Academic, clinical and personal experiences of undergraduate healthcare students during the COVID-19 pandemic: A prospective cohort study.

BACKGROUND: Coronavirus disease 2019 has impacted upon the role and safety of healthcare workers, with the potential to have a lasting effect on their wellbeing. Limited research has been conducted during previous pandemics exploring how student healthcare workers are impacted as they study and train for their professional careers. OBJECTIVE: The aim of the current study was to examine the specific impact of COVID-19 on the academic, clinical and personal experiences of healthcare students. METHOD: Undergraduate students across three year groups within the School of Health Sciences at Ulster University completed online Qualtrics surveys at three timepoints during one academic year (2020/2021). Quantitative survey data was downloaded from Qualtrics into SPSS Version 25 for descriptive analysis of each cross-sectional sample. Qualitative survey data was downloaded into text format, which was thematically analysed using content analysis. RESULTS: 412 students completed the survey at Time 1 (October 2020), n = 309 at Time 2 (December 2020) and n = 259 at Time 3 (April 2021). Academically, the pandemic had mostly a negative impact on the learning environment, the development of practical skills, the assessment process and opportunities for peer learning and support. Students reported increased stress and challenges managing their workload and maintaining a sense of motivation and routine. Clinically, they felt unprepared by the university for placement where the pandemic had an increasingly negative impact over time on learning and skill development. In terms of personal experiences, despite the majority of students taking steps to keep physically and mentally well, negative impacts on friendships, mental wellbeing and concerns for family were reported. The pandemic had not impacted upon career choice for most students. CONCLUSION: Consideration must be given to the development of practical skills so students feel prepared for their professional careers given the practical nature of their roles. Programme coordinators should adopt a holistic approach to student wellbeing.

Electrocardiogram interpretation competency among emergency nurses and emergency medical service (EMS) personnel: A cross-sectional and comparative descriptive study.

AIMS: The aim of this research study was to compare electrocardiogram (ECG) interpretation competency among emergency nurses and EMS personnel. DESIGN: A cross-sectional comparative descriptive study design was used. METHODS: This study recruited 170 participants (105 emergency nurses and 65 EMS personnel) in northwest of Iran. Data were collected during 2018 using ECG, an interpretation competency questionnaire and analysed using SPSS V.24 through independent t test, linear regression, Pearson and Spearman correlation coefficient. A statistical significance of p < .05 was assumed. RESULTS: The study results showed a mean score of 6.65 ± 2.16 out of 10 for emergency nurses' and 4.87 ± 1.81 for EMS personnel ECG interpretation competency (p < .05). CONCLUSIONS: Hospital emergency nurses were more qualified to interpret the ECG than the pre-hospital emergency medical personnel (p = .792 and ß (SE)) = 0.22 (0.84). Active involvement in ECG interpretation and standard continued education are needed to develop and improve the emergency nurses and EMS personnel ECG interpretation competency.

A clinical decision support tool to assist with the interpretation of the 12-lead electrocardiogram.

This article reports the design and testing of a novel interactive method, abbreviated to ANALYSE (systemAtic aNALYsiS of Electrocardiography) to assist interpretation of 12-lead electrocardiogram. 15 participants interpreted a total of 150 12-lead electrocardiogram recordings randomly using a standard and this novel (ANALYSE) reporting format. The overall aggregated mean score attained using the standard format was 53% (range = 38-82%, standard deviation = 12). Conversely, the overall aggregated mean score attained using ANALYSE format was 75% (range = 55%-93%, standard deviation = 9). A total of 14/15 participants consistently scored higher when interpreting electrocardiograms using the ANALYSE format (range = 10% -45%). A significant difference between the aggregated marks scored using the ANALYSE format and the standard format was calculated (Wilcoxon Z Score = -3.2374 (df = 14), p < 0.01). This study demonstrates the clinical utility of a novel method (ANALYSE) to assist the learning of electrocardiogram interpretation and its association with enhanced diagnostic performance in novices.

