BMC Emergency Medicine最新文献

Background: Violence against emergency medical service personnel is common and often underreported, with verbal abuse being the most frequent form, but physical and sexual violence also occur. The consequences of such violence range from psychological distress to physical harm and organizational burden. Despite growing evidence of the phenomenon, the dynamic, situational and interactional nature of this violence as well as effective prevention strategies are still insufficiently understood. The objective of this study was to examine factors contributing to violence against EMS personnel before, during and after violent incidents.

Methods: This qualitative study employed two multiprofessional stakeholder workshops (n = 36) conducted in Finland in October 2024. Participants included professionals from the EMS setting and its related fields, as well as patient representatives. The workshops produced experiences and perspectives on violence against EMS personnel, focusing on incident dynamics of violent encounters. Data was analyzed using thematic analysis.

Results: Violence towards EMS personnel presents itself through three interrelated themes: the aggressor, the EMS personnel, and the organization and the EMS system. The findings indicate that these themes interact dynamically, with violent incidents shaped by individual behavior, professional practice and institutional conditions. Aggressor-related factors such as substance use, psychiatric illness, and altered mental states were described as increasing risk for violence. EMS personnel's fatigue, inexperience, and moralizing attitudes were seen to increase vulnerability, whereas resilience and situational awareness were protective. At the organizational level, unclear risk-assessment protocols, inconsistent cooperation with the police, and limited post-incident support emerged as key challenges.

Conclusions: Violence against EMS presents itself as a multifactorial and dynamic phenomenon involving aggressor behavior, EMS personnel actions and organizational structures. Embedding de-escalation and disengagement competencies into training, strengthening system-level safeguards and implementing post-incident support are essential. The development of predictive models on how to manage risks at every stage of the EMS mission is warranted.

Background: Current cardiopulmonary resuscitation (CPR) includes breaks to check rhythm and pulse, while high-quality CPR requires continual chest compressions. Artificial intelligence (AI)-driven "closed-loop" or "near-closed-loop" systems may evaluate CPR-contaminated signals, guide shock and pulse decisions.

Methods: According to PRISMA statement 2020, We searched databases from 2010 to 2025 for studies on animal or human cardiac arrest where AI-based or algorithmic tools gave real-time intended guidance during CPR or CPR-dominant analysis windows. Evidence was divided into animal perfusion-targeted closed-loop CPR, human shock-advisory algorithms, and human pulse/perfusing-rhythm detection. We classified shockable and non-shockable instances using a bivariate random-effects model and meta-analyses. Many studies reported performance on many electrocardiogram segments per episode; therefore, aggregated findings should be taken as segment-level diagnostic performance, not episode-level accuracy.

Results: Fourteen studies met the inclusion criteria: three porcine closed-loop experiments, eight human shock-advisory studies, two pulse-detection studies, and one simulated individualized strategy. Eight shock-advisory studies demonstrated the pooled sensitivity = 0.93 and the specificity = 0.97. There were substantial variation and a high risk of bias in the patient selection and index test domains. Pulse-detection models worked well during clean pauses but not during compressions. Animal controllers enhanced coronary perfusion pressure, end-tidal carbon dioxide (EtCO₂), or ventricular fibrillation surrogates relative to fixed-depth CPR; however, these effects were transient.

Conclusions: AI systems can execute guideline CPR in some datasets, and animal studies confirm perfusion-targeted control's physiological viability. Closed-loop AI-guided CPR is intriguing yet unvalidated, requiring episode-level clinical studies due to fragmented, varied, and methodologically problematic evidence.

Registration: PROSPERO CRD420251239485.