0.52) 4.25 (0.45) -0.15 Self-Confidence 12 4.20 (0.42) 4.17 (0.84) -0.03 Self-Confidence 13 3.70 (1.16) 4.08 (0.90) +0.38 +1.28 Summed Confidence 32.30 (3.74) 33.58 (2.46) *Note. Using Mann Whitney U test, statistically significant change at p <0.05. Item p-Value 0.939 0.908 0.215 0.204 0.883 0.462 0.746 0.392 0.273 Pin Index Safety System Configuration for Oxygen Students in both groups were asked to identify the pin index safety system configuration for oxygen. In the subject group, all students (100%) selected the correct answer (2,5). In the control group, all but one student (90%) selected the correct answer. Safe Delivery of Anesthesia Care In a multiple-choice format questionnaire, students in both groups were asked to identify which answer was not a requirement for safe delivery of anesthesia care. In the subject group, all students (100%) selected the correct answer (uncontrolled release of positive pressure from the breathing circuit). In the control group, all but one student (90%) selected the correct answer. Piped Gas Pressure All students in both groups were able to verify that piped gas pressures are 50 or greater psi/g. Knowing When Breathing Circuit System Pressure and Leak Test Should be Conducted Both groups of students were asked to select when a breathing circuit system pressure and leak test should be conducted. Correct answers included: prior to each use, if any component SIMULATION FOR ANESTHESIA MACHINE SET-UP 25 of the circuit is changed, and daily. All students in both groups correctly selected daily. In the control group only 70% correctly selected if any component of the circuit is changed whereas all students in the subject group selected the correct answer (100%). For daily, 50% of students in the control group answered correctly compared to 75% in the subject group. Ensuring Anesthesia Equipment Finally, both groups of students were asked to select who was ultimately responsible for ensuring the anesthesia machine was ready for use, with the correct answer being The Anesthesia provider. In the subject group 67% correctly answered the question while the other 33% answered the Certified Registered Nurse Anesthetist. In the control group 63% of the responses answered correctly and 36% selected the Certified Registered Nurse Anesthetist. Qualitative Findings The students in the subject group were asked a final question which was to describe their experience with the anesthesia machine set-up simulation. A recurrent theme in the students responses was that the simulation allowed them to troubleshoot common anesthesia machine setup problems that they may encounter in the clinical setting. One student responded that this simulation was a good exercise to help students independently trouble shoot common anesthesia problems. Additionally, another response from the subject group stated that trouble shooting helped cement concepts and encourage problem solving in the simulation. Another common theme in the student responses was that the anesthesia machine set-up simulation provided them an opportunity to critically think through the problems and apply the knowledge they have gained from the book to an actual problem during the simulation. A response from one of the participants stated that the simulation allowed opportunity for critical thinking and problem-solving skills to be applied to learning the task. Finally, a student replied SIMULATION FOR ANESTHESIA MACHINE SET-UP 26 that the simulation made me apply what I have learned from the course instead of just regurgitating it. Discussion Checklists are an important aspect of patient safety in the operating room, and similarly important for the delivery of anesthesia care. The anesthesia machine set-up is a foundational skill that all CRNAs must know, because although there may be variations in machines, proper anesthesia machine set-up will prepare the CRNA to provide the best possible care for a patient. This intervention was chosen to build confidence in first year SRNAs. Hanshaw and Dickerson (2020) found that with simulation-based training, it is possible to build a students confidence before and during their interactions throughout clinical practice. In addition to building confidence, simulation practice also decreases practitioner anxiety when in the clinical setting (Ballister, 2018; Hanshaw & Dickerson, 2020). In general, the subject group participants demonstrated a more positive outlook on performing the anesthesia machine set-up simulation over the standard anesthesia machine checkoff. An interesting point of discussion is the subject groups lower confidence rating in their post survey after the anesthesia machine set-up simulation. The subject group had a lower score in the post simulation survey, which may be related to the participants predicting the simulation to be similar to the checkoff and performed more poorly than anticipated. Although their performance was not graded for this simulation, the students may have been discouraged by their personal performance and comfort with the anesthesia machine set-up. Strengths Simulation is a versatile teaching strategy that is suitable for learners with different styles of learning. This anesthesia machine set-up simulation utilized existing resources at Marian SIMULATION FOR ANESTHESIA MACHINE SET-UP 27 University with a focus on improving educational practices already employed in the education of SRNAs. The outcome measures of this project reinforce simulation as a vital teaching method for future SNRAs at Marian University and beyond. As stated above, the responses from the subject group provide stronger support for including more simulation in the curriculum for the training of SRNA students. Limitations and Recommendations There were several important limitations of this project. The sample population consisted of a small, homogenous sample from one mid-west Certified Registered Nurse Anesthetist program. Increasing the knowledge relating simulation to CRNA training will require replicating the project over several semesters and conducting the project at different sites may expand the value of this project. Additionally, presenting the qualitative responses from the subject group about the anesthesia set-up simulation to other anesthesia programs will encourage the incorporation of more simulation in SRNA curriculums. This project focused on incorporating more simulation into CRNA education in addition to the current checkoff method. With this being one of the first simulations for first year SRNA students, and the perceived difficulty of the simulation is what may have resulted in a lower confidence rating of the subject group versus the control group who only participated in the standard check-off. A pre-test for the subject group would prove beneficial for future studies to compare pre-test confidence and post-test confidence with the anesthesia machine set-up simulation. Implications for Future Practice The research question posed by this anesthesia machine set-up simulation evaluated if first year SRNA participation would result in increased knowledge, confidence, and clinical SIMULATION FOR ANESTHESIA MACHINE SET-UP 28 judgement. The project aims to augment current teachings and improve the ability for students to understand and act on cues to accurately interpret issues that may arise during an anesthesia machine set-up. Findings of the anesthesia machine set-up simulation project suggest an increased satisfaction after the simulation, evident by the student surveys and responses. Simulation provides students with an opportunity to utilize active learning in a safe environment with varying, but appropriate, degrees of complexity. Simulation provides participants the opportunity to receive post-experience feedback immediately to enhance understanding and improve future outcomes. Finally, development and improvement of anesthesia machine set-up will only further enhance the knowledge and confidence of future CRNAs and improve patient safety and care. SIMULATION FOR ANESTHESIA MACHINE SET-UP 29 References Ballister, M. (2018). Student registered nurse anesthetists: Impact of structured high-fidelity simulation on anesthesia ready time. Anesthesia EJournal, 6(2), 7-11. Cannon-Diehl, M., Rugari, S., & Jones, T. (2012). High-Fidelity simulation for continuing education in nurse anesthesia. AANA Journal, 80(3), 191-196. Council on Accreditation. (2020, November). The value of simulation in nurse anesthesia education. https://www.coacrna.org/wp-content/uploads/2020/01/COA-ResponseRegarding-the-Use-of-Simulation.pdf Fletcher, J. (1995). AANA journal course: Update for nurse anesthetistsanesthesia simulation: A tool for learning and research. Journal of the American Association of Nurse Anesthetists 63(1), 61-67. Grota, P. G., & O'Neal, C. (2020). Using international nursing association for clinical simulation and learning standards to evaluate the rigor of high-fidelity simulation learning experiences. Clinical Simulation in Nursing, 46, 5056. https://doi.org/10.1016/j.ecns.2020.05.001 Hanshaw, S. L., & Dickerson, S. S. (2020). High fidelity simulation evaluation studies in nursing education: A review of the literature. Nurse Education in Practice, 46, 102818. doi:10.1016/j.nepr.2020.102818 Hegland, P. A., Aarlie, H., Strmme, H., & Jamtvedt, G. (2017). Simulation-based training for nurses: Systematic review and meta-analysis. Nurse Education Today, 54, 6-20. doi:10.1016/j.nedt.2017.04.004 SIMULATION FOR ANESTHESIA MACHINE SET-UP 30 Hustad, J., Johannesen, B., Fossum, M., &; Hovland, O. J. (2019). Nursing students transfer of learning outcomes from simulation-based training to clinical practice: A focus-group study. BMC Nursing, 18. doi:10.1186/s12912-019-0376-5 INACSL Standards Committee. (2016a). INACSL standards of best practice: SimulationSM: debriefing. Clinical Simulation in Nursing, 12(S), S21-S25. http://dx.doi.org/10.1016/ j.ecns.2016.09.008 INACSL