An Adapted Hybrid Model for Hands-On Practice on Disaster and Military Medicine Education in Undergraduate Medical Students During the COVID-19 Pandemic

Background The coronavirus disease 2019 (COVID-19) pandemic has resulted in substantial impacts on all aspects of medical education. Modern health systems must prepare for a wide variety of catastrophic scenarios, including emerging infectious disease outbreaks and human and natural disasters. During the COVID-19 pandemic, while the use of traditional teaching methods has decreased, the use of online-based teaching methods has increased. COVID-19 itself and the accompanying infection control measures have restricted full-scale practice. Therefore, we developed an adapted hybrid model that retained adequate hands-on practice and educational equality, and we applied it with a group of undergraduate medical students participating in a mandatory disaster education course in a military medical school. Methods The course covered the acquisition of skills used in emergency and trauma scenarios through designated interdisciplinary modules on disaster responses. Several asynchronous and synchronous online webinars were used in this one-credit mandatory disaster and military medicine education course. To allow opportunities for hands-on practice and ensure education equality, the students were divided into 15 groups, with 12 students in each group. The hands-on practice exercises were also recorded and disseminated to the students in the designated area for online learning. Results A total of 164 3rd-year medical students participated in this mandatory disaster and military medicine course during the COVID-19 pandemic. The satisfaction survey response rate was 96.5%. The students were satisfied with the whole curriculum (3.8/5). Most of the free-text comments regarding the course represented a high level of appreciation. The students felt more confident in the knowledge and skills they gained in hands-on exercises than they did in the knowledge and skills they gained in online exercises. The students showed significant improvements in knowledge after the course. Conclusions We demonstrated that this adapted hybrid arrangement provided an enhanced learning experience, but we also found that medical students were more confident in their knowledge and skills when they had real hands-on practice.

Development and Validation of an Artificial Intelligence Electrocardiogram Recommendation System in the Emergency Department.

The machine learning-assisted electrocardiogram (ECG) is increasingly recognized for its unprecedented capabilities in diagnosing and predicting cardiovascular diseases. Identifying the need for ECG examination early in emergency department (ED) triage is key to timely artificial intelligence-assisted analysis. We used machine learning to develop and validate a clinical decision support tool to predict ED triage patients' need for ECG. Data from 301,658 ED visits from August 2017 to November 2020 in a tertiary hospital were divided into a development cohort, validation cohort, and two test cohorts that included admissions before and during the COVID-19 pandemic. Models were developed using logistic regression, decision tree, random forest, and XGBoost methods. Their areas under the receiver operating characteristic curves (AUCs), positive predictive values (PPVs), and negative predictive values (NPVs) were compared and validated. In the validation cohort, the AUCs were 0.887 for the XGBoost model, 0.885 for the logistic regression model, 0.878 for the random forest model, and 0.845 for the decision tree model. The XGBoost model was selected for subsequent application. In test cohort 1, the AUC was 0.891, with sensitivity of 0.812, specificity of 0.814, PPV of 0.708 and NPV of 0.886. In test cohort 2, the AUC was 0.885, with sensitivity of 0.816, specificity of 0.812, PPV of 0.659, and NPV of 0.908. In the cumulative incidence analysis, patients not receiving an ECG yet positively predicted by the model had significantly higher probability of receiving the examination within 48 h compared with those negatively predicted by the model. A machine learning model based on triage datasets was developed to predict ECG acquisition with high accuracy. The ECG recommendation can effectively predict whether patients presenting at ED triage will require an ECG, prompting subsequent analysis and decision-making in the ED.

Impact of the COVID-19 Pandemic on the Loading and Quality of an Emergency Department in Taiwan: Enlightenment from a Low-Risk Country in a Public Health Crisis.

The impact of the coronavirus disease 2019 (COVID-19) pandemic on health-care quality in the emergency department (ED) in countries with a low risk is unclear. This study aimed to explore the effects of the COVID-19 pandemic on ED loading, quality of care, and patient prognosis. Data were retrospectively collected from 1 January 2018 to 30 September 2020 at the ED of Tri-service general hospital. Analyses included day-based ED loading, quality of care, and patient prognosis. Data on triage assessment, physiological states, disease history, and results of laboratory tests were collected and analyzed. The number of daily visits significantly decreased after the pandemic, leading to a reduction in the time to examination. Admitted patients benefitted from the pandemic with a reduction of 0.80 h in the length of stay in the ED, faster discharge without death, and reduced re-admission. However, non-admitted visits with chest pain increased the risk of mortality after the pandemic. In conclusion, the COVID-19 pandemic led to a significant reduction in low-acuity ED visits and improved prognoses for hospitalized patients. However, clinicians should be alert about patients with chest pain due to their increased risk of mortality in subsequent admission.