Proficiency of EKG Interpretation in the Primary Care Setting: A Quality Improvement Initiative

Background: The American Heart Association (AHA) reports that heart disease is the leading cause of death for men, women, and people of most racial and ethnic groups in the United States, costing nearly $1 billion daily. An estimated 80% of these cases are preventable; almost 400,000 people in the United States die yearly due to cardiovascular disease. Recent projections estimate that by 2035, 45% of the population will have cardiovascular disease costing the United States $1 trillion annually. Local Problem: Electrocardiogram (EKG) is the gold standard for the diagnosis, assessment, and management of patients with cardiovascular disease (CVD); however, prior studies show low accuracy rates among practitioners. There is a recognized need for improvement in EKG education and advancement in continued learning for all clinicians. This project aims to improve providers' proficiency and confidence in EKG interpretation and subsequent medical decision-making in a non-cardiac care clinic. Methods: This quality improvement project was completed in five phases. First, a per-implementation survey assessed perceived confidence and proficiency in clinicians' EKG interpretation. Next, an education module was created based on the initial survey results. Phase three included an EKG pre-skills test where the clinicians interpreted six distinct EKG rhythm strips, followed by the education module. A post-skills test was then administered to assess learning. In phase four, the clinicians used an algorithm (Colbeck, 2016) to assist EKG interpretation and guide clinical practice. Phase five focused on the usability and sustainability of ongoing EKG education and resources. Finally, a post-intervention survey assessed continued confidence, long-term knowledge retention, and proficiency in EKG interpretation. Results: The EKG skills test was comprised of 6 EKGs with a total of 12 questions. The overall quiz score was computed as the sum of the 12 questions. A paired t-test was utilized to compare pre-and post- knowledge tests. There was a significant improvement in post-scores, with a mean of 5.1 points higher on the post-test. Of those who participated, 80% (n=8) reported that they were either "not confident" or "somewhat confident" before the education. Following the educational module, self-reported confidence 3 improved, with no practitioners (n=0) reporting "Not Confident." Eight weeks post-intervention, while utilizing the algorithm (Colbeck, 2016), 100% (n=10) of clinicians reported being either "confident" or "very confident" in EKG interpretation. Conclusion: This project shows improved EKG interpretation competency and demonstrates the importance of ongoing education in the non-cardiac setting. Long-term knowledge retention is critical to the diagnosis and medical decision-making of cardiac abnormalities.

1 Proficiency of EKG Interpretation in the Primary Care Setting: A Quality Improvement Initiative Rachelle C. Perry, Jordan Hays, Melinda Patterson College of Nursing: The University of Utah NURS 7703: DNP Scholarly Project III May 9, 2023 2 Abstract Background: The American Heart Association (AHA) reports that heart disease is the leading cause of death for men, women, and people of most racial and ethnic groups in the United States, costing nearly $1 billion daily. An estimated 80% of these cases are preventable; almost 400,000 people in the United States die yearly due to cardiovascular disease. Recent projections estimate that by 2035, 45% of the population will have cardiovascular disease costing the United States $1 trillion annually. Local Problem: Electrocardiogram (EKG) is the gold standard for the diagnosis, assessment, and management of patients with cardiovascular disease (CVD); however, prior studies show low accuracy rates among practitioners. There is a recognized need for improvement in EKG education and advancement in continued learning for all clinicians. This project aims to improve providers' proficiency and confidence in EKG interpretation and subsequent medical decision-making in a non-cardiac care clinic. Methods: This quality improvement project was completed in five phases. First, a per-implementation survey assessed perceived confidence and proficiency in clinicians' EKG interpretation. Next, an education module was created based on the initial survey results. Phase three included an EKG pre-skills test where the clinicians interpreted six distinct EKG rhythm strips, followed by the education module. A post-skills test was then administered to assess learning. In phase four, the clinicians used an algorithm (Colbeck, 2016) to assist EKG interpretation and guide clinical practice. Phase five focused on the usability and sustainability of ongoing EKG education and resources. Finally, a post-intervention survey assessed continued confidence, long-term knowledge retention, and proficiency