Standards Committee. (2016b). INACSL standards of best practice: SimulationSM: Simulation design. Clinical Simulation in Nursing, 12(S), S5-S12. https://doi.org/10.1016/j.ecns.2016.09.005 Jeffries, P. R. (2005). A framework for designing, implementing, and evaluating simulations used as teaching strategies in nursing. Nursing Education Perspectives, 26(2), 96-103. Jeffries, P. R., Rodgers, B., & Adamson, K. (2015). NLN Jeffries simulation theory: Brief narrative description. Nursing Education Perspectives, 36(5), 292293. Kim, J., Park, J., & Shin, S. (2016). Effectiveness of simulation-based nursing education depending on fidelity: A meta-analysis. BMC Medical Education, 16. doi:10.1186/s12909016-0672-7 Mcmullen, M., Wilson, R., Fleming, M., Mark, D., Sydor, D., Wang, L., Zamora, J., Phelan, R., & Burjorjee, J. E. (2016). Debriefing-on-demand. Simulation in healthcare: The journal of the society for simulation in healthcare, 11(3), 157-163. doi:10.1097/sih.0000000000000140 National League of Nursing. (2021). Student satisfaction and self-confidence in learning. National League of Nursing. http://www.nln.org/docs/default-source/default-documentlibrary/instrument-1_educational-practices-questionnaire.pdf?sfvrsn=0. SIMULATION FOR ANESTHESIA MACHINE SET-UP 31 Shields, J., & Gentry, R. (2020). Effect of Simulation Training on Cognitive Performance Using Transesophageal Echocardiography. https://nurseanesthesiology.aana.com/wpcontent/uploads/2020/03/Shields-R.pdf Sittner, B. J., Aebersold, M. L., Paige, J. B., Graham, L. L., Schram, A. P., Decker, S. I., & Lioce, L. (2015). INACSL standards of best practice for simulation: Past, present, and future. Nursing Education Perspectives, 36(5), 294-298. doi:10.5480/15-1670 SIMULATION FOR ANESTHESIA MACHINE SET-UP Appendix A 32 Literature Review Matrix # Reference (APA) Ballister, M. (2018). Student registered nurse anesthetists: Impact of structured high-fidelity simulation on anesthesia ready time. Anesthesia EJournal, 6(2), 7-11. Level of Evidence Level 7 Literature Review Matrix Research Variables Question/Statement Does HFS increase basic One class with skill proficiency in structured HFS SRNAs with an training and one evaluation within the first without month of clinical training Sample Instruments Results 2 consecutive classes of SRNA students Iowa Model of evidence-based practice Structured HFS did not impact the anesthesia ready time of new-topractice SRNAs. However, the information collected during implementation of HFS and data analysis can be used to develop future avenues to improve current processes for structured HFS and clinical training opportunities. Best evidence does not exist regarding a specific place for HFS among experienced nurse anesthetists. A knowledge gap persists related to exactly how and when to use HFS as an effective teaching and learning method for continuing education among practicing nurse anesthetists. The field of anesthesia simulation is in its infancy. The simulators themselves should become more sophisticated as technology improves and becomes more costeffective. 1 Cannon-Diehl, M., Rugari, S., & Jones, T. (2012). High-Fidelity simulation for continuing education in nurse anesthesia. AANA Journal, 80(3), 191-196. Level 3 What is the usefulness of high-fidelity simulation (HFS) as a valuable tool for continuing education. Age, Years of practice, employment status, Simulation experience 22 Practicing Nurse anesthetists Survey Questions Fletcher, J. (1995). AANA journal course: Update for nurse anesthetistsanesthesia simulation: A tool for learning and research. Journal of the American Association of Nurse Anesthetists 63(1), 61-67. Level 5 High fidelity simulators are a new technology for training and research in anesthesiology. NA NA NA 2 3 SIMULATION FOR ANESTHESIA MACHINE SET-UP Appendix A cont. 4 5 Grota, P. G., & O'neal, C. (2020). Using international nursing association for clinical simulation and learning standards to evaluate the rigor of high-fidelity simulation learning experiences. Clinical Simulation in Nursing, 46, 5056. https://doi.org/10.1016/j.ecns.2020.05.001 Level 3 Hanshaw, S. L., & Dickerson, S. S. (2020). High fidelity simulation evaluation studies in nursing education: A review of the literature. Nurse Education in Practice, 46, 102818. doi: 10.1016/j.nepr.2020.102818 Level 1 The primary purpose of this article is to share an approach applying The International Nursing Association for Clinical Simulation and Learning (INACSL) Standards to support the substitution of two-hour traditional clinical with one hour of high-quality, rigorous simulation or SLE as determined by faculty governance. What is the state of the science on the evidence of learning outcomes in high-fidelity simulation in undergraduate nursing education? 33 Different simulationbased learning experiences Eight SLEs across courses were evaluated using the rubric in blinded peer review. Kolbs theory of experiential learning, INACSL standards of best practice SimulatonSM, Evaluation rubric The rubric is an effective way to standardize plans for high quality SLE. Further studies of validity and reliability need to be conducted. Sample size, sex, level year in nursing program, Knowledge performance/time, Cognitive knowledge, Sim Fidelity knowledge 20 research studies focusing on evaluating outcomes of high-fidelity simulation in undergraduate pre-licensure baccalaureate nursing students. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Design of high-fidelity simulation in the nursing literature is being grounded in outcome variables and increasingly based in pedagogical and nursing judgment theoretical framework. Varied learning outcomes relating to simulation learning have positive outcomes, although there is a lack of comprehensiveness related to practice outcomes. A deeper understanding of the lived experience of high-fidelity simulation is needed to look at retention of learning and transference into nursing clinical practice. SIMULATION FOR ANESTHESIA MACHINE SET-UP Appendix A cont. 6 Hegland, P. A., Aarlie, H., Strmme, H., & Jamtvedt, G. (2017). Simulation-based training for nurses: Systematic review and meta-analysis. Nurse Education Today, 54, 6-20. doi:10.1016/j.nedt.2017.04.004 Level 1 The aim of this systematic review is to evaluate effect of simulation-based training on nurses' skills and knowledge Heterogeneity of the studies Fifteen RCTs Data was analyzed through metaanalysis and narrative syntheses. GRADE was used to assess the quality of evidence Simulation training appears to be an effective strategy to improve nurses' skills, but further good-quality RCTs with adequate sample sizes are needed. Hustad, J., Johannesen, B., Fossum, M., &; Hovland, O. J. (2019). Nursing students transfer of learning outcomes from simulation-based training to clinical practice: A focus-group study. BMC Nursing, 18(1). doi:10.1186/s12912-019-0376-5 Level 3 The aim of this study was to explore nursing students experiences of simulation-based training and how the students perceived the transfer of learning to clinical practice. Age, Gender, Degree year, types of clinical placement Eight focus group interviews were conducted with a total of 32 s- and third-year nursing students who participated in a simulationbased training organized as preparation for clinical placement Transcribed interviews were analyzed with thematic analysis. INACSL Standards Committee (2016, December). INACSL standards of best practice: SimulationSM: Debriefing. Clinical Simulation in Nursing, 12(S), S21-S25. http://dx.doi.org/10.1016/ j.ecns.2016.09.008 INACSL Standards Committee (2016, December). INACSL standards of best practice: SimulationSM: Simulation design. Clinical Simulation in Nursing, 12(S), S5-S12. http://dx.doi.org/10.1016/ j.ecns.2016.09.005. Level 7 Reflection of experiences during simulation can lead to new interpretations by participants. Standardized simulation design provides a framework for developing effective simulation-based experiences. The design of simulation-based experiences incorporates best practices from adult learning, education, instructional design, clinical standards of care, evaluation, and simulation pedagogy. NA NA INACSL 2016 Standards of Best Practice This study revealed students transfer of learning outcomes from simulation-based training to clinical practice. The students experiences of the simulation-based training remain as enduring and conscious learning outcomes throughout their completion of clinical practice. NA NA NA INACSL 2016 Standards of Best Practice 7 8 9 34 Level 7 NA SIMULATION FOR ANESTHESIA MACHINE SET-UP Appendix A cont. 10 11 INACSL Standards Committee (2016, December). INACSL standards of best practice: SimulationSM: Outcomes and objectives. Clinical Simulation in Nursing, 12(S), S13-S15. http://dx.doi.org/10.1016/j.ecns.2016.09.006 Level 7 INACSL Standards Committee (2016, December). INACSL standards of best practice: SimulationSM: Participant evaluation. Clinical Simulation in Nursing, 12(2), S26-S29. http://dx.doi.org/10.1016/j.ecns.2016.09.009 Level 7 Kim, J., Park, J., & Shin, S. (2016). Effectiveness of simulation-based nursing education depending on fidelity: A metaanalysis. BMC Medical Education, 16(1). doi:10.1186/s12909-016-0672-7 Level 1 Mcmullen, M., Wilson, R., Fleming, M., Mark, D., Sydor, D., Wang, L., Zamora, J., Phelan, R., & Burjorjee, J. E. (2016). Debriefing-ondemand. Simulation in Healthcare: The Journal of the Society for Simulation in Healthcare, 11(3), 157-163. doi:10.1097/sih.0000000000000140 Level 2 12 13 35 The Kirkpatrick Model is a commonly used ranking model that evaluates training programs and transfer of learning outcomes. These outcomes are an integral component of instructional and research design. Formative evaluation of the participants fosters personal and professional development, to assist the participant in progression toward achieving objectives or outcomes. The purpose of this study was to determine the effect size of simulationbased educational interventions in nursing and compare effect sizes according to the fidelity level of the simulators through a meta-analysis NA NA INACSL 2016 Standards of Best Practice NA NA NA INACSL 2016 Standards of Best Practice NA Study outcomes included learning and reaction outcomes. Learning outcomes were categorized into three domains: cognitive, psychomotor, and affective. 