Importance:Guideline-adherent management of pediatric in-hospital cardiac arrest (IHCA) remains challenging, and deviations from best practices are common. Augmented-reality (AR)-enabled, role-specific decision support may improve adherence to American Heart Association (AHA) Pediatric Advanced Life Support (PALS) guidance and key performance metrics. Objective:To determine whether an AR-enhanced, role-specific decision support system improves resuscitation performance and adherence to AHA PALS guidelines during simulated pediatric IHCA. Design, Setting, and Participants:This open-label, multicenter, simulation-based randomized clinical trial was conducted from April to May 2025 at 2 tertiary pediatric emergency centers (Geneva, Switzerland and Alberta, Canada). Participants included teams of pediatric nurses and physicians. Intervention:Teams managed a standardized scenario of a 12-minute IHCA due to hyperkalemia (progressing from nonshockable to shockable rhythms) using the AR-enhanced, role-specific decision support system (intervention) or AHA PALS pocket cards (control). Main Outcomes and Measures:The primary outcome was time from recognition of loss of pulse to first epinephrine. Secondary outcomes included adherence to 3- to 5-minute epinephrine dosing intervals, time to first defibrillation, adherence to 2-minute shock and rhythm-check cycles, chest compression fraction, peri-shock pause, medication-dosing accuracy, and user experience and technology acceptance. Results:A total of 54 participants were randomized into 18 teams (18 team leaders [12 female [71%] and 36 nurses [33 female [87%]), with 9 teams (27 participants) in each group. Mean (SD) time to first epinephrine was shorter in the intervention group (97.2 [38.5] vs 113.8 [44.5] seconds; mean difference, -16.6 seconds; 95% CI, -51.3 to 17.0 seconds; P = .40), but this difference was not significant. For subsequent epinephrine, the intervention group improved consistency: mean (SD) deviation from the 4-minute target was 17.2 (32.5) vs 49.7 (40.3) seconds (mean difference, -32.4 seconds; 95% CI, -58.8 to -5.8 seconds; P = .03), with fewer guideline violations (2 of 19 participants [11%] vs 9 of 21 participants [43%]; risk difference, -0.32; 95% CI, -0.55 to -0.05; risk ratio, 0.25; 95% CI, 0.06 to 0.996; P = .03). Time to first defibrillation and adherence to 2-minute cycles were similar between groups. Estimates for chest compression fraction, peri-shock pauses, and medication-dosing accuracy did not suggest meaningful between-group difference. User experience and technology acceptance were favorable. Conclusions and Relevance:In this randomized clinical trial, AR support did not clearly improve time to first epinephrine in simulated pediatric cardiac arrest, with estimates compatible with both benefit and little or no effect. It improved adherence to epinephrine dosing intervals without impairing other performance domains. Trial Registration:ClinicalTrials.gov Identifier: NCT06376643.

