RESUMEN
Our scoping review provides a comprehensive analysis of the landscape of artificial intelligence (AI) applications in prehospital emergency care (PEC). It contributes to the field by highlighting the most studied AI applications and identifying the most common methodological approaches across 106 included studies. The findings indicate a promising future for AI in PEC, with many unique use cases, such as prognostication, demand prediction, resource optimization, and the Internet of Things continuous monitoring systems. Comparisons with other approaches showed AI outperforming clinicians and non-AI algorithms in most cases. However, most studies were internally validated and retrospective, highlighting the need for rigorous prospective validation of AI applications before implementation in clinical settings. We identified knowledge and methodological gaps using an evidence map, offering a roadmap for future investigators. We also discussed the significance of explainable AI for establishing trust in AI systems among clinicians and facilitating real-world validation of AI models.
RESUMEN
OBJECTIVES: To explore student and staff satisfaction with the use of medical students as a surge workforce during the COVID-19 pandemic. METHOD: We conducted a mixed methods analysis of staff and student experiences with the medical student workforce at a single metropolitan ED over an 8-month period between December 2021 and July 2022, using an online survey tool. Students were invited to complete the survey fortnightly, whereas senior medical and nursing staff were invited weekly. RESULTS: There was a 32% response rate for surveys sent to medical student assistants (MSAs) and 18% and 15% for medical and nursing staff, respectively. Most students felt well prepared and supported in the role and would recommend it to other students. They reported that the role allowed them to gain experience and confidence within the ED, especially after much of their learning had moved online throughout the pandemic. Senior nurses and doctors found MSAs to be useful members of the team, largely through their assistance with task completion. Both staff and students recommended a more comprehensive orientation, changes to the supervision model and increased clarity in the students' scope of practice. CONCLUSIONS: The results of the present study provide insight into the use of medical students as an emergency surge workforce. Responses from medical students and staff suggested that the project was beneficial for both groups as well as for overall departmental performance. These findings are likely to be translatable beyond the COVID-19 pandemic setting.