40 selected studies were performed by using the Case Control Study Checklist developed by the Critical Appraisal Skills Programmed This study was planned and conducted in adherence to PRISMA standards [6] of quality for reporting metanalysis. Simulation is an effective tool in medical education with debriefing as the cardinal educational component. Alternate debriefing strategies might further enhance the educational value of simulation Debriefing sessions 8 postgraduate year 1 anesthesia residents Convenience Sample These results suggest that simulation-based nursing educational interventions have strong educational effects, with particularly large effects in the psychomotor domain. Since the effect is not proportional to fidelity level, it is important to use a variety of educational interventions to meet all of the educational goals. The debriefing-ondemand approach was successfully incorporated into our simulation scenarios and was well received by this group of junior trainees new to simulation. SIMULATION FOR ANESTHESIA MACHINE SET-UP Appendix A cont. 14 Sittner, B. J., Aebersold, M. L., Paige, J. B., Graham, L. L. M., Schram, A. P., Decker, S. I., & Lioce, L. (2015). INACSL standards of best practice for simulation: Past, present, and future. Nursing Education Perspectives, 36(5), 294298. Level 5 Describe the historical evolution of the international Nursing Association for Clinical Simulation and Learning's Standards of Best Practice: Simulation 36 NA Journal publications, INACSL website and surveys and reports from INACSL Standards Committee Comprehensive review Simulation standards will continue to evolve as the science of simulation advances SIMULATION FOR ANESTHESIA MACHINE SET-UP Appendix B 37 Jeffries Simulation Model "Jeffries Simulation Model," by P. R. Jeffries, 2005, Nursing Education Perspectives, 26(2), 96103. (https://journals.lww.com/neponline/pages/articleviewer.aspx?year=2005&issue=03000&article=00009&type=abstract) Copyright 2005 by National League for Nursing Inc. SIMULATION FOR ANESTHESIA MACHINE SET-UP Appendix C Student Satisfaction and Self-Confidence in Learning 38 SIMULATION FOR ANESTHESIA MACHINE SET-UP Appendix D Demographics Questionnaire 39 SIMULATION FOR ANESTHESIA MACHINE SET-UP Appendix E Modified Student Satisfaction and Self Confidence in Learning 40 SIMULATION FOR ANESTHESIA MACHINE SET-UP Appendix E cont. 41 SIMULATION FOR ANESTHESIA MACHINE SET-UP Appendix E cont. 42 SIMULATION FOR ANESTHESIA MACHINE SET-UP Appendix F Knowledge Assessment Questionnaire 43 SIMULATION FOR ANESTHESIA MACHINE SET-UP Appendix G IRB Exemption Form 44 SIMULATION FOR ANESTHESIA MACHINE SET-UP 45 Appendix H Table 2 Helpfulness of Teaching Methods Used in Machine Set-Up Checkoff/Simulation Helpfulness of Teaching Methods Control Group Strongly Disagree Disagree Undecided Agree Strongly Agree n % 0 2 0 6 2 0.0 20.0 0.0 60.0 20.0 Subject Group Strongly Disagree Disagree Undecided Agree Strongly Agree 0 0 0 3 9 0.0 0.0 0.0 25.0 75.0 Note. n=22 Table 3 Learning Materials and Activities to Promote Learning for the Anesthesia Machine Set-Up Checkoff Promotion of Learning Control Group Strongly Disagree Disagree Undecided Agree Strongly Agree n % 0 1 1 6 2 0.0 10.0 10.0 60.0 20.0 Subject Group Strongly Disagree Disagree Undecided Agree Strongly Agree 0 1 1 5 5 0.0 8.3 8.3 41.7 41.7 Note. n=22 SIMULATION FOR ANESTHESIA MACHINE SET-UP 46 Appendix H cont. Table 4 Enjoyment of Simulation Teaching Methods Enjoyment of Simulation Teaching Methods Control Group Strongly Disagree Disagree Undecided Agree Strongly Agree n % 0 2 3 3 2 0.0 20.0 30.0 30.0 20.0 Subject Group Strongly Disagree Disagree Undecided Agree Strongly Agree 0 0 0 4 8 0.0 0.0 0.0 33.3 66.7 Note. n=22 Table 5 Teaching Materials Motivated Students and Enhanced Learning Motivating and Helpful Control Group Strongly Disagree Disagree Undecided Agree Strongly Agree n % 0 3 1 4 2 0.0 30.0 10.0 40.0 20.0 Subject Group Strongly Disagree Disagree Undecided Agree Strongly Agree 0 0 1 4 7 0.0 0.0 8.3 33.3 58.3 Note. n=22 SIMULATION FOR ANESTHESIA MACHINE SET-UP 47 Appendix H cont. Table 6: Simulation Teaching Methods Suitable to Learning Style Simulation Teaching Methods Suitable to Learning Style Control Group Strongly Disagree Disagree Undecided Agree Strongly Agree n % 0 1 2 5 2 0.0 10.0 20.0 50.0 20.0 Subject Group Strongly Disagree Disagree Undecided Agree Strongly Agree 0 0 1 3 8 0.0 0.0 8.3 25.0 66.7 Note. n=22 Table 7 Confidence in Mastering the Content of the Simulation Activity Confidence in Mastering the Content of the Simulation Activity Control Group Strongly Disagree Disagree Undecided Agree Strongly Agree n % 0 0 2 6 2 0.0 0.0 20.0 60.0 20.0 Subject Group Strongly Disagree Disagree Undecided Agree Strongly Agree 0 1 1 8 2 0.0 8.3 8.3 66.7 16.7 Note. n=22 SIMULATION FOR ANESTHESIA MACHINE SET-UP 48 Appendix H cont. Table 8 Confidence that the Checkoff/Simulation Covered Critical Content Confidence that Simulation Covered Critical Content Control Group Strongly Disagree Disagree Undecided Agree Strongly Agree n % 0 0 2 5 3 0.0 0.0 20.0 50.0 30.0 Subject Group Strongly Disagree Disagree Undecided Agree Strongly Agree 0 0 1 9 2 0.0 0.0 8.3 75.0 16.7 Note. n=22 Table 9 Confidence in Developing the Skills and Obtaining the Knowledge from the Simulation to Perform Anesthesia Machine Setup Confidence in Developing Skills and Obtaining Knowledge Control Group Strongly Disagree Disagree Undecided Agree Strongly Agree n % 0 0 2 6 2 0.0 0.0 20.0 60.0 20.0 Subject Group Strongly Disagree Disagree Undecided Agree Strongly Agree 0 0 0 8 4 0.0 0.0 8.3 66.7 33.3 Note. n=22 SIMULATION FOR ANESTHESIA MACHINE SET-UP 49 Appendix H cont. Table 10 Helpfulness of Resources Instructors Used to Teach Checkoff/Simulation Helpfulness of Resources Instructors Used to Teach Simulation Control Group Strongly Disagree Disagree Undecided Agree Strongly Agree n % 0 3 0 4 3 0.0 30.0 0.0 40.0 30.0 Subject Group Strongly Disagree Disagree Undecided Agree Strongly Agree 0 0 0 7 5 0.0 0.0 0.0 58.3 41.7 Note. n=22 Table 11 Student Responsibility to Learn From the Checkoff/Simulation Activity Student Responsibility to Learn From the Simulation Activity Control Group Strongly Disagree Disagree Undecided Agree Strongly Agree n % 0 1 0 4 4 0.0 10.0 0.0 40.0 40.0 Subject Group Strongly Disagree Disagree Undecided Agree Strongly Agree 0 0 1 4 7 0.0 0.0 8.3 33.3 58.3 Note. n=22 SIMULATION FOR ANESTHESIA MACHINE SET-UP 50 Appendix H cont. Table 12 Knowing How to Get Help with Confusing Concepts Knowing How to Get Help with Confusing Concepts Control Group Strongly Disagree Disagree Undecided Agree Strongly Agree n % 0 0 0 6 4 0.0 0.0 0.0 60.0 40.0 Subject Group Strongly Disagree Disagree Undecided Agree Strongly Agree 0 0 0 9 3 0.0 0.0 0.0 75.0 25.0 Note. n=22 Table 13 Using Simulation Activities to Learn Critical Aspects of Anesthesia Machine-Set-Up Using Simulation Activities to Learn Critical Aspects of Set-up Control Group Strongly Disagree Disagree Undecided Agree Strongly Agree n % 0 0 0 8 2 0.0 0.0 0.0 80.0 20.0 Subject Group Strongly Disagree Disagree Undecided Agree Strongly Agree 0 1 0 7 4 0.0 8.3 0.0 58.3 33.3 Note. n=22 SIMULATION FOR ANESTHESIA MACHINE SET-UP 51 Appendix H cont. Table 14 Responsibility of Instructors to Educate on Simulation during Class Responsibility of Instructors to Educate on Simulation Control Group Strongly Disagree Disagree Undecided Agree Strongly Agree n % 1 0 2 5 2 10.0 0.0 20.0 50.0 20.0 Subject Group Strongly Disagree Disagree Undecided Agree Strongly Agree 0 1 1 6 4 0.0 8.3 8.3 50.0 33.3 Note. n=22  ...

... 1 DEBRIEF AFTER SIMULATION Marian University Leighton School of Nursing Doctor of Nursing Practice Final Project Report for Students Graduating in May 2022 Debriefing After a Difficult Airway Algorithm Simulation Connor Curley Marian University Leighton School of Nursing Chair: Bradley Stelflug, DrAP, MBA, CRNA _________________________ (Signature) Date of Submission: June 13, 2021 2 DEBRIEF AFTER SIMULATION Introduction The difficult airway algorithm is a necessary technique that must be mastered by all student nurse anesthetists. Students in Marian Universitys Nurse Anesthesia Program are taught the potentially life-saving skills included in the difficult airway algorithm. This project added a debriefing component to the simulation training usually conducted in the program. The purpose is to prove that debriefing increases their knowledge on the subject, enhances their confidence and satisfaction when applying difficult airway situations. Background Failure to properly implement the difficult airway algorithm can lead to hypoxic brain injury or death of the patient in a very short time. Issues from the lack of learning the difficult airway algorithm include delayed recognition of critical events, decreased confidence and high anxiety when faced with not being able to breath for the patient. It would be rare for a student registered nurse anesthetist to have experienced a cannot intubate cannot ventilate (CICV) situation. A CICV situation is stressful for even the most experienced anesthesia providers that have practiced for many years. These situations escalate very quickly, and immediate action must be taken. Delayed reaction or incompetent skills when managing a CICV situation can lead to death of the patient. While in school, students often learn of the difficult airway algorithm. Frequent practice and memorization of each step is vital. Inexperience is a