This randomized clinical trial evaluates the effect of an augmented-reality decision support tool on time to epinephrine and adherence to pediatric advanced life support guidelines during simulated in-hospital pediatric cardiac arrest. QuestionDoes a multifaceted, augmented reality (AR)-enhanced, role-specific clinical decision support system improve adherence to American Heart Association (AHA) Pediatric Advanced Life Support (PALS) guidelines and key performance metrics during simulated pediatric in-hospital cardiopulmonary arrest compared with AHA PALS pocket cards?FindingsIn this randomized clinical trial of 18 teams of pediatric nurses and physicians (54 participants) there was no statistically significant difference in time to first epinephrine between groups, while adherence to epinephrine dosing intervals improved with AR support.MeaningWhile AR support did not clearly improve time to first epinephrine in this study, findings suggest it may improve adherence to resuscitation guidelines without impairing other performance metrics. ImportanceGuideline-adherent management of pediatric in-hospital cardiac arrest (IHCA) remains challenging, and deviations from best practices are common. Augmented-reality (AR)-enabled, role-specific decision support may improve adherence to American Heart Association (AHA) Pediatric Advanced Life Support (PALS) guidance and key performance metrics.ObjectiveTo determine whether an AR-enhanced, role-specific decision support system improves resuscitation performance and adherence to AHA PALS guidelines during simulated pediatric IHCA.Design, Setting, and ParticipantsThis open-label, multicenter, simulation-based randomized clinical trial was conducted from April to May 2025 at 2 tertiary pediatric emergency centers (Geneva, Switzerland and Alberta, Canada). Participants included teams of pediatric nurses and physicians.InterventionTeams managed a standardized scenario of a 12-minute IHCA due to hyperkalemia (progressing from nonshockable to shockable rhythms) using the AR-enhanced, role-specific decision support system (intervention) or AHA PALS pocket cards (control).Main Outcomes and MeasuresThe primary outcome was time from recognition of loss of pulse to first epinephrine. Secondary outcomes included adherence to 3- to 5-minute epinephrine dosing intervals, time to first defibrillation, adherence to 2-minute shock and rhythm-check cycles, chest compression fraction, peri-shock pause, medication-dosing accuracy, and user experience and technology acceptance.ResultsA total of 54 participants were randomized into 18 teams (18 team leaders [12 female [71%] and 36 nurses [33 female [87%]), with 9 teams (27 participants) in each group. Mean (SD) time to first epinephrine was shorter in the intervention group (97.2 [38.5] vs 113.8 [44.5] seconds; mean difference, -16.6 seconds; 95% CI, -51.3 to 17.0 seconds; P = .40), but this difference was not significant. For subsequent epinephrine, the intervention group improved consistency: mean (SD) deviation from the 4-minute target was 17.2 (32.5) vs 49.7 (40.3) seconds (mean difference, -32.4 seconds; 95% CI, -58.8 to -5.8 seconds; P = .03), with fewer guideline violations (2 of 19 participants [11%] vs 9 of 21 participants [43%]; risk difference, -0.32; 95% CI, -0.55 to -0.05; risk ratio, 0.25; 95% CI, 0.06 to 0.996; P = .03). Time to first defibrillation and adherence to 2-minute cycles were similar between groups. Estimates for chest compression fraction, peri-shock pauses, and medication-dosing accuracy did not suggest meaningful between-group difference. User experience and technology acceptance were favorable.Conclusions and RelevanceIn this randomized clinical trial, AR support did not clearly improve time to first epinephrine in simulated pediatric cardiac arrest, with estimates compatible with both benefit and little or no effect. It improved adherence to epinephrine dosing intervals without impairing other performance domains.Trial RegistrationClinicalTrials.gov Identifier: NCT06376643

BackgroundEffective team communication is critical in pediatric cardiopulmonary arrest management, where delays or miscommunication can jeopardize survival. TeamScreen, a web-based interface displayed on a large screen, was developed to enhance cardiopulmonary resuscitation (CPR) by providing real-time visualization of clinical data and resuscitation steps aligned with the American Heart Association pediatric advanced life support algorithms. ObjectiveThis study evaluated the usability of the TeamScreen Figma prototype, evaluating how efficiently and accurately experienced emergency physicians and nurses retrieved critical information during a simulated pediatric in-hospital cardiac arrest scenario. Although no strict time constraints were imposed, participants were instructed to perform the tasks as spontaneously and as quickly as possible. MethodsUsability testing involved 20 pediatric emergency physicians and nurses with varied CPR experience. Participants performed 21 information retrieval tasks within a simulated pediatric cardiac arrest scenario (shockable rhythm). The data collected included audio-video recordings via the think-aloud method and participant responses to the Post-Study System Usability Questionnaire (PSSUQ) version 3 and a posttest survey. Effectiveness, efficiency, and satisfaction were measured by task completion rates, time-on-task metrics, and PSSUQ scores, respectively. Think-aloud data were analyzed for usability issues using Nielsen Norman Group’s rating scale and Bastien and Scapin’s ergonomic criteria. ResultsFive physicians and 15 nurses achieved a mean task success rate of 81.19% (SD 16.87%), with a mean completion time of 8.13 (SD 7.07) seconds, calculated across all 21 tasks and all participants. PSSUQ scores reflected high satisfaction (mean 2.40 [SD 1.24] of 7.00; the lower the better), notably for information clarity and system utility. Qualitative analyses identified 16 usability issues, of which 5 were deemed major, primarily involving information visibility, navigation, and density, highlighting areas for interface and workflow enhancement. ConclusionsThe usability evaluation confirmed TeamScreen’s potential to improve real-time information access during pediatric CPR, with high task success and satisfaction scores supporting its role in aiding decision-making. Challenges with visibility, navigation, and information density require further refinement. These findings will guide improvements and inform the design of multicenter trials to assess TeamScreen’s efficacy in simulation-based resuscitation settings.