in EKG interpretation. Results: The EKG skills test was comprised of 6 EKGs with a total of 12 questions. The overall quiz score was computed as the sum of the 12 questions. A paired t-test was utilized to compare pre-and postknowledge tests. There was a significant improvement in post-scores, with a mean of 5.1 points higher on the post-test. Of those who participated, 80% (n=8) reported that they were either "not confident" or "somewhat confident" before the education. Following the educational module, self-reported confidence 3 improved, with no practitioners (n=0) reporting "Not Confident." Eight weeks post-intervention, while utilizing the algorithm (Colbeck, 2016), 100% (n=10) of clinicians reported being either "confident" or "very confident" in EKG interpretation. Conclusion: This project shows improved EKG interpretation competency and demonstrates the importance of ongoing education in the non-cardiac setting. Long-term knowledge retention is critical to the diagnosis and medical decision-making of cardiac abnormalities. Keywords: EKG proficiency, clinician competency, EKG education, knowledge retention 4 Improving Proficiency of EKG Interpretation in the Primary Care Setting Problem Description The American Heart Association (AHA) reports that heart disease is the leading cause of death for men, women, and people of most racial and ethnic groups in the United States, costing nearly $1 billion daily. An estimated 80% of these cases are preventable; still, almost 400,000 people in the United States die yearly due to cardiovascular disease (Narayan et al., 2021). Recent Centers for Disease Control (CDC) projections estimate that by 2035, 45% of the population will suffer from cardiovascular disease costing the United States more than $ 1 trillion annually (CDC Prevention Programs, 2018). Preventative care and precise management of heart disease in the primary care setting can play an essential role in the prevalence of cardiovascular disease. Therefore, primary care clinicians must be proficient in cardiovascular disease diagnostics and have access to continued education to ensure they know the most current AHA practice standards. Electrocardiogram (EKG) is the most frequently used cardiovascular diagnostic test and remains the gold standard for diagnosis, assessment in response to therapy, and risk stratification management for patients with cardiovascular dysfunction (Goldberger et al., 2022). Over 100 million EKGs are obtained in the United States annually, including 21% in the primary care setting (Tison et al., 2019). Although the accurate interpretation of the standard 12-lead EKG is fundamental to diagnosing cardiovascular disease, prior studies report low accuracy rates among practitioners (Krasne et al., 2020). Electrocardiogram interpretation is a clinically challenging skill and when misinterpreted, can delay disease-specific treatment and result in poor patient outcomes. (Breen et al., 2019). More specifically, Breen et al. found nearly 33% of clinician EKG readings have missed or erroneous findings, with 11% leading to medical mismanagement (2019). In the past 80 years, EKGs have evolved with technological advancements to expand clinical inferences within the EKG test. An abnormal test can show irregular rate, irregular rhythm, shape abnormalities, electrolyte imbalances, medication side effects, high blood pressure, and heart attack. AlGhatrif et al. (2012) suggests that understanding the disorders 5 that represent EKG variations could make identifying abnormalities easier to recognize. There is a recognized need for improvement in EKG education and advancement in continued learning for all clinical practitioners (Waechter et al., 2019). Available Knowledge In October 2021, The American Heart Association (AHA) published an update on the Mission Lifeline initiative implemented in 2007. "Accurate risk stratification and diagnostic testing are critical" (Jacobs et al., 2021) for time-dependent therapies when treating and diagnosing acute coronary syndrome (ACS). AHA guidelines state the EKG is the most critical diagnostic component of prehospital ACS treatment, yet evidence shows a wide variation in outpatient training standards (Nallamothu et al., 2007). As part of their Mission Lifeline initiative, the AHA reports that staff education is a modifiable barrier and urges ongoing education and training of clinical staff in all aspects of the ACS continuum of care (Jacobs et al.., 2021). Because of its broad applicability, Kligfield et al. (2012) call for the "establishment of and adherence to professionally developed and endorsed evidence-based standards" for EKG interpretation to "ensure the high level of precision required and expected by clinicians and their patients." Rationale Gilbert's behavioral engineering model (BEM) guided this quality improvement project by understanding that change is influenced by environment and personal behavior. The BEM is a framework used to understand better where performance gaps are so the analyzer can decide which variables require altering to improve behavior best (Gilbert, 1978). This theory further suggests that personal performance is directly correlated to the level of management support. Understanding the critical role management plays in behavior change and establishing the level of support and buy-in from the clinic director was necessary. During several informal meetings, he validated his interest and support for the EKG quality improvement (QI) project emphasizing the importance of continued education for clinical providers. 