major factor that can lead to a hypoxic brain injury when managing a CICV situation. Providing a debrief to the simulation experience may help to enhance students knowledge, confidence and skills when faced with a difficult airway situation. Simulation training has shown that they can have an effect on improving patient outcomes and possibly malpractice claims (Kurup, et.al, 2017). Simulations can provide 3 DEBRIEF AFTER SIMULATION scenarios that a student may not encounter during school or even years after graduating. A study by the National Council of State Boards of Nursing, NCSBN, in 2014, found that replacing clinical experience with simulation experience resulted in no significant difference in clinical competency (Hayden et. al, 2014). By providing a debriefing after the difficult airway algorithm simulation without replacing clinical experience should further the clinical competence of the students. An effective simulation is needed in order to educate students appropriately. The International Nursing Association for Clinical Simulation and Learning (INACSL) standards have created best practice guidelines for simulation practice. These guidelines include a debriefing portion to the simulation. Using these guidelines, an improved simulation experience can be composed that will optimize the education and technical skills of all the students participating in the simulation. Problem Statement Marian University student registered nurse anesthetists in their first year of training are inexperienced and lack the knowledge and confidence to manage a difficult airway situation. A cannot intubate, cannot ventilate simulation scenario can be created to mimic what an anesthesia provider may experience in the clinical setting using the INACSL standards of best practice guidelines. Gap Analysis Currently the Marian University Nurse Anesthesia program provides simulations to students, but these simulations are not designed using a debrief component. Therefore, students may lack important feedback after the simulation that may make them more competent providers of the difficult airway algorithm. The INACSL standards include a debrief component as part of 4 DEBRIEF AFTER SIMULATION their evidence-based best practice. Adding the debrief component may heighten the experience satisfaction, knowledge and self-confidence to those who participate. Review of Literature Search Methodology Articles were located on PubMed and Google Scholar. The search was narrowed down to articles with publication dates within the last seven years. Difficult airway algorithm, highfidelity, cannot intubate cannot ventilate (CICV), simulation training, knowledge, confidence, satisfaction, debriefing, INACSL standards of best practice, anesthesia, and airway management were the Boolean search phrases used. Thirteen relevant articles were used based on the information they provided. The inclusion criteria for these studies provide simulations using healthcare personnel, provide information regarding knowledge, confidence and/or clinical skill outcomes. Each of these articles had a level of evidence that ranged from a level I to level IV. These articles included simulation training, increased knowledge, confidence and skills. The type of healthcare providers and whether participants were students or practicing providers did not factor into the results of the search. Simulation Simulation use for advancing knowledge, confidence, and clinical skills was a common topic from the selected articles. By providing a simulation scenario to students prior to clinical, practicing healthcare workers enhance their ability to perform the sought-after task (Offiah, G., et al., 2019). Many of these studies implement the use of high-fidelity simulations. High fidelity simulations use a life-like manikin that is able to reproduce human physiology. Practicing medical residents that have difficult airway management simulations throughout their residency have improvement with compliance with guidelines (Hubert, et. al, 2014). When it comes to 5 DEBRIEF AFTER SIMULATION advanced airway management, simulation training is by far superior to no simulation training at all (Kennedy et. al, 2014). Simulations have been shown to be an ample substitution to clinical experience in nursing training if clinical is unavailable. By splitting nursing students clinical hours and simulation hours to a 50:50 ratio there is not a statistically significant difference in clinical knowledge or competency compared to students that spend all their hours in clinical and none in simulation (Hayden, et. al, 2014). Students that have had high-fidelity simulation training of the CICV scenario have a shorter decision-making time than those students that have not participated in a simulation (McCrossin, 2014). Knowledge Increased knowledge and education is one of the major goals of simulation training for anesthesia residents (Yunoki, K., & Sakai, T., 2018). Offering simulation compared to no simulation, provides learners an opportunity to get hands-on experience prior to entering the clinical setting. For learners that have less didactic and clinical experience, such as underclassmen, these simulations are highly recommended (Erlinger, 2019). Many studies seek the best way to improve knowledge and compare simulation to other methods of teaching. When comparing high fidelity simulation training to computer-based case studies, the simulation was superior in enhancing the students knowledge and skills in Mejias 2018 study regarding treatment of malignant hyperthermia. Students that experience high-fidelity simulations display and report an increased knowledge compared to students that only have traditional learning (DSouza, et. al, 2017). A systematic review by Alanazi, et. al (2017), concluded that evidence from many different studies using high fidelity simulations in education of healthcare students significantly improves knowledge, skills and self-confidence. All of which are extremely important when implementing the difficult airway algorithm. 6 DEBRIEF AFTER SIMULATION Confidence Confidence is of utmost importance when it comes to caring for a patient in any healthcare setting. Whether debriefing after simulation experience improves confidence has been one of the major outcomes that many of these articles seek to ascertain. The results of these articles all have similar outcomes of increased confidence. When nursing students are put through a high-fidelity simulation with a debriefing segment, they demonstrate advanced skills, critical thinking and a greater self-confidence in caring for patients (Samawi, 2014). Wellconstructed simulation learning with adequate debriefing can help not only students with little to no experience, but practicing healthcare providers such as physicians and nurses that experience a simulation scenario have shown a great improvement in knowledge and confidence (Boiling& Hardin-Pierce, 2016). To decide when to employ the difficult airway algorithm, the provider must be quick and confident. Anesthesia trainees that participate in high-fidelity simulation compared to those that do not, show an enhanced decision-making time and confidence (McCrossin, et. al, 2014). Therefore, it is important to determine whether adding a debriefing segment to the simulation training experience will enhance the participants confidence of the difficult airway algorithm. Theoretical Framework The difficult airway algorithm simulation with debrief component will be based on the INACSL standards. The INACSL standards use the NLN Jeffries Simulation Theory. This theory starts by finding a need for a well-designed simulation-based experience, such as the difficult airway algorithm. The NLN Jeffries Simulation Theory then seeks to use the desired goals and outcomes to influence how the simulation will be designed. Once the goals and outcomes are determined, the design of the simulation must factor in how the facilitator and the participants 7 DEBRIEF AFTER SIMULATION can interact with each other throughout the project. For this difficult airway algorithm simulation, a debriefing session will be implemented by the facilitator and participant. This debriefing session aims to enhance confidence, knowledge and satisfaction. The NLN Jeffries Simulation Theory emphasizes key concepts which include environment, trust, collaboration, experience, how much the design is interactive, and learner centered. The simulation should be hosted in a non-judgmental, learning-centered environment and facilitated by trusted investigators. Collaboration and interaction between the participants and the investigators must be appropriate to the situation. Once the simulation is created, the key concepts can be provided to the participants. Goals, Objectives, Expected Outcomes The goal of this simulation is to determine whether debriefing increases knowledge, confidence and satisfaction when applying the difficult airway algorithm. It is expected that students who are given a debrief component to their simulation training will show a higher level of knowledge and confidence when applying the difficult airway algorithm, in addition to an increased overall satisfaction with the simulation experience. Project Design/Methods Twenty-four Marian University Nurse Anesthesia students in their first year of the program were randomly divided into two groups. The control group participated in the difficult airway simulation training that was currently used in the Marian program. The experimental group participated in the same simulation with the addition of a debrief component. then the data was compared and analyzed. Active participation followed by a debriefing session for the experimental group. An evaluation via a confidence and satisfaction survey and a five-question knowledge-based posttest was delivered to the control group immediately after active 8 DEBRIEF AFTER SIMULATION participation and to the experimental