Abstract Background In cardiac arrest management, cognitive aids provide prompts to encourage recall of critical information, which may improve clinical performance. Whether cognitive aids influence provider workload, cognitive load, teamwork dynamics, or leadership during cardiac arrest remains unknown. In this study, we evaluated the effect of using a multi-faceted decision support system with augmented reality-based cognitive aids (i.e. InterFACE-AR) vs. the American Heart Association (AHA) Pediatric Advanced Life Support (PALS) pocket card on provider workload and cognitive load, teamwork, and leadership during simulated pediatric cardiac arrest. Methods We conducted secondary analysis of data collected from a prospective, randomized controlled trial comparing the use of the InterFACE-AR system to the AHA PALS pocket card during simulated pediatric cardiac arrest. Participants were recruited in groups of 3 to perform the roles of team leader, medication nurse, and documenting nurse. All teams completed a 12-min simulated cardiac arrest scenario. Provider workload (NASA-RTLX) and cognitive load (Paas score) were captured from participants after the scenario. Teamwork (TEAM score) and leadership performance (CALM score) were assessed via video review. Results A total of 18 simulation sessions were analyzed (Control: n = 9; InterFACE-AR: n = 9), involving 54 participants in total. Team leaders using the InterFACE-AR system had lower RTLX (mean difference [MD]: -15.0; 95% confidence interval [CI]: -27.0 to -4.6, p = 0.022) and Paas score (MD: -2.4; 95%CI: -3.6 to -1.4, p < 0.001), while documenting nurses showed similar reductions (RTLX -13.7, 95%CI: -26.7 to -0.4, p = 0.049; Paas -1.6, 95%CI: -2.8 to -0.1, p = 0.046) compared with those using PALS pocket card. Medication nurses demonstrated no statistically significant differences in RTLX (p = 0.098) or Paas score (p = 0.194). Teams using the InterFACE-AR system achieved significantly higher TEAM scores compared to those using PALS pocket card only (39.2 vs 35.8, MD: 3.4, 95%CI: 0.8 – 5.9, p = 0.030). CALM scores did not differ significantly between groups. Conclusion Use of an AR-based decision support system during simulated pediatric cardiac arrest reduces workload and cognitive load for the team leader and documenting nurse, but does not affect workload or cognitive load of medication nurses. Use of the InterFACE-AR system seems to improve teamwork performance but does not influence leadership performance of team leaders. Trial registration ClinicalTrials.gov. Identifier: NCT06376643 .

Pediatric cardiac arrests are time-sensitive events requiring effective team communication, situational awareness, and rapid decision-making. To support resuscitation teams, we developed InterFACE-AR, a digital system designed to improve adherence to American Heart Association’s Pediatric Advanced Life Support guidelines. One key component, the Guiding Pad tablet app, is a bedside cognitive aid that enables nurses to document team actions and resuscitation events in real time while providing algorithm-based prompts to guide the team’s next step in the care pathway. This study aimed to evaluate the usability of the Guiding Pad app by assessing its effectiveness, efficiency, and user satisfaction (aesthetic, ease of use, and clarity of content). We also identified usability problems and proposed design improvements. Usability tests were conducted among pediatric emergency nurses who completed a simulated pediatric cardiac arrest scenario using the Guiding Pad. Participants were asked to perform 27 predefined tasks while verbalizing their thought process using the think-aloud method. Following the scenario, they completed the Post-Study System Usability Questionnaire (PSSUQ) and participated in a semi-structured interview. Data sources included audio and screen recordings, questionnaire responses, and interview notes. Quantitative outcomes were task completion and success rates, task duration, number of clicks, and PSSUQ scores. Qualitative outcomes were usability problems identified during task performance and feedback from interviews. On average, tasks were completed in 11.75 seconds (SD 8.35) and 2.06 clicks (median 1.36). Of the 27 predefined tasks, 21 (77.78%) were fully completed, while completion rates for the remaining tasks ranged from 67% to 93%. The mean overall PSSUQ score was 2.38 out of 7, with lower values reflecting higher satisfaction. A total of 20 usability problems were identified, most of which related to guidance issues such as insufficient prompting, suboptimal grouping of items, and legibility concerns. The Guiding Pad was generally well received and demonstrated overall acceptability and supported task performance in a pediatric resuscitation scenario. However, several usability issues were identified that warrant further refinement. Addressing these limitations may enhance the app’s effectiveness, efficiency, and user satisfaction, thereby strengthening its suitability for real-world clinical use.

