Teaching Disaster Medicine With a Novel Game-Based Computer Application: A Case Study at Sichuan University

Training nurses in an international emergency medical team using a serious role-playing game: a retrospective comparative analysis

BACKGROUND

Although game-based applications have been used in disaster medicine education, no serious computer games have been designed specifically for training these nurses in an IEMT setting. To address this need, we developed a serious computer game called the IEMTtraining game. In this game, players assume the roles of IEMT nurses, assess patient injuries in a virtual environment, and provide suitable treatment options.

METHODS

The design of this study is a retrospective comparative analysis. The research was conducted with 209 nurses in a hospital. The data collection process of this study was conducted at the 2019-2020 academic year. A retrospective comparative analysis was conducted on the pre-, post-, and final test scores of nurses in the IEMT. Additionally, a survey questionnaire was distributed to trainees to gather insights into teaching methods that were subsequently analyzed.

RESULTS

There was a significant difference in the overall test scores between the two groups, with the game group demonstrating superior performance compared to the control group (odds ratio = 1.363, p value = 0.010). The survey results indicated that the game group exhibited higher learning motivation scores and lower cognitive load compared with the lecture group.

CONCLUSIONS

The IEMT training game developed by the instructor team is a promising and effective method for training nurses in disaster rescue within IEMTs. The game equips the trainees with the necessary skills and knowledge to respond effectively to emergencies. It is easily comprehended, enhances knowledge retention and motivation to learn, and reduces cognitive load.

Improving Nursing Students' COVID-19 Knowledge Using a Serious Game

Improving nursing students' understanding of the novel COVID-19 is an essential component of their education. We designed and developed a serious game-based computer learning application that requires nursing students to solve problems through simulated cases. This study aimed to compare knowledge mastery among nursing students who used this teaching method versus those who received online lectures. A retrospective observational study was conducted using preclass, postclass, and final test scores of 130 students retrieved from the university's database of test scores. Both teaching methods produced significant increases in short-term knowledge of COVID-19. There was no statistical difference between the two methods in pre- and postclass scores; however, the serious game group scored higher than the online lecture group in knowledge retention. In summary, the serious game application is a potentially effective method for COVID-19 education among nursing students, particularly in terms of its capacity for improved knowledge retention.

Comorbidity-adjusted NEWS predicts mortality in suspected patients with COVID-19 from nursing homes: Multicentre retrospective cohort study

Aims

To assess the prognostic accuracy of comorbidity‐adjusted National Early Warning Score in suspected Coronavirus disease 2019 patients transferred from nursing homes by the Emergency Department.

Design

Multicentre retrospective cohort study.

Methods

Patients transferred by high‐priority ambulances from nursing homes to Emergency Departments with suspected severe acute respiratory syndrome coronavirus 2 infection, from March 12 to July 31 2020, were considered. Included variables were: clinical covariates (respiratory rate, oxygen saturation, systolic blood pressure, heart rate, temperature, level of consciousness and supplemental oxygen use), the presence of comorbidities and confirmatory analytical diagnosis of severe acute respiratory syndrome coronavirus 2 infection. The primary outcome was a 2‐day mortality rate. The discriminatory capability of the National Early Warning Score was assessed by the area under the receiver operating characteristic curve in two different cohorts, the validation and the revalidation, which were randomly selected from the main cohort.

Results

A total of 337 nursing homes, 10 advanced life support units, 51 basic life support units and 8 hospitals in Spain entailing 1,324 patients (median age 87 years) was involved in this study. Two‐day mortality was 11.5% (152 cases), with a positivity rate of severe acute respiratory syndrome coronavirus 2 of 51.2%, 77.7% of hospitalization from whom 1% was of intensive care unit admission. The National Early Warning Score results for the revalidation cohort presented an AUC of 0.771, and of 0.885, 0.778 and 0.730 for the low‐, medium‐ and high‐level groups of comorbidities.

Conclusion

The comorbidity‐adjusted National Early Warning Score provides a good short‐term prognostic criterion, information that can help in the decision‐making process to guide the best strategy for each older adult, under the current pandemic.

Impact

What problem did the study address?

Under the current coronavirus disease 2019 pandemic, targeting older adults at high risk of deterioration in nursing homes remains challenging.

What were the main findings?

Comorbidity‐adjusted National Early Warning Score helps to forecast the risk of clinical deterioration more accurately.

Where and on whom will the research have impact?

A high NEWS, with a low level of comorbidity is associated with optimal predictive performance, making these older adults likely to benefit from continued follow up and potentially hospital referral under the current coronavirus disease 2019 pandemic.

The role of emergency department triage early warning score (TREWS) and modified early warning score (MEWS) to predict in-hospital mortality in COVID-19 patients

Background

It is necessary to identify critical patients requiring hospitalization early due to the rapid increase in the number of COVID-19 cases.

Aim

This study aims to evaluate the effectiveness of scoring systems such as emergency department triage early warning score (TREWS) and modified early warning score (MEWS) in predicting mortality in COVID-19 patients.

Methods

In this retrospective cohort study, PCR positive patients evaluated for COVID-19 and decided to be hospitalized were evaluated. During the first evaluation, MEWS and TREWS scores of the patients were calculated. Intensive care needs as well as 24-h and 28-day mortality rates were evaluated.

Results

A total of 339 patients were included in the study. While 30 (8.8%) patients were hospitalized in the intensive care unit, 4 (1.2%) died in the emergency. The number of patients who died within 28 days was found to be 57 (16.8%). In 24-h mortality, the median MEWS value was found to be 7 (IQR 25–75) while the TREWS value was 11.5 (IQR 25–75). In the ROC analysis made for the diagnostic value of 28-day mortality of MEWS and TREWS scores, the area under the curve (AUC) for the MEWS score was found to be 0.833 (95% CI 0.777–0.888, p < 0.001) while it was identified as 0.823 (95% CI 0.764–0.882, p < 0.001) for the TREWS.

Conclusion

MEWS and TREWS calculated at emergency services are effective in predicting 28-day mortality in patients requiring hospitalization due to COVID-19.