group after the debriefing session. Once completed by both groups the data was compared and analyzed. After the control group finished their survey and posttest, they were permitted to have a debriefing session. Project site This projects setting was the Marian University simulation laboratory. This site allowed access to resources including a high-fidelity manikin, a ventilator, Ambu-bag and mask, direct laryngoscopy blades and handles, video-laryngoscopy equipment, laryngeal mask airways, and other readily available airway equipment. This site has trained personnel that were able to run the high-fidelity manikin and personnel that were able to help the participants if they required assistance. Measurement Instruments Participants were provided a rubric to follow prior to the simulation. Throughout the simulation all the students were evaluated on their ability at applying the difficult airway algorithm graded by a checklist. Then all the students were tested using the National League for Nursing (NLN) Survey which gathers participants perceptions of their satisfaction and selfconfidence with their simulation experience. This questionnaire examines the participants satisfaction and self-confidence and satisfaction from the simulation using twenty-three questions concerning satisfaction and thirteen for self-confidence. A five-question knowledgebased test was provided to each group. Data Analysis Demographics such as age and gender were collected and analyzed for both sample groups. Data collected from the questionnaires was analyzed using median age and experience. Then the results were compared between the control group and experimental group. 9 DEBRIEF AFTER SIMULATION Findings and Results The difficult airway algorithm simulation was scheduled for two different days. Twentyfour students participated in the simulation. Twelve students on day one (Control group) and 12 students on day two (experimental group) were given 30-minute blocks to complete the simulation. Every participant filled out an agreement form for the simulation and answered three different demographic questions prior to starting the simulation. Students were provided the same scenario regarding the difficult airway algorithm. The control group completed the knowledge-based posttest and the confidence and satisfaction survey after they completed the simulation training. The experimental group were given a debriefing session immediately after the simulation training. The debriefing session allowed the participant to discuss how he/she did during the simulation, in an environment conducive to learning and allowed the participant to speak openly. The experimental participants were asked how they felt they performed during the simulation and what they believed they could improve on. The participants were also asked what their thoughts were regarding how the simulation went and if it was what they expected. After the debriefing, the experimental group completed the same posttest and survey that the control group completed. Demographics Of the twenty-four participants, sixty-six percent were female (16); ages ranged from twenty-five to fifty-five years old with the median age of 30.5 and mean age of 33. All participants had previous nursing experience; experience ranged from three years to twenty years with the median experience of six years and mean of 6.79 years. Descriptive Female Male Average Age Average Years of Experience 10 DEBRIEF AFTER SIMULATION Control Group Experimental Group 9 7 3 5 33.33 32.75 7.5 5.833 Objective One: Difference in Participant Knowledge Objective one was to determine whether debriefing after a difficult airway algorithm made a significant difference in the knowledge of the participants. Results of the knowledgebased posttest were analyzed using JASP software system, which is a software system that analyzes data and proves whether there is statistical significance between groups via the paired ttest. A paired sample t-test was performed to compare the knowledge of students that were involved in the debriefing and those that did not participate in the debriefing. Paired t-tests compare two variables of the same subject to determine whether there is a significant difference. when looking for the significant difference the p-value must be less than or equal to 0.05. Since the control group test scores (M=3.667, SD=0.778) were higher than the test scores of the experimental group (M=3.0, SD=1.206), there was no significant difference between the groups in participant knowledge. Figure 1-A- Descriptive Statistics Descriptive Statistics Control Group Test Scores Valid 12 Missing 12 Mean 3.667 Median 3.500 Std. 0.778 Deviation Minimum 3.000 Maximum 5.000 Experimental Group Test Scores 12 12 3.000 3.000 1.206 1.000 5.000 11 DEBRIEF AFTER SIMULATION Note- valid-number of participants used in group, missing- number of participants not used from total, mean- average number of all data points, median- middle value of all data points, standard deviation-how dispersed data is from mean, minimum- lowest score, maximum- highest score. Figure 1-B- Paired Samples T-Test Paired Samples T-Test Measure 1 Control Group Test Scores Measure 2 - t df p Mean SE Difference Difference Experimental 1.48 1 0.16 Group Test Scores 3 1 6 0.667 0.449 Note. t-value- indicated difference in two sample sets, df (degrees of freedom)- maximum number of logically independent values, p-value- determines whether data is statistically significant enough to accept or reject hypothesis, SE (standard error)- measures accuracy of sample size distribution. Descriptive N Mean SD SE Control Group Test Scores 12 3.667 0.778 0.225 Experimental Group Test Scores 12 3.000 1.206 0.348 The five-question knowledge-based test was created by the DNP student investigator and evaluated for content by two experienced nurse anesthetist faculty. Questions involved knowledge regarding equipment, procedures and timing of the difficult airway algorithm. These questions were evaluated on the number of correct answers. Table 1-A Test Question Control Group Results Experimental Group Results Percent Difference After your first unsuccessful intubation attempt, all of the following should be considered EXCEPT: Answer- Emergency invasive airway Incorrect- call for help, return to spontaneous Correct= 12 Incorrect= 0 100% correct Correct= 9 Incorrect= 3 75% correct 25% worse 12 DEBRIEF AFTER SIMULATION ventilation, wake the patient True or false: After your first unsuccessful intubation attempt and face mask ventilation is not adequate, LMA placement is the next consideration. Answer- true Incorrect-false Examples of an emergency noninvasive airways include (SELECT ALL THAT APPLY): Answer- combitube, king tube Incorrect- nasal rae, retrograde intubation True or false: A King tubes distal end is intended for the esophagus and has two inflation ports one for each balloon. Answer- false Incorrect- true True or false: Retrograde intubation is an example of an invasive airway. Answer- true Incorrect- false Correct= 4 Incorrect= 8 33% correct Correct= 5 Incorrect= 7 42% correct 9% better Correct= 8 Incorrect= 4 66% correct Correct= 8 Incorrect= 4 66% correct No difference Correct= 9 Incorrect= 3 75% correct Correct= 4 Incorrect= 8 33% correct 42% worse Correct= 10 Incorrect= 2 83% correct Correct= 10 Incorrect= 2 83% correct No change Objective Two: Increased Participant Confidence Objective two sought to determine whether the debriefing enhanced the participants confidence and satisfaction of the difficult airway algorithm. Confidence and satisfaction were measured using the NLN Student Satisfaction and Self-Confidence in Learning Survey. After completion of the simulation, and debrief if part of the experimental group, both groups filled out the survey rating their self-confidence and satisfaction with the difficult airway algorithm. A paired sample t-test was used to determine if there was a significant difference (p-value <0.05) in the mean confidence and satisfaction between the two groups. After reviewing the data analysis 13 DEBRIEF AFTER SIMULATION for self-confidence in the control group (M= 35.083, SD4.337) and the experimental group (M= 34.917, SD= 3.343), there was no significant difference between the two groups. Figure 2-A Descriptive Statistics Descriptive Statistics Control group Confidence Valid 12 Missing 12 Mean 35.083 Median 34.500 Std. 4.337 Deviation Minimum 29.000 Maximum 40.000 Experimental Group Confidence 12 12 34.917 34.500 3.343 30.000 40.000 Note- valid-number of participants used in group, missing- number of participants not used from total, mean- average number of all data points, median- middle value of all data points, standard deviation-how dispersed data is from mean, minimum- lowest score, maximum- highest score. Figure 2-B Paired Samples T-Test Paired Samples T-Test Measure 1 Measure 2 Control group Experimental Group Confidence Confidence t df p 0.08 1 0.93 4 1 4 Note. t-value- indicated difference in two sample sets, df (degrees of freedom)- maximum number of logically independent values, p-valuedetermines whether data is statistically significant enough to accept or reject hypothesis Objective Three: Increased Participant Satisfaction The data collected from the survey observing student satisfaction had very similar results as student self-confidence. Results showed that there was no significant difference between the control group (M= 22.33, SD= 2.6) and the experimental group (M= 22.08, SD= 4.0). 