6 Gilbert suggests that one must focus on environmental variables to change performance successfully before addressing personal variables. Gilbert highlights three areas of focus within the environment: DataInstrumentation-Incentives (Gilbert, 1978). More specifically, do the clinicians have access within their environment to the information they need, resources to assist them, and the motivation with incentives to aid in their success? In addition to the environmental variables, there are three areas of personal behavior to address when optimizing change: Knowledge-Capacity-Motives (Gilbert, 1978). The personal matrix will address the needs of the individual and assess if the teaching is appropriate for the clinical position. Personal behavior variables also investigate if the clinician is capable of understanding/using this information and do they have realistic motives for the use of the information and ensuing change. Initial assumptions regarding the intervention's success were based on environmental and personal variables outlined in the BEM framework. Utilizing the BEM framework, it was anticipated that providers at the Corner Clinic would have improved EKG interpretation skills and, subsequently, increased confidence in their EKG interpretation and general practice. Specific Aims This project aimed to improve providers' proficiency and confidence in interpreting EKGs and subsequent medical decision-making in a non-cardiac/primary care clinic. Methods Context The Corner Clinic, LLC has been a privately owned urgent care clinic in St George, Utah, since 2017. They employ ten clinicians between 2 locations, including 1 physician (DO), 1 physician assistant (PA), and 8 nurse practitioners (NP). Of these ten clinicians, 8 have master's degrees, and one has a doctorate of nursing practice (DNP). Of these clinicians, 75% are in their first one to two years of practice. Four of the ten practitioners (40%) have no cardiology experience. The company is funded by Heywood Development, LLC, under the direction of Bruce Heywood as Chief Executive Officer. In a review of public tax information, this LLC had total revenue of $1.3 million in 2021. Corner Clinic is a 7 cash-based service only, meaning the clinic does not submit payment for services with insurance companies. This supporting information is significant because, in the past ten years, private firms have increasingly invested in urgent care modalities as patients seek alternatives to primary care, costly visits to the emergency room, and a more convenient mode of healthcare access (Adams, 2022). Adams further explains that urgent care centers in the US grew to more than 10,400 locations in 2021, a 63% increase over the previous seven-year period (2022). It is important to understand healthcare delivery trends now and in the future as the US population seeks alternative care options. The sustainability of alternative healthcare delivery models will narrow the healthcare access gap while addressing health disparities. Alternative care options are those with quality measures based on improved health outcomes and clinical providers who seek ongoing education for best-practice patient care. Intervention(s) The interventions of this project occurred in five phases. The first phase of this QI initiative was to understand the needs of the clinicians regarding their perceived proficiencies. An initial survey, the pre-intervention survey (see Appendix A), was conducted among the ten clinicians to fully understand desired proficiencies and perceived abilities. Phase two used the survey results to understand the unique needs of the clinicians, and created an education module that included these results (see Appendix B). The educational module included the EKG algorithm (see Appendix C) that the clinicians would reference in the post-intervention period (Colbeck, 2016). Phase three included an EKG pre-skills test where the clinicians interpreted six distinct EKG rhythm strips (see Appendix D). The education module immediately followed the pre-skills test and concluded with a question-and-answer session. This was presented as a PowerPoint presentation (see Appendix B). A post-skills test was then administered to assess learning change (see Appendix D). Phase four was an 8-week period where the clinicians utilized an EKG algorithm (see Appendix C) to