Identification of factors associated with return of spontaneous circulation after pediatric out-of-hospital cardiac arrest using natural language processing

Artificial intelligence to support out-of-hospital cardiac arrest care: A scoping review

Background

Artificial intelligence (AI) has demonstrated significant potential in supporting emergency medical services personnel during out-of-hospital cardiac arrest (OHCA) care; however, the extent of research evaluating this topic is unknown. This scoping review examines the breadth of literature on the application of AI in early OHCA care.

Methods

We conducted a search of PubMed®, Embase, and Web of Science in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews guidelines. Articles focused on non-traumatic OHCA and published prior to January 18th, 2023 were included. Studies were excluded if they did not use an AI intervention (including machine learning, deep learning, or natural language processing), or did not utilize data from the prehospital phase of care.

Results

Of 173 unique articles identified, 54 (31%) were included after screening. Of these studies, 15 (28%) were from the year 2022 and with an increasing trend annually starting in 2019. The majority were carried out by multinational collaborations (20/54, 38%) with additional studies from the United States (10/54, 19%), Korea (5/54, 10%), and Spain (3/54, 6%). Studies were classified into three major categories including ECG waveform classification and outcome prediction (24/54, 44%), early dispatch-level detection and outcome prediction (7/54, 13%), return of spontaneous circulation and survival outcome prediction (15/54, 20%), and other (9/54, 16%). All but one study had a retrospective design.

Conclusions

A small but growing body of literature exists describing the use of AI to augment early OHCA care.

Emergency Medical Services Leadership Perspectives on Implementation of Evidence-Based Guidelines: A Qualitative Study

Introduction: Prehospital evidence-based guidelines (EBGs) are developed to optimize clinical outcomes for emergency medical services (EMS) patients. However, widespread implementation of EBGs is often inconsistent. Therefore, this study aimed to assess the baseline knowledge and practices of EMS leaders related to EBG implementation.Methods: This was a qualitative study using focus groups to assess prehospital implementation awareness and knowledge. Participants were EMS EBG authors, EMS medical directors, and EMS professional organization leaders. Focus groups were held via video conference, audio recorded, and transcribed. Thematic coding used domains and constructs of the Consolidated Framework for Implementation Research (CFIR).Results: Six focus groups were conducted with a total of 18 participants. A total of 1,044 codes were analyzed. "Process" was the CFIR domain with the most codes (n = 350, 33.5%), followed by the "inner setting" (the EMS agency; n = 250, 23.9%), "characteristics of the intervention" (n = 203, 19.4%), "outer setting" (the health care system and community the EMS agency serves, and the broader national EMS professional context; n = 141, 13.5%), and "characteristics of individuals" (n = 100, 9.6%). The ten most frequent constructs across all domains were: reflecting and evaluating, executing, cosmopolitanism, planning, external policy and incentives, design quality and packaging, learning climate, culture, complexity, and other personal attributes.Conclusion: EMS leadership and stakeholder views on EBG implementation identified dominant themes related to the process of implementation and the culture and learning/implementation climate of EMS agencies. Opinions were mixed on the utility of the CFIR as a potential guide for EMS implementation. Further work is required to gain the frontline EMS clinician perspective on implementation and tie key themes to quantitative prehospital implementation outcomes.