14 DEBRIEF AFTER SIMULATION Figure 3-A Descriptive Statistics Descriptive Statistics Control Group Satisfaction Valid 12 Missing 12 Mean 22.333 Median 23.000 Std. 2.605 Deviation Minimum 17.000 Maximum 25.000 Experimental Group Satisfaction 12 12 22.083 23.500 4.033 11.000 25.000 Note- valid-number of participants used in group, missing- number of participants not used from total, mean- average number of all data points, median- middle value of all data points, standard deviation-how dispersed data is from mean, minimum- lowest score, maximum- highest score. Figure 3-B Paired Samples T-Test Paired Samples T-Test Measure 1 Control Group Satisfaction Measure 2 Experimental Group Satisfaction t df p 0.15 1 0.87 8 1 7 Note. t-value- indicated difference in two sample sets, df (degrees of freedom)- maximum number of logically independent values, p-valuedetermines whether data is statistically significant enough to accept or reject hypothesis Limitations, Recommendations and Implications for Change The difficult airway algorithm is an important tool that anesthesia providers must know. This project on the difficult airway algorithm examined the effect that a debriefing session after a simulation experience had on first year anesthesia students. Limitations 15 DEBRIEF AFTER SIMULATION There were multiple limitations that affected this project. The sample population was very small. Twelve students per group limits how accurately the results can be generalized. For future consideration, replication of this project should be conducted using multiple anesthesia cohorts increasing the sample population. The project only incorporated a debriefing session. There was not a baseline of student knowledge, self-confidence or satisfaction prior to the difficult airway algorithm simulation. Consideration of replicating this project would include a pretest that would be used as a reference to compare previous knowledge to what was learned throughout the simulation and debriefing session. Additionally, student simulation experience was unknown. This project was conducted during the COVID-19 pandemic. This caused students to have variable amounts of simulation experience due to access to the simulation lab. Students with less simulation experience could have less self-confidence and satisfaction when participating in this project because of unfamiliarity with equipment and environment. Gathering data regarding past simulation experience would be recommended in future consideration of this project. Implications to Practice Determining whether a debriefing session after a difficult airway algorithm simulation increased knowledge, confidence and satisfaction of first year anesthesia students at Marian University was the primary goal of this project. After completion of this project, it remains unclear whether debriefing changes a student's base knowledge. Results to the knowledge-based portion of the project did not show any significant difference between the two groups. The confidence and satisfaction survey results were also not significantly different enough to show a positive or negative result of debriefing. 16 DEBRIEF AFTER SIMULATION Marian University currently employs a difficult airway simulation that does not utilize a debriefing session after the experience. This debriefing should have increased the participants' clinical competencies (Jeong, K. I., & Choi, J. Y., 2017). However, limitations to this project showed that further research is needed in order to show that a debriefing component increases a students knowledge, confidence, and satisfaction. 17 DEBRIEF AFTER SIMULATION References Alanazi, A. A., Nicholson, N., & Thomas, S. (2017). The use of simulation training to improve knowledge, skills, and confidence among healthcare students: a systematic review. Internet Journal of Allied Health Sciences and Practice, 15(3), 2. Boling, B., & Hardin-Pierce, M. (2016). The effect of high-fidelity simulation on knowledge and confidence in critical care training: An integrative review. Nurse Education in Practice, 16(1), 287-293. doi:10.1016/j.nepr.2015.10.004 DSouza, M.S., Arjunan, P., &Venkatesaperumal,R (2017). International Journal of Medical and Health Sciences Research. High Fidelity Simulation in Nursing Education, International Journal of Medical and Health Sciences Research. 7(7), 340-353. Erlinger, L. R., Bartlett, A., & Perez, A. (2019). High-Fidelity Mannequin Simulation versus Virtual Simulation for Recognition of Critical Events by Student Registered Nurse Anesthetists. AANA journal, 87(2), 105109. Eyikara, E., & Baykara, Z. (2017). The importance of simulation in nursing education. World Journal on Educational Technology: Current Issues, 9(1), 0207. https://doi.org/10.18844/wjet.v9i1.543 Hayden, J. K., Smiley, R. A., Alexander, M., Kardong-Edgren, S., & Jeffries, P. R. (2014). The NCSBN national simulation study: a longitudinal, randomized, controlled study replacing clinical hours with simulation in prelicensure nursing education. Journal of Nursing Regulation, 5(2). doi:10.1016/s2155-8256(15)30062-4 INACSL Standards of Best Practice: SimulationSM Simulation Design. (2016). Clinical Simulation in Nursing, 12. doi:10.1016/j.ecns.2016.09.005 18 DEBRIEF AFTER SIMULATION Hubert, V., Duwat, A., Deransy, R., Mahjoub, Y., & Dupont, H. (2014). Effect of simulation training on compliance with difficult airway management algorithms, technical ability, and skills retention for emergency cricothyrotomy. Anesthesiology, 120(4), 9991008. https://doi.org/10.1097/ALN.0000000000000138 Jeong, K. I., & Choi, J. Y. (2017). Journal of Korean Academy of Nursing, 47(6), 842853. https://doi.org/10.4040/jkan.2017.47.6.842 Kennedy, Cassie C. MD, FCCP1; Cannon, Eric K. MD2; Warner, David O. MD2; Cook, David A. MD, MHPE, FACP3, (2014). Advanced airway management simulation training in medical education, critical care medicine: - Volume 42 - Issue 1 - p 169-178 doi: 10.1097/CCM.0b013e31829a721f Kurup, V., Matei, V., & Ray, J. (2017). Role of in-situ simulation for training in healthcare: opportunities and challenges. Current opinion in anesthesiology, 30(6), 755760. https://doi.org/10.1097/ACO.0000000000000514 Lee, JuHee et al. (2020) Debriefing methods and learning outcomes in simulation nursing education: A systematic review and meta-analysis. Nurse education today vol. 87 : 104345. doi:10.1016/j.nedt.2020.104345 McCrossin, K., White, H. T., & Sane, S. (2014). The effect of high-fidelity simulation on the confidence and decision-making ability of anaesthesia trainees in managing subsequent simulated 'can't intubate, can't oxygenate' scenarios. Anaesthesia and intensive care, 42(2), 207212. Meja V, Gonzalez C, Delfino AE, Altermatt FR, Corvetto MA. (2018) Adquirir habilidades no manejo de crises de hipertermia maligna: comparao de simulao de alta-fidelidade 19 DEBRIEF AFTER SIMULATION versus estudo de caso em computador [Acquiring skills in malignant hyperthermia crisis management: comparison of high-fidelity simulation versus computer-based case study]. Rev Bras Anestesiol.68(3):292-298. doi:10.1016/j.bjan.2018.01.005 Offiah, G., Ekpotu, L. P., Murphy, S., Kane, D., Gordon, A., O'Sullivan, M., Sharifuddin, S. F., Hill, A., & Condron, C. M. (2019). Evaluation of medical student retention of clinical skills following simulation training. BMC medical education, 19(1), 263. https://doi.org/10.1186/s12909-019-1663-2 Samawi, Z., Miller, T., & Haras, M. S. (2014). Using High-Fidelity Simulation and Concept Mapping to Cultivate Self-Confidence in Nursing Students. Nursing Education Perspectives, 35(6), 408-409. doi:10.5480/12-1042.1 Verkuyl, M., St-Amant, O., Hughes, M., Lapum, J., & McCulloch, T. (2019,). Combining selfdebriefing and group debriefing in simulation. https://www.sciencedirect.com/science/article/pii/S1876139919302117 Yunoki, K., & Sakai, T. (2018). The role of simulation training in anesthesiology resident education. Journal of anesthesia, 32(3), 425433. https://doi.org/10.1007/s00540-0182483-y  ...

... The Effects of Video Simulation on Preoperative Evaluation Marian University Leighton School of Nursing Doctor of Nursing Practice Final Project Report for Students Graduating in May 2022 The Effects of Video Simulation Learning on Preoperative Evaluation Robin Betz Marian University Leighton School of Nursing Chair: Bradley Stelflug, DrAP, MBA, CRNA _________________________ (Signature) Date of Submission: June 20th, 2021 The Effects of Video Simulation on Preoperative Evaluation The Effect of Simulation Learning on Preoperative Evaluation This project was submitted to the faculty of Marian University Leighton School of Nursing as partial fulfillment of degree requirements for the Doctor of Nursing Practice Certified Registered Nurse Anesthetist Track. The student registered nurse anesthetist (SRNA) curriculum consists of didactic and clinical. While the didactic portion contains the knowledge needed clinically, it is often hard to transition that knowledge into practice. Simulation is a proven method shown to help SRNAs transition into practice. Furthermore, simulation learning improves SRNA confidence, knowledge, and clinical performance (Ambardekar et al., 2016). It is vital SRNAs are both confident and knowledgeable in their comprehension and ability to perform basic skills, such as preoperative anesthesia evaluations. Confidence and knowledge translate into efficiency in practice and aids in improved patient safety and outcomes in the clinical setting (Ritter et al., 2020). Background Multiple studies detail the value of simulation-based learning related to instructing SRNAs on different technical and non-technical skills, including peripheral nerve blocks, cannot intubate/cannot oxygenate scenarios, situational awareness, and decision making. One vital area to the efficiency and safety of nurse anesthetists is their ability to evaluate patients preoperatively. The preoperative anesthesia evaluation is an essential part of safely anesthetizing patients. Standard of care dictates an evaluation must be performed on every patient prior to the administration of anesthesia (Sweitzer, 2021). It is widely accepted that in a healthy patient The Effect of Simulation Learning on Preoperative Evaluation 3 receiving a low-risk procedure the preoperative evaluation can be done the day of surgery, however, in a high-risk patient or procedure, a more