Background: Augmented reality (AR)-based cognitive aids provide real-time guidance during resuscitation, but their impact on clinicians’ visual attention is not well understood. This study aimed to describe visual attention distribution among clinicians using an AR-based decision support system during simulated pediatric cardiac arrest. Methods: This descriptive study was a secondary analysis of data from the intervention arm of a randomized clinical trial. Pediatric resuscitation teams from two centers participated in a standardized cardiac arrest simulation. The AR-based decision support system provided role-specific, real-time visual guidance, including task prompts, timers, and medication information displayed within the clinician’s field of view. Visual attention data from team leaders and medication nurses were collected using AR devices (HoloLens 2) with built-in eye tracking. Predefined areas of interest (AOIs) were established, and visual attention was quantified using fixation percentage, fixation count, and fixation duration. Results:Nine simulation sessions were analyzed. AR components accounted for a substantial proportion of visual attention (Mean percentage (SD) team leaders: 41.9% (14.9); medication nurses: 59.0% (14.6)). Team leaders demonstrated distributed attention across multiple AOIs, whereas medication nurses showed concentrated attention on medication-related tasks. Distinct fixation patterns were observed, with some AOIs characterized by frequent, brief fixations and others by less frequent, longer fixations. A substantial proportion of attention occurred outside predefined AOIs (team leaders: 46.0% (15.2); medication nurses: 22.9% (10.7)). Conclusion:Visual attention during use of the AR-based decision support system by both team leaders and medication nurses was characterized by substantial engagement and distinct role-specific attention patterns.

Resuscitation education is central to improving resuscitation performance and survival outcomes after cardiac arrest, yet no guidance exists for the standardization of reporting outcomes for cardiac arrest resuscitation education research. Standardization of reporting outcomes will allow the comparison of studies and collation of results, thus strengthening the conclusions that can be drawn from systematic reviews. Here, we aim to guide researchers by providing a list of quantitative outcome measures for resuscitation education research. The Utstein working group conducted 3 rounds of a Delphi process to identify outcome categories for educational research for health care professionals and laypeople. Sixteen outcome categories with 60 specific outcomes were identified for resuscitation education research involving health care providers, and 16 outcome categories with 51 specific outcomes were identified for research involving laypeople. Definitions are provided for all specific outcomes. The Resuscitation Education Outcomes Pyramid provides a summary of all potential outcome categories in a tiered fashion, with outcome categories further grouped into instructor outcomes, learner outcomes, patient outcomes, or systems- and population-level outcomes. Implementation of these standardized outcomes for resuscitation education research will support knowledge synthesis efforts through systematic reviews of the literature, leading to more impactful educational guidelines and improved clinical practice in the future.

Background: Cardiac arrest is a critical medical emergency that requires strict adherence to clinical guidelines to achieve optimal outcomes. Deviations from these guidelines, often due to task complexity, can adversely affect patient outcomes. Augmented reality (AR) offers a way to deliver role-specific, in-view guidance, but evidence on its perceived usability, user experience, and acceptability in cardiac arrest resuscitation remains limited. Objective: This study aimed to design, develop, and evaluate a role-specific AR decision support system for resuscitation team leaders and medication nurses. In this observational study, we assessed clinicians' perceived usability, user experience, and technology acceptance of the new AR system in a high-fidelity simulated cardiac arrest scenario. Methods: We conducted a prospective observational pilot study using a high-fidelity simulated pediatric cardiac arrest scenario. A total of 10 clinicians were recruited from Alberta Children's Hospital, including 5 (50%) of 10 pediatric emergency physicians serving as team leaders (men: 3/5, 60%, and women: 2/5, 40%; median age 41, IQR: 40-42 y) and 5 (50%) of 10 emergency nurses serving as medication nurses (men: 1/5, 20%, and women: 4/5, 80%; median age 45, IQR: 42-46 y). Participants used role-specific AR decision support interfaces deployed on HoloLens 2 head-mounted displays. Following the simulation, perceived usability, user experience, and technology acceptance were assessed using validated questionnaires: the System Usability Scale, User Experience Questionnaire, and Technology Acceptance Model. Data were collected via postsimulation surveys and analyzed descriptively. Results: Descriptive analyses were performed without inferential statistical testing. The mean System Usability Scale scores were 75.5 (SD 9.25, 95% CI 64.0-87.0) for team leaders and 82.0 (SD 11.20, 95% CI 68.0-96.0) for medication nurses. User experience was positive across roles, with mean User Experience Questionnaire scores indicating favorable attractiveness (team leaders: 1.87, SD 1.14, 95% CI 0.45-3.28; medication nurses: 2.43, SD 0.52, 95% CI 1.79-3.08), pragmatic quality (team leaders: 1.88, SD 0.87, 95% CI 0.80-2.97; medication nurses: 1.80, SD 0.69, 95% CI 0.94-2.66), and hedonic quality (team leaders: 2.40, SD 0.89, 95% CI 1.30-3.50; medication nurses: 2.28, SD 0.69, 95% CI 1.42-3.13). Technology acceptance was high, with mean combined Technology Acceptance Model scores of 5.92 (SD 0.46, 95% CI 5.35-6.49) for team leaders and 6.02 (SD 0.56, 95% CI 5.32-6.71) for medication nurses. Conclusions: This study introduces a novel role-specific AR decision support system that delivers tailored, in-view guidance to resuscitation team leaders and medication nurses during cardiac arrest. Unlike prior cognitive aids that present uniform or device-agnostic information, this system explicitly adapts interface content and structure to distinct clinical roles and workflows. The findings contribute early empirical evidence on the perceived usability, user experience, and acceptability of role-tailored AR support in high-acuity team settings and yield transferable design principles for developing role-aware AR interfaces. In real-world contexts, such systems may support protocol adherence and team coordination during resuscitation training and early-stage clinical deployment, informing future evaluations that incorporate objective performance and workflow outcomes.