aid in accurate EKG interpretation and guide clinical practice guidelines. In addition to the initial education module, we wanted to provide ongoing resources to the clinic as a long-term educational resource. Phase five focused on the usability, feasibility, and 8 sustainability of ongoing EKG education and resources in a non-cardiac clinic. To aid in this assessment, a post-intervention survey (see Appendix E) was conducted to assess continued confidence and long-term proficiency in EKG interpretation while integrating the algorithm as part of their clinical practice (Colbeck, 2016). Study of the Intervention(s) Two approaches were used to assess the impact of the education module and the ongoing use of the EKG algorithm in this quality improvement project. Pre- and post-intervention surveys were used to compare perceived skill and knowledge of EKG interpretation. The second approach was comparing objective skills by comparing the EKG pre-and post-skills tests. These skill tests determined a baseline skill level and the effectiveness of the education module by comparison to the post-skills test results. Before this QI project, this outpatient clinic had no ongoing education and did not adhere to any practice guidelines regarding EKG interpretation. Therefore, it can be reasonably assumed that any change and improvement can be directly attributed to the intervention. There were no comparison groups included in this intervention. Measures A pre-intervention survey (see appendix A) was given for demographic and background information, which was used to provide context to the sample population. This survey also included an open-ended question regarding specific areas the clinicians felt would be valuable to learn and what would be relevant in their practice. Additionally, it asked clinicians to quantify their confidence level in EKG interpretation. A pre-skills test was given before the educational module to assess baseline knowledge (see Appendix D), including six EKGs with specific areas of irregularity. A PowerPoint presentation (see Appendix B) was then given starting with a brief review of EKG basics. The education module also included specific EKG abnormalities for review and recognition. Finally, instruction was given to differentiate between emergent, urgent, and routine cardiology referrals. After the educational intervention, a post-skills test was given to determine increased knowledge levels and improved ability to 9 identify abnormal EKG patterns (see Appendix D). At the same time, we also assessed each clinician's post-skills test confidence level. Finally, we reassessed perceived confidence levels in EKG interpretation after eight weeks of the clinicians using the EKG algorithm (see Appendix C) (Colbeck, 2016). Clinicians were given a final survey, the post-implementation survey (see Appendix E), to measure the intervention's usability, feasibility, and satisfaction and the continued use of the algorithm to implement practice guidelines in their clinical practice. Analysis The study sample and demographic data were analyzed using descriptive statistics and percentages. In addition to demographic data in the pre-intervention survey, we used an open-ended question to evaluate feedback on perceived educational needs. These open-ended questions were examined by conducting a content analysis to develop categories and sub-categories that later shaped the content of the teaching portion of the improvement project. Quantitative analysis assisted in understanding pre-and post-intervention data and was analyzed by dependent T-Test to measure the change in knowledge. Self-reported confidence levels were analyzed using the Wilcoxon signed-rank test. Ethical Considerations This DNP project was QI in nature and not subject to Institutional Review Board oversight at the University of Utah. There are no conflicts of interest concerning this study in which to disclose. Results This QI implementation was completed in the outpatient setting with clinicians not specialized in cardiology; 60% (n=6) reported no past or present cardiology experience. The initial phase consisted of a pre-intervention survey (see Appendix A) including five demographic questions, two Likert-scale questions addressing clinicians' perceived confidence, and one open-ended question asking what is most 10 valuable to them in EKG education. Demographic data were analyzed using descriptive statistics and are summarized in Table 1. We used Likert-scale questions to evaluate clinicians' confidence in their primary practice and EKG interpretation. Initial observation showed that all clinicians (n=10) were more confident in their primary practice than in their ability to interpret EKGs accurately. The initial pre-intervention survey revealed that 80% of providers (n=8) rated themselves either "not confident" or "somewhat confident" when interpreting EKGs (see Table 2). The open-ended survey question was evaluated, and education