A computer-human interaction model to improve the diagnostic accuracy and clinical decision-making during 12-lead electrocardiogram interpretation.

INTRODUCTION: The 12-lead Electrocardiogram (ECG) presents a plethora of information and demands extensive knowledge and a high cognitive workload to interpret. Whilst the ECG is an important clinical tool, it is frequently incorrectly interpreted. Even expert clinicians are known to impulsively provide a diagnosis based on their first impression and often miss co-abnormalities. Given it is widely reported that there is a lack of competency in ECG interpretation, it is imperative to optimise the interpretation process. Predominantly the ECG interpretation process remains a paper based approach and whilst computer algorithms are used to assist interpreters by providing printed computerised diagnoses, there are a lack of interactive human-computer interfaces to guide and assist the interpreter. METHODS: An interactive computing system was developed to guide the decision making process of a clinician when interpreting the ECG. The system decomposes the interpretation process into a series of interactive sub-tasks and encourages the clinician to systematically interpret the ECG. We have named this model 'Interactive Progressive based Interpretation' (IPI) as the user cannot 'progress' unless they complete each sub-task. Using this model, the ECG is segmented into five parts and presented over five user interfaces (1: Rhythm interpretation, 2: Interpretation of the P-wave morphology, 3: Limb lead interpretation, 4: QRS morphology interpretation with chest lead and rhythm strip presentation and 5: Final review of 12-lead ECG). The IPI model was implemented using emerging web technologies (i.e. HTML5, CSS3, AJAX, PHP and MySQL). It was hypothesised that this system would reduce the number of interpretation errors and increase diagnostic accuracy in ECG interpreters. To test this, we compared the diagnostic accuracy of clinicians when they used the standard approach (control cohort) with clinicians who interpreted the same ECGs using the IPI approach (IPI cohort). RESULTS: For the control cohort, the (mean; standard deviation; confidence interval) of the ECG interpretation accuracy was (45.45%; SD=18.1%; CI=42.07, 48.83). The mean ECG interpretation accuracy rate for the IPI cohort was 58.85% (SD=42.4%; CI=49.12, 68.58), which indicates a positive mean difference of 13.4%. (CI=4.45, 22.35) An N-1 Chi-square test of independence indicated a 92% chance that the IPI cohort will have a higher accuracy rate. Interpreter self-rated confidence also increased between cohorts from a mean of 4.9/10 in the control cohort to 6.8/10 in the IPI cohort (p=0.06). Whilst the IPI cohort had greater diagnostic accuracy, the duration of ECG interpretation was six times longer when compared to the control cohort. CONCLUSIONS: We have developed a system that segments and presents the ECG across five graphical user interfaces. Results indicate that this approach improves diagnostic accuracy but with the expense of time, which is a valuable resource in medical practice.

Data Driven Computer Simulation to Analyse an ECG Limb Lead System Used in Connected Health Environments.

BACKGROUND: Recently under the Connected Health initiative, researchers and small-medium engineering companies have developed Electrocardiogram (ECG) monitoring devices that incorporate non-standard limb electrode positions, which we have named the Central Einthoven (CE) configuration. OBJECTIVES: The main objective of this study is to compare ECG signals recorded from the CE configuration with those recorded from the recommended Mason-Likar (ML) configuration. METHODS: This study involved extracting two different sets of ECG limb leads from each patient to compare the difference in the signals. This was done using computer simulation that is driven by body surface potential maps. This simulator was developed to facilitate this experiment but it can also be used to test similar hypotheses. This study included, (a) 176 ECGs derived using the ML electrode positions and (b) the 176 corresponding ECGs derived using the CE electrode positions. The signals from these ECGs were compared using root mean square error (RMSE), Pearson product-moment correlation coefficient (r) and similarity coefficient (SC). We also investigated whether the CE configuration influences the calculated mean cardiac axis. The top 10 cases where the ECGs were significantly different between the two configurations were visually compared by an ECG interpreter. RESULTS: We found that the leads aVL, III and aVF are most affected when using the CE configuration. The absolute mean difference between the QRS axes from both configurations was 28° (SD = 37°). In addition, we found that in 82% of the QRS axes calculated from the CE configuration was more rightward in comparison to the QRS axes derived from the ML configuration. Also, we found that there is an 18% chance that a misleading axis will be located in the inferior right quadrant when using the CE approach. Thus, the CE configuration can emulate right axis deviation. The clinician visually identified 6 out of 10 cases where the CE based ECG yielded clinical differences that could result in false positives. CONCLUSIONS: The CE configuration will not yield the same diagnostic accuracy for diagnosing pathologies that rely on current amplitude criteria. Conversely, rhythm lead II was not significantly affected, which supports the use of the CE approach for assessing cardiac rhythm only. Any computerised analysis of the CE based ECG will need to take these findings into consideration.

