Perceptions and concerns of emergency medicine practitioners about artificial intelligence in emergency triage management during the pandemic: a national survey-based study.

Objective: There have been continuous discussions over the ethics of using AI in healthcare. We sought to identify the ethical issues and viewpoints of Turkish emergency care doctors about the use of AI during epidemic triage. Materials and methods: Ten emergency specialists were initially enlisted for this project, and their responses to open-ended questions about the ethical issues surrounding AI in the emergency room provided valuable information. A 15-question survey was created based on their input and was refined through a pilot test with 15 emergency specialty doctors. Following that, the updated survey was sent to emergency specialists via email, social media, and private email distribution. Results: 167 emergency medicine specialists participated in the study, with an average age of 38.22 years and 6.79 years of professional experience. The majority agreed that AI could benefit patients (54.50%) and healthcare professionals (70.06%) in emergency department triage during pandemics. Regarding responsibility, 63.47% believed in shared responsibility between emergency medicine specialists and AI manufacturers/programmers for complications. Additionally, 79.04% of participants agreed that the responsibility for complications in AI applications varies depending on the nature of the complication. Concerns about privacy were expressed by 20.36% regarding deep learning-based applications, while 61.68% believed that anonymity protected privacy. Additionally, 70.66% of participants believed that AI systems would be as sensitive as humans in terms of non-discrimination. Conclusion: The potential advantages of deploying AI programs in emergency department triage during pandemics for patients and healthcare providers were acknowledged by emergency medicine doctors in Turkey. Nevertheless, they expressed notable ethical concerns related to the responsibility and accountability aspects of utilizing AI systems in this context.

Value of the Glasgow coma scale, age, and arterial blood pressure score for predicting the mortality of major trauma patients presenting to the emergency department.

BACKGROUND: The purpose of this study is to detect the mortality predictive power of new Glasgow coma scale, age, and arterial pressure (GAP) scoring system in major trauma patients admitted to the emergency department (ED). METHODS: A total of 100 major trauma patients admitted to Uludag University Faculty of Medicine ED who were 18 years of age or more were included in the study. In this prospective study, revised trauma score (RTS), injury severity score (ISS), trauma-related ISS (TRISS), Mechanism, GAP (MGAP) and GAP scores of the patients were calculated. RESULTS: A significant positive correlation was established between ISS, TRISS, MGAP, and GAP in predicting in-hospital mortality (p<0.0001). Short-term (24 hours) and long-term (4-week) mortality prediction rates and area under the curve in receiver operating characteristics analysis were 0.727-0.680 for RTS, 0.863-0.816 for ISS, 0.945-0,911 for TRISS, 0.970-0.938 for MGAP, and 0.910-0.904 for GAP. All calculated trauma scoring systems revealed a significant mortality prediction power (p<0.001). GAP score was found statistically and significantly selective and sensitive in predicting both in-ED and in-hospital mortality (p=0.0001). CONCLUSION: In major trauma patients, GAP score is an easily calculable system both in the field and at the time of admission in the EDs by providing emergency physicians with future decision-making schemes by means of mortality prediction of the patients.