material was constructed based on the appraisal of common themes, including differentiation of urgent v. emergent, identifying structural abnormalities, and wanting a "tune-up" on EKG interpretation. The education phase of the QI project was planned to coincide with the clinic's mandatory monthly staff meeting. All ten clinicians who were initially surveyed were in attendance, including DO (n=1), NP (n=7), DNP (n=1), and PA (n=1). The EKG skills-test comprised 6 EKGs with a total of 12 questions. The overall quiz score was computed as the sum of the 12 questions. A paired t-test was utilized to compare pre-and post-knowledge tests (see Table 6). We found a significant improvement in post scores, with a mean improvement of 5.1 points. The t-value is 7.564 with a p-value of < 0.00003 which supports the statistically significant improvement in pre- to post-skill test scores. In addition to the skills test, we surveyed clinicians immediately following the education to understand if one education session would influence and improve perceived confidence. Provider change in confidence was measured using Wilcoxon Signed Test. 80% (n=8) reported that they were either "not confident" or "somewhat confident" before the education (see Table 2). Immediately following the educational module, selfreported perceived confidence improved, with no practitioners (n=0) reporting "Not Confident." When analyzed using Wilcoxon Signed Rank Test, the p-value was < 0.005, (see Table 2) indicating a statistically significant improvement in confidence. In the final implementation phase of this QI project, we wanted to understand the feasibility, usability, and satisfaction of EKG education and ongoing algorithm usage (Colbeck, 2016). Provider change in confidence was again measured using Wilcoxon Signed Test, which shows continuing and sustainable knowledge with p < 0.003 (see Table 4). Eight 11 weeks post-intervention, while utilizing a visual aid/algorithm, 100% (n=10) of clinicians reported being either "confident" or "very confident" in EKG interpretation (see Table 2). Discussion Summary This quality improvement project aimed to increase EKG interpretation proficiency and confidence through initial education and subsequent longer-term improvement with a visual algorithm. The project results described in this paper can be attributed partly to the understanding and application of Gilberts Behavioral Engineering Model (BEM). The Corner Clinic was ideal for this quality improvement project because management was supportive throughout the implementation, and the environment invited clinical improvement. Throughout the application, all clinicians (n=10 100%) completed the preintervention survey, EKG pre-skills test, EKG post-skills test, and the 8-week post-intervention survey. There was significant improvement in EKG interpretation knowledge, as demonstrated by the post-skills test scores (Figure 1). But perhaps more importantly, knowledge retention was ongoing, as evidenced by the advance of clinician-perceived confidence immediately after the intervention and at the eight-week follow-up. These results support the findings by Burns et al. (2019), as he illustrates an unmet need for EKG educational development among clinicians but demonstrates the potential opportunities in EKG education both short-term and long-term. Teaching using a visual algorithm to aid accurate interpretation and knowledge retention supports a previous study by Brooks et al. (2016) that reports EKG interventions should be reinforced frequently, even daily. Interpretation This QI project was implemented to educate a non-cardiac outpatient clinic on EKG interpretation. We anticipated improved accuracy and proficiency in EKG interpretation by implementing an educational program with observation over eight weeks. On initial analysis, the providers supported the data that despite training in EKG interpretation, accuracy rates remain low among practitioners 12 (Krasne et al., 2020). Before the education module, the mean EKG score of providers was 5.30 points out of a total of 12 points giving the clinicians an average of a 44% for the group (see Table 6). Further analysis reveals 50% (n=5) had a score of 4 or less (33%), which supports Breen et al. findings that nearly 33% of clinician EKG readings have missed or erroneous findings (2019). The immediate post-skills test illustrates our anticipated outcome improved scores with a mean score of 10.40 points out of 12 points. This data confirms earlier studies that have recognized a need for EKG continued learning for all clinical practitioners (Waechter et al., 2019). Limitations The clinical site chosen for intervention had a relatively small sample size (n=10). Consequently, we analyzed the test scores (quantitative data) using paired t-test instead of the Wilcoxon Sign Ranked Test, which is more resilient to outliers and heavy tail distributions. Fortunately, all staff attend the education session because