extensive workup before the day of surgery (DOS) is warranted (Sweitzer, 2021). Preanesthetic clinics provide a location for these assessments to take place. Preanesthetic clinics have been shown to reduce unnecessary testing, case cancellations, delays, and mortality (Sweitzer, 2021). Blitz et al. (2016) hypothesized preoperative evaluation clinics reduction in mortality could be contributed to an increase in patient engagement, better communication between interdisciplinary teams, and improved clinical coordination. The preoperative evaluation includes a clinical evaluation, a perioperative risk assessment, management and optimization of diseases affecting perioperative risks, creating a plan for anesthesia care, educating patients and their families regarding the perioperative period, obtaining informed consent, regulating the appropriateness of the anesthesia setting, discussing preventative care measures, and meeting regulatory requirements (Sweitzer, 2021). The clinical evaluation and risk assessment are fundamental in determining if the procedure can be safely performed. The clinical preoperative evaluation and assessment aspect minimally includes a patient interview, focused examination, airway assessment, review of medical records, arranging preoperative testing deemed necessary, and consulting any specialists indicated (Sweitzer, 2021). The risk assessment is another fundamental piece of the preoperative evaluation and discloses risks associated with the patients condition and the planned procedure (Sweitzer, 2021). Risk factors associated with patient conditions are often delineated during the preoperative evaluation and assessment. These risk factors include the American Society of Anesthesiologists physical status (ASA-PS), functional status, biomarkers, and surgical risks related to the type of procedure (Sweitzer, 2021). Furthermore, there are medical ailments The Effect of Simulation Learning on Preoperative Evaluation 4 associated with an increased risk for perioperative adverse events, it is vital the SRNA know how to identify these conditions and employ risk stratification. Conditions that increase risk for perioperative adverse events include cardiovascular, cerebrovascular, neurological, pulmonary, liver, endocrine, thyroid, and kidney diseases (Sweitzer, 2021). Further risks are associated with advanced age, anemia, malnutrition, thromboembolic disorders, obesity, tobacco use, and alcohol misuse (Sweitzer, 2021). The forenamed conditions not only indicate a potential for perioperative complications but can also alter the way the anesthesia providers deliver care. Once threats to patient safety have been identified the anesthetist can optimize the risk factors, create an anesthetic plan, and obtain informed consent. The preoperative assessment goal is to evaluate the patient and their tolerance to anesthesia, reduce risk, and prepare the patient for surgery (Sweitzer, 2021). The preoperative evaluation should be conducted at an appropriate timeframe for the patients risk level and the type of procedure being performed. A thorough and timely preoperative assessment has been shown to improve patient satisfaction and decrease complications, cancellations, costs, delays, and overall mortality (Sweitzer, 2021). Problem Statement Anesthesia mortality has fallen over the past several decades and is now estimated to be approximately one death per every 200,000 to 300,000 anesthetics (Falk & Fleisher, 2020). The preoperative evaluation aids in reducing risks and ultimately mortality related to anesthesia care, thus making it a fundamental skill the SRNA must possess (Blitz et al., 2016). This project aimed to determine how an SRNAs confidence and knowledge are affected when receiving education on preoperative interviews via reading material compared to those who receive instruction via video simulation along with the reading material. The Effect of Simulation Learning on Preoperative Evaluation 5 Gap Analysis Current practice at Marian University includes the students reading Nurse Anesthesia by John Nagelhout then performing a simulation of preoperative evaluation on a live model. Best practice guidelines for simulation include a purposeful design to meet specified objectives and achieve expected outcomes (INACSL, 2016). Video based learning as an adjunct to simulation allows for specified objectives to be defined, thus making it more likely to achieve expected outcomes. The implementation of video-based learning as an adjunct to simulation regarding preoperative anesthesia evaluation is appropriate at Marian University in the Certified Registered Nurse Anesthesia Program. Video simulation of preoperative evaluations was not previously utilized at Marian University; therefore, the gap in best practice and research is present, and Marian is an appropriate site for implementation. Review of Literature A comprehensive search was conducted utilizing the Hackelmeier Memorial Library. The search included databases EBSCOhost, PubMed, WorldCat.org, and CINAHL. Key phrases student nurse anesthesia simulation, nurse anesthesia students perception of simulation, and anesthesia providers perception of simulation were used. This search methodology yielded 155, 51, and 158 results respectively. Inclusion criteria included peer reviewed articles, English language, published between 2015 and 2021, a focus on simulation learning, and measurement of confidence, knowledge, and/or skill level of the participants. The search was concentrated on healthcare-related participants to streamline relevance. Exclusion criteria included articles greater than five years old and articles not focusing on simulation, confidence, knowledge, or skill level. A total of 13 articles were selected for this literature review. Simulation as an Adjunct to Learning The Effect of Simulation Learning on Preoperative Evaluation 6 Preanesthetic evaluations have the potential to yield decreased complications, better flow of peri anesthetic care, improved patient satisfaction, and decreased cost, cancellations, and mortality (Sweitzer, 2021). Duffy et al., (2019) asserts simulation is widely accepted as an effective adjunct to didactic in both anesthesiology and other medical specialties. Gauger et al., (2018) define simulation as a method to replace or amplify real-life health encounters by utilizing lifelike mannequins, physical models, standard patients, or computers. Benefits of simulation include increased confidence, knowledge, and skill level (Tsai et al., 2016). Simulation also enhances competence in procedures rarely seen in clinical practice (Gauger et al., 2018), compensates for gaps in training (Duffy et al., 2019), and meets learners needs without risk to the patient or learner (Ambardekar et al., 2016). Furthermore, exposure to a specific skill leads to increased patient safety (Staun et al., 2020). Additional benefits to the participant include the ability to ask questions, nurture friendships, and build mentorships (Duffy et al., 2019). Ambardekar et al., (2016), Duffy et al., (2019), and Ritter et al., (2020) acknowledge simulation retention has been noted up to one-year post-simulation lab experience, further highlighting the effectiveness of this learning adjunct. Numerous studies detail how simulation affects learners perception of confidence and knowledge (Ambardekar et al., 2016, Duffy et al., 2016, Tsai et al., 2016). Seven articles in this literature review are in agreeance simulation has a positive impact on a participants overall experience (Ambardekar et al., 2016; Duffy et al., 2019; Gauger et al., 2018; Ritter et al., 2020; Shields & Gentry, 2020; Staun et al., 2020; Tsai et al., 2016). Many of these articles focused on the measurement of confidence and/or knowledge. Confidence was positively affected in six of the eight articles, signifying simulation increases a learners confidence related to a specific skill (Gauger et al., 2018; Tsai et al, 2016; Duffy et al., 2019; Staun et al., 2020; Ambardekar et al., The Effect of Simulation Learning on Preoperative Evaluation 2016; Ritter et al., 2020). Confidence was increased to a greater extent in participants with less experience in studies performed by Ritter et al., (2020) and Tsai et al., (2016). Non-technical and Technical Skills Simulation has been shown to improve technical and non-technical skills alike in the medical field (Ambardekar et al., 2016). Non-technical skills are essential in the response to critical events and learning these skills is imperative (Duffy et al., 2019). Non-technical skills gained through the use of simulation include communication, situational awareness, leadership, and coordination (Tsai et al., 2016). Communication is often cited as a factor in medical errors and is the cause of 64% of sentinel events documented (Tsai et al., 2016). Mental planning (Ritter et al., 2019) and critical thinking (Ambardekar et al., 2016) were also quoted as nontechnical skills attained through simulation. Anxiety and Simulation Learning Studies by Lewis (2019) and Ambardekar et al., (2016) detailed barriers to simulation learning, declaring perceived overall cognitive load (POCL) and anxiety as factors influencing performance during simulation. Lewis, (2019) noted the level of support offered by the simulation staff would decrease cognitive load and anxiety and ultimately lead to higher performance and cognitive processing. A safe, supported, confidential, and non-judgmental learning environment was identified as necessary to minimize anxiety and improve learning outcomes (Ambardekar et al., 2016). Clinical Performance Post-Simulation and the Need for Additional Research In a study by Gauger et al., (2018) both procedural time and procedural performance were assessed via timing the procedure and the Cricothyroidotomy Skills Maintenance Program Global rating scale and checklist. The measurements indicated procedural time was decreased 