Background: Pediatric cardiopulmonary resuscitation (CPR) is a highly complex and time-critical process that demands precise team coordination and strict adherence to pediatric advanced life support (PALS) guidelines. In real-world practice, adherence often deteriorates due to cognitive overload, fragmented communication, and disruption of information flow under stress. Although digital cognitive aids have shown potential to improve adherence, existing tools are often limited to single tasks, lack team-wide integration, or fail to adapt in real time to dynamic clinical environments. Objective: This study aimed to design and evaluate InterFACE (Interconnected and Focused Mobile Applications on Patient Care Environment), an integrated, augmented reality (AR)-enabled digital health system developed to support real-time PALS adherence and enhance team coordination during pediatric resuscitation. Methods: A structured, mixed methods, user-centered design process was used. Persona development and spatial analysis characterized the needs and positions of key resuscitation roles. A 3-round Delphi process with experts identified critical information elements for display. Iterative user experience (UX) prototyping was performed, followed by simulation-based evaluations of three system components: (1) TeamScreen, a wall-mounted team display providing a shared overview of the resuscitation process; (2) Guiding Pad (developed by Pierre Louis Rebours and Marc Ibrahim), a tablet-based app for documentation and algorithm navigation; and (3) AR head-mounted displays (HMDs) for team leaders and medication nurses, delivering role-specific, context-aware guidance. Usability was assessed with standardized instruments, including the System Usability Scale (SUS), Technology Acceptance Model (TAM), and User Experience Questionnaire (UEQ). Results: The Delphi study achieved consensus on 20 core information elements, distributed across the 3 interfaces. Usability testing demonstrated high acceptance across all modalities. The Guiding Pad supported effective navigation of resuscitation algorithms with a 78%-100% task completion rate. The TeamScreen achieved an overall task success rate of 81%, improving situational awareness despite some confusion in high-density regions. AR HMDs received favorable evaluations, with SUS scores rated "Good" to "Excellent," and UEQ ratings indicating high intuitiveness, stimulation, and attractiveness. Participants consistently described InterFACE as intuitive, useful for real-time decision-making, and supportive of team synchronization. Reported challenges included interface complexity, incomplete integration with patient monitors, and potential cognitive load from simultaneous information streams. Conclusions: InterFACE represents a significant advancement in digital cognitive aids by combining shared displays, tablets, and AR guidance into a synchronized, role-specific ecosystem. The system shows promise in enhancing adherence to PALS, reducing cognitive load, and improving team coordination in simulated pediatric resuscitations. While results demonstrate strong usability and acceptance, further research is needed to evaluate clinical effectiveness in real-world settings, including randomized controlled trials, integration with hospital information systems via Fast Healthcare Interoperability Resources (FHIR) standards, and potential artificial intelligence-driven decision support to optimize adaptability and long-term skill retention.