The evaluation of an open source online training system for teaching 12 lead electrocardiographic interpretation.

INTRODUCTION: The aim of this study is to present and evaluate the integration of a low resource JavaScript based ECG training interface (CrowdLabel) and a standardised curriculum for self-guided tuition in ECG interpretation. METHODS: Participants practiced interpreting ECGs weekly using the CrowdLabel interface to assist with the learning of the traditional didactic taught course material during a 6 week training period. To determine competency students were tested during week 7. RESULTS: A total of 245 unique ECG cases were submitted by each student. Accuracy scores during the training period ranged from 0-59.5% (median = 33.3%). Conversely accuracy scores during the test ranged from 30 - 70% (median = 37.5%) (p < 0.05). There was no correlation between students who interpreted high numbers of ECGs during the training period and their marks obtained. CONCLUSIONS: CrowdLabel is shown to be a readily accessible dedicated learning platform to support ECG interpretation competency.

An evaluation of eye tracking technology in the assessment of 12 lead electrocardiography interpretation.

INTRODUCTION: This study investigated eye tracking technology for 12 lead electrocardiography interpretation to Healthcare Scientist students. METHODS: Participants (n=33) interpreted ten 12 lead ECG recordings and randomized to receive objective individual appraisal on their efforts either by traditional didactic format or by eye tracker software. RESULTS: One hundred percent of participants reported the experience positively at improving their ECG interpretation competency. ECG analysis time ranged between 13.2 and 59.5s. The rhythm strip was the most common lead studied and fixated on for the longest duration (mean 9.9s). Lead I was studied for the shortest duration (mean 0.25s). Feedback using eye tracking data during ECG interpretation did not produce any significant variation between the assessment marks of the study and the control groups (p=0.32). CONCLUSIONS: Although the hypothesis of this study was rejected active teaching and early feedback practices are recommended within this discipline.

Evaluation of a direct access cardiac arrhythmia monitoring service.

BACKGROUND: This paper describes the clinical outcomes from a novel direct access arrhythmia monitoring service. METHODS: The study was carried out in the north of Scotland. Data was collected over a 29 month period between 18 June 2008 and 8 November 2010 from consecutive cases from two groups of patients, general practitioner (GP) direct access and 'redirected' consultant referrals. Monitor test results, frequency of arrhythmias requiring further care and clinic attendances were recorded. Statistical differences were analyzed using Χ(2), Fisher's and Student's t-test as appropriate with the significance taken at the 0.05 level. RESULTS: 239 patients were referred from 47 GP practices. There were 165 (69%) referrals through the 'direct' and 72 (31%) through the 'redirected' route. The average age was 55.5 ± 16.7 years with 84 (35.1%) males. 127 (53.1%) had a patient activated event recording and the remaining 112 (46.9%) had Holter monitoring. Of the 239 patients, only nine (3.8%) cases required referral to a consultant cardiologist. Of these, three were directly returned to GP care without consultant clinic review. Six patients with significant arrhythmias were reviewed at cardiology clinic. There were no adverse events. CONCLUSIONS: Direct access for cardiac arrhythmia monitoring seems to provide an effective mechanism for diverting inappropriate or non-essential referrals away from the cardiology clinic.