7 The Effect of Simulation Learning on Preoperative Evaluation 8 while performance was increased (Gauger et al., 2018). Shields & Gentry, (2020) tracked the participants ability to interpret and recognize transesophageal echocardiogram images as well as cardiac pathology identification. These studies, along with numerous others, described the need for further analysis of simulation-based training concerning the translation of training into clinical practice (Ambardekar et al., 2016, Tsai et al., 2016, Yunoki & Sakai, 2017). Simulation retention (Gauger et al., 2018), clinical outcomes (Shields & Gentry, 2020), reduction of patient mortality and morbidity (Tsai et al., 2016), and improved patient care (Ambardekar et al., 2016) were all topics implicated for future research associated with simulation as a learning adjunct. Summary This review of the literature highlights the benefits of simulation learning in the healthcare field, particularly within anesthesia. Simulation is effective in boosting confidence, knowledge, and skill level regarding both technical and non-technical skills (Ambardekar et al., 2016, Yunoki & Sakai, 2017). Theoretical Framework The theoretical framework guiding this DNP project was the NLN Jefferies Simulation Framework. The NLN Jeffries Model encompasses the concepts of context, background, design, simulation experience, facilitator and educational strategies, participant, and outcomes. The NLN Jefferies framework can be used to evaluate simulation training, this study focused on the outcomes aspect of this framework, specifically participant and learning outcomes. Cowperthwait (2020) notes that through simulation-based learning, both an increase in participants confidence and their learned abilities can be transitioned into clinical practice. Utilization of the NLN Jefferies framework evaluated simulation regarding preoperative The Effect of Simulation Learning on Preoperative Evaluation 9 evaluation on students in Marian Universitys DNP Nurse Anesthesia program for the purpose of this DNP study. Goals, Objectives, and Expected Outcomes The goal of this DNP project was to establish how video learning can aid in developing self-confidence and knowledge regarding simulation of preoperative evaluation in the SRNA. The objective of this project was to determine if video learning improves self-confidence and knowledge in the Marian SRNA. Expected outcomes consisted of the video enhancing selfconfidence and learning. Project Design The intervention was to introduce video simulation teaching regarding preoperative evaluations for anesthesia. Current practice at Marian University consisted of the SRNA reading Chapter 20 in Nurse Anesthesia by John Nagelhout and Sass Elisha. After completing the required reading, the SRNA was then asked to perform a preoperative evaluation on a standard model. The randomized control group of 12 students followed current practice. The randomized experimental group of 12 participants received a video detailing best practice preoperative evaluation and completed the required reading, then performed the preoperative evaluation on the same model. Confidence was assessed post-intervention via a modified Student Satisfaction and Self Confidence in Learning survey. A post-simulation quiz was administered to evaluate knowledge gain. The control group was later offered the simulation video on preoperative evaluation to ensure fairness and equal learning opportunity. Methods The Effect of Simulation Learning on Preoperative Evaluation 10 Traditional learning modalities in anesthesia include reading materials followed by performing vital anesthesia skills, such as a preoperative evaluation. This DNP project incorporated a seven-minute video detailing best practice preoperative evaluation following the guidelines detailed in Nurse Anesthesia by John Nagelhout. The students then participated in a simulation lab in which they performed a preoperative anesthesia evaluation on a single, standard patient. Directly after the simulation students evaluated their confidence level and took a quiz that quantified their knowledge base. The survey measured the students confidence in their ability to perform a preoperative evaluation. The knowledge quiz determined if the video provided to the experimental group increased level of understanding pertaining to preoperative assessment. For this quality improvement project, the 2023 Marian class of SRNAs were randomized into two groups of 12 participants each. Group one read Chapter 20 of Nurse Anesthesia by John Nagelhout and then performed a preoperative evaluation on a live model. Group two was provided with a video depicting preoperative evaluation along with reading Chapter 20 of Nurse Anesthesia. Group two then completed their preoperative evaluation on the same live model. The class rated their confidence via a survey post-simulation. Knowledge was assessed through a quiz post intervention. Measurement Instrument Confidence was evaluated using a five-point Likert Scale, a validated modified Student Satisfaction & Self Confidence in Learning Preoperative Evaluation & Assessment (Appendix A). The SRNAs rated their confidence post-intervention on a scale of one to five, one being strongly disagree and five being strongly agree. Knowledge was assessed via a ten-question post The Effect of Simulation Learning on Preoperative Evaluation 11 simulation quiz pertaining to the preoperative assessment evaluation. The knowledge post simulation quiz was validated by a team of CRNAs (Appendix B). Data Analysis Data was compiled and analyzed via Microsoft Excel (Appendix C). Confidence was analyzed via a Wilcoxon test to determine if the experimental groups self-confidence was increased as hypothesized. A Wilcoxon test compares two groups to determine if the groups are statistically significant regarding a particular measurement. Application of the Wilcoxon test was deemed appropriate as comparison between two groups was the data point being evaluated. The statistics detailing confidence showed a test indicator of ten, which infers there is a difference in confidence between the control group and experimental group. The data was then analyzed utilizing the t test to determine if statistical significance was present. A t-test is designed to determine if there is a statistical difference between the two groups. The confidence t test determined a p value of 0.0087, indicating a highly significant difference between the control group and the experimental group, with the experimental group exhibiting a significant increase in confidence. A t-test was also employed to determine knowledge gain and delineate if knowledge improvement was statistically significant between the control and experimental group. The t-test was appropriate to utilize in this setting because the goal was to conclude which group had a higher mean test score. The t-test confirmed knowledge difference between the control group and the experimental group was mildly significant, with a p value of 0.0697. Discussion The Effect of Simulation Learning on Preoperative Evaluation 12 The result of this project depicts the effectiveness of video simulation when employed on SRNAs at Marian University considering student confidence and knowledge. Students who received a video depicting best practice preoperative evaluations rated their confidence at an average of 38.2 as compared to a confidence score of 34.1 in students who did not receive a video. Furthermore, the average test scores were 71.1% compared to 67.5% respectively in students who received the video versus those who did not. Video learning improved confidence and knowledge in the Marian SRNA concerning preoperative evaluation simulation test out. Conclusion SRNAs are subject to didactic before entering a clinical setting. Simulation has been shown to improve knowledge and confidence in the SRNA, which can lead to improved clinical outcomes (Ritter et al., 2020). This DNP project provided a video-based intervention the SRNAs used in adjunct with didactic and simulation to aid in mastering preoperative anesthesia evaluations. The video instruction led to increased confidence in performance and a marginally improved level of knowledge concerning preoperative evaluations in Marian SRNAs. Weaknesses of this project include a lack of objective data to determine if the skill was successfully performed and self-reporting of confidence using a Likert- scale can be inaccurate. Future studies should objectively evaluate how successfully the skill is performed and how the skill set is translated into practice. Additional research on the longevity of the skill retained is also warranted. The Effect of Simulation Learning on Preoperative Evaluation 13 References Ambardekar, A.P., Singh, D., Lockman, J.L., Rodgers, D.L., Hales, R.L., Gurnaney, H.G., Nathan, A., & Deutsch, E.S. (2016, January 28). Pediatric anesthesiology fellow education: is a simulation based boot camp feasible and valuable? Pediatric Anesthesia 26(2016), 481-487. doi:10.1111/pan.12865 Blitz, J.D., Kendale, S.M., Jain, S.K., Cuff, G.E., Kim, J.T., Rosenberg, A.D. (August, 2016). Preoperative evaluation clinical visit is associated with decreased risk of in-hospital postoperative mortality. Anesthesiology 125(2), 280-294. doi;10.1097/ALN.0000000000001193 Cowperthwait, A. (2020). NLN/Jeffries Simulation framework for simulated participant methodology. Clinical Simulation in Learning (2020), 1-10. https://doi.org/10.1016/j.ecns.2019.12.009. Duffy, C.C., Nawoor-Quinn, Z., Burlacu, C.L. (2019, August 19). 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Journal of Anesthesia 2018 (32), 425-433. https://doi.org/10.1007/s00540018-2483-y The Effect of Simulation Learning on Preoperative Evaluation Appendix A 15 The Effect of Simulation Learning on Preoperative Evaluation Appendix B 16 The Effect of Simulation Learning on Preoperative Evaluation Appendix C 17  ...