Aim: To evaluate the impact of higher physical realism of manikins on educational and clinical outcomes during life support education. Methods: This systematic review was conducted as part of the continuous evidence evaluation process of the International Liaison Committee on Resuscitation (ILCOR). A search of PubMed, Embase, and Cochrane was conducted from January 1, 2005 until April 30, 2024. Studies comparing training with higher physical realism manikins and lower realism manikins were eligible for inclusion. Studies comparing manikins to other forms of training (e.g. screen-based, virtual reality) were excluded. Risk of bias was assessed using Cochrane Risk of Bias 2 (RoB 2) for randomized trials and Risk Of Bias In Non-Randomized Studies of Interventions (ROBINS-I) for observational studies. For outcomes reported by four or more randomized studies, random effects meta-analysis using standardized mean difference was performed. Results: Of the 1276 articles identified and screened, 21 articles comprised the final review (19 randomized trials, 2 observational studies). Meta-analysis of eight RCTs reporting simulation skill performance in a simulated clinical scenario at course conclusion demonstrated a benefit from the use of higher- realism manikins compared with lower realism manikins (standardized mean difference 0.66, 95% CI 0.08 – 1.25). Meta-analysis of seven RCTs reporting knowledge at course conclusion showed no significant difference between the use of both types of manikins. Significant risk of bias and a high degree of heterogeneity were found among the included studies. Conclusion: This systematic review found that higher manikin realism during resuscitation training was associated with improved simulated clinical scenario performance at course conclusion; without an effect on knowledge at course conclusion. Future studies should examine the impact of resource requirements for high realism simulation on generalizability and implementation.

The increasing use of artificial intelligence (AI) by scholars presents a pressing challenge to healthcare publishing. While legitimate use can potentially accelerate scholarship, unethical approaches also exist, leading to factually inaccurate and biased text that may degrade scholarship. Numerous online AI detection tools exist that provide a percentage score of AI use. These can assist authors and editors in navigating this landscape. In this study, we compared the scores from three AI detection tools (ZeroGPT, PhraslyAI, and Grammarly AI Detector) across five plausible conditions of AI use and evaluated them against human assessments. Thirty open access articles published in the journals Advances in Simulation and Simulation in Healthcare prior to 2022 were selected, and the article introductions were extracted. Five experimental conditions were examined, including: (1) 100

Objectives:The use of cardiopulmonary resuscitation (CPR) feedback devices during training is increasing. This review evaluates whether incorporating CPR feedback devices in training improves patient survival, CPR quality in actual resuscitation, skill acquisition and retention after training. Methods:This systematic review was part of the continuous evidence evaluation process of the International Liaison Committee on Resuscitation (ILCOR). We searched MEDLINE, EMBASE, and SCOPUS databases from inception to September 30, 2024, including randomized controlled trials (RCTs) in all languages (with an English abstract) comparing CPR training with and without feedback devices. Outcome included patient survival, quality of clinical performance in resuscitation, and CPR skill acquisition and retention. Non-RCT studies, unpublished work without peer review or animal studies were excluded. Risk of bias was assessed using Cochrane tools, and certainty of evidence was graded using the Grading of Recommendations Assessment, development and Evaluation (GRADE) approach. Standardized mean difference (SMD) were calculated and pooled effects were analyzed using random-effects models. PROSPERO CRD42023488130. Results:We identified 20 RCTs with 4579 participants. Risks of bias ranged from low to critical (low: 8, moderate: 9, and critical: 3). No studies evaluated the patient survival, clinical performance in resuscitation or cost-effectiveness. Compared to no feedback, using CPR feedback devices during training significantly improved key quality metrics. Pooled effect sizes were 0.76 (95%CI 0.02 - 1.50) for mean compression depth (15 studies), 0.98 (95%CI: 0.10 - 1.87) for depth compliance (16 studies), 0.29 (95%CI: 0.10 - 0.48) for mean rate (17 studies), 0.44 (95%CI: 0.23 - 0.66) for rate compliance (9 studies), and 0.53 (95%CI: 0.31 - 0.75) for recoil compliance (10 studies) in favour of using feedback devices during training. Heterogeneity was large (I2 > 50%) in all analyses. Planned subgroup analyses revealed no statistically significant interaction between healthcare professionals and laypersons. Using the GRADE approach, the certainty of evidence was downgraded for certain outcomes due to critical risk of bias for 3 studies and inconsistency but upgraded for strong association. Conclusion:The use of CPR feedback devices during resuscitation training improves key quality metrics of CPR performance, with moderate to high certainty of evidence. However, further studies are needed to evaluate the impact on cost-effectiveness, clinical performance and patient outcomes.

IntroductionWith increased incorporation of simulation-based methodologies into quality improvement activities, standards for reporting on simulation-specific elements in healthcare improvement research are needed.MethodsWe followed established consensus process methodology to iteratively create simulation-based extensions for SQUIRE 2.0 reporting guidelines. Initial steps involved forming a steering committee, defining the scope, and conducting premeeting activities with an expert panel of simulation and quality