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Objective: To assess Australian and New Zealand emergency clinicians' attitudes towards the use of artificial intelligence (AI) in emergency medicine. Methods: We undertook a qualitative interview‐based study based on grounded theory. Participants were recruited through ED internal mailing lists, the Australasian College for Emergency Medicine Bulletin, and the research teams' personal networks. Interviews were transcribed, coded and themes presented. Results: Twenty‐five interviews were conducted between July 2021 and May 2022. Thematic saturation was achieved after 22 interviews. Most participants were from either Western Australia (52%) or Victoria (16%) and were consultants (96%). More participants reported feeling optimistic (10/25) than neutral (6/25), pessimistic (2/25) or mixed (7/25) towards the use of AI in the ED. A minority expressed scepticism regarding the feasibility or value of implementing AI into the ED. Multiple potential risks and ethical issues were discussed by participants including skill loss from overreliance on AI, algorithmic bias, patient privacy and concerns over liability. Participants also discussed perceived inadequacies in existing information technology systems. Participants felt that AI technologies would be used as decision support tools and not replace the roles of emergency clinicians. Participants were not concerned about the impact of AI on their job security. Most (17/25) participants thought that AI would impact emergency medicine within the next 10 years. Conclusions: Emergency clinicians interviewed were generally optimistic about the use of AI in emergency medicine, so long as it is used as a decision support tool and they maintain the ability to override its recommendations. This qualitative study found that emergency clinicians in Australia and New Zealand were generally optimistic about the use of artificial intelligence (AI)‐enabled decision support tools in EDs. A number of issues were raised that require attention before full acceptance. AI‐enabled tools must operate as decision support only, be locally validated, be effectively integrated into existing workflows and take ethical concerns into consideration. [ABSTRACT FROM AUTHOR]

Ultrasound is used in cardiopulmonary resuscitation (CPR) and advanced life support (ALS). However, there is divergence between the recommendations of many emergency and critical care societies who support its use and the recommendations of many international resuscitation organizations who either recommend against its use or recommend it only in limited circumstances. Ultrasound offers potential benefits of detecting reversable causes of cardiac arrest, allowing specific interventions. However, it also risks interfering with ALS protocols and increasing unhelpful interventions. As with many interventions in ALS, the evidence base for ultrasound use is weak, and well-designed randomized trials are needed. This paper reviews the current theory and evidence for harms and benefits. [ABSTRACT FROM AUTHOR]

The article examines the potential impact of artificial intelligence (AI) on emergency department (ED) overcrowding and access block. It discusses how AI could help address these issues through various stages of patient care, including input, throughput, and output, while also highlighting the limitations and challenges of AI integration in clinical practice.

Objective: The Australasian College for Emergency Medicine Curriculum Framework contains numerous mentions of point‐of‐care ultrasound (PoCUS). However, obtaining formal PoCUS credentials is often problematic. The Fiona Stanley Hospital ED PoCUS training programme was devised to assist emergency medicine trainees to meet the credentialing requirements of the Australasian College for Emergency Medicine and the Australasian Society for Ultrasound in Medicine. Methods: Six emergency medicine registrars are selected for each 6‐month semester. Successful applicants nominate two modules of Australasian Society for Ultrasound in Medicine's Certificate in Clinician Performed Ultrasound and receive dedicated non‐clinical time. For 3 h a week, an emergency physician holding formal PoCUS credentials supervises a pair of trainees while they perform scans on ED patients. During these sessions, trainee logbooks can be reviewed and assessments occur as required by the module. Results: Over an 18‐month period, 18 emergency registrars were involved, averaging eight 3‐h sessions each. All selected the Extended Focused Abdominal Scan for Trauma module, 14 chose Abdominal Aortic Aneurysm and eight chose Basic Echo in Life Support. Overall, 30 (75%) of 40 modules were completed within the trainees' 6‐month semester. Just under half of logged scans were obtained during the supervised sessions. Overall, the average number of scans performed exceeded each module's logbook requirements. Trainees perceived that involvement in the programme benefited their ability to manage patients. There was overwhelming support for the structure of the programme. Conclusions: The Fiona Stanley Hospital ED model is effective in assisting emergency medicine trainees to gain formal PoCUS credentials. As it requires relatively little organisation, time and staffing, it could be adopted in many EDs around Australia and New Zealand. [ABSTRACT FROM AUTHOR]

Objective: To determine if an ultrasound‐guided femoral nerve block (FNB) is superior to an ultrasound‐guided fascia iliaca compartment block (FICB) in providing pain relief to patients with a neck of femur or proximal femoral fracture. Methods: A double‐blind randomised controlled trial was conducted. All participants received two blocks, one active and one placebo. An active FICB was administered to 52 participants and 48 participants received an active FNB. Results: Analysis was completed on data collected from 100 participants. Most patients were elderly and the majority were female. Both FICB and FNB achieved clinically significant mean reductions in pain scores (2.62 for FICB and 2.3 for FNB). There was no significant difference in reduction in pain scores between the two cohorts, P = 0.408. Conclusions: Ultrasound‐guided FNB is not superior to ultrasound‐guided FICB, with both facilitating an equivalent analgesia effect in patients with a neck of femur or proximal femur fracture. [ABSTRACT FROM AUTHOR]

Objectives Methods Results Conclusion To investigate health consumers' ethical concerns towards the use of artificial intelligence (AI) in EDs.Qualitative semi‐structured interviews with health consumers, recruited via health consumer networks and community groups, interviews conducted between January and August 2022.We interviewed 28 health consumers about their perceptions towards the ethical use of AI in EDs. The results discussed in this paper highlight the challenges and barriers for the effective and ethical implementation of AI from the perspective of Australian health consumers. Most health consumers are more likely to support AI health tools in EDs if they continue to be involved in the decision‐making process. There is considerably more approval of AI tools that support clinical decision‐making, as opposed to replacing it. There is mixed sentiment about the acceptability of AI tools influencing clinical decision‐making and judgement. Health consumers are mostly supportive of the use of their data to train and develop AI tools but are concerned with who has access. Addressing bias and discrimination in AI is an important consideration for some health consumers. Robust regulation and governance are critical for health consumers to trust and accept the use of AI.Health consumers view AI as an emerging technology that they want to see comprehensively regulated to ensure it functions safely and securely with EDs. Without considerations made for the ethical design, implementation and use of AI technologies, health consumer trust and acceptance in the use of these tools will be limited. [ABSTRACT FROM AUTHOR]