of the mandatory staff meeting. Still, due to sharing the time with the clinic agenda, the education module was short, and it would have been beneficial to have more time for questions/answers and practice. Again, the clinicians were gracious to be present but did not want to dedicate a prolonged period to EKG education. Another limitation was the lack of objective data for comparative analysis during the 8-week post-intervention survey. Provider responses were obtained on perceived confidence at the 8-week mark, but the clinicians were reluctant to complete another EKG analysis for comparison to pre- and post-skills-test data. While repeat analysis of confidence shows improvement over each survey, it would be beneficial to assess congruence between actual skills through all implementation phases. Another limitation was found in the questioning of clinicians on the surveys. One clinician (n=1 10%) reported being "very confident” over all three observational surveys over the eight weeks. The valuation modality made it difficult to assess any learning as it was not specific enough to extract more precise points of knowledge gain. Finally, due to the time constraint of this project, data was limited to eight weeks. An extended analysis of knowledge retention was not obtainable but would have provided a more robust collection of data. 13 Conclusions The outcomes of this QI project to improve EKG interpretation confidence and competency demonstrates the importance of ongoing education in the non-cardiac setting. Expertise in abnormal pattern recognition typically results from years of clinical practice and is difficult to maintain when working in a non-cardiac environment. However, long-term knowledge retention is critical to the diagnosis and medical decision-making of cardiac abnormalities (Krasne et al., 2021). This study illustrates the positive impact that continued education has on clinicians' proficiency in EKG interpretation. Future research is needed to understand the most effective learning modality for clinicians in the workplace. Educational projects like this have to potential to extend to many non-cardiac clinical settings and even to emergency medical personnel, who are often the first medical contact in emergent settings. 14 Acknowledgments I want to acknowledge and thank my project chair Melinda Patterson, DNP, MSN, RN, NE-BC, for her guidance and support throughout this scholarly project. I would also like to thank my content expert Jordan Hays, NP, for his knowledge and expertise during the planning and implementation process. Lastly, this project would not have been possible without the participation of the clinicians at the Corner Clinic. Thank you for your contribution and commitment to a meaningful change. 15 References AlGhatrif, M., Lindsay, J. (2012). A brief review: history to understand fundamentals of electrocardiography. 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Retrieved from https://pubmed.ncbi.nlm.nih.gov/3154414 17 Table 1: Demographics for medical professionals at Corner Clinic Medical professionals N=10 (%) Age (years) 20-30 31-40 41-50 51-60 Gender Male Female Cardiology Experience Yes No 3 (30) 3 (30) 2 (20) 2 (20) 3 (30) 7 (70) 6 (60) 4 (40) Employment Status Physician 1 (10) Physician Assistant 1 (10) Nurse Practitioner 8 (80) Table 2: Likert Confidence in EKG How Confident are you in EKG interpretation? 18 Not Confident Somewhat Confident Confident Very Confident Extremely Confident Pre-Confidence Post Confidence N=10 (%) N=10 (%) 8 Week Post Confidence N=10 (%) 4 (40) 4 (40) 1 (10) 1 (10) 0 (0) 0 (0) 3 (30) 3 (30) 3 (30) 1 (10) 0 (0) 0 (0) 3 (30) 5 (50) 2 (20) p > 0.005 z-value = -2.810 p> 0.003 z-value = -3.000 Table 3: Wilcoxon Signed Rank Test Comparing Confidence Levels at different intervals Pre-Confidence vs. Post-Confidence Asymp. Sig. (2-tailed) a. Wilcoxon Signed Ranks Test b. Based on negative ranks. -2.810b .005 Post-Confidence Test vs. Final 8-week Confidence Z -3.000b Asymp. Sig. (2.003 tailed) a. Wilcoxon Signed Ranks Test b. Based on negative ranks. Table 4: Likert Confidence in Primary Practice How Confident are you in your current Primary Practice? Not Confident Somewhat Confident Confident Pre N=10 (%) 8 WK Post N=10 (%) 0 (0%) 0 (0%) 2 (20%) 0 (0%) 4 (40%) 5 (50%) P<0.046 Z value -2.000 19 Very Confident Extremely Confident 3 (30%) 1 (10%) 3 (30%) 2 (20%) Table 5: Wilcoxon Signed Rank Test Comparing Primary Practice Confidence Levels Change in Confidence in Overall Practice Z -2.000b Asymp. Sig. (2.046 tailed) Table 6: Pre-and Post-Test Analysis using Dependent T-test 20 Figure 1 21 Quantitative EKG Test Scores Test Scores 11 10 11 10 8 12 11 10 8 7 4 4 11 10 8 6 6 4 4 Practitioners 2 1 2 3 Pre-Intervention Survey 4 5 Pre-Test 6 Post-Test 7 Appendix A 8 9 10 22 23 Appendix B Education PowerPoint Presentation n 24 25 26 Appendix C EKG Algorithm 27 Appendix D Pre- and Post-Skills Test EKGs 28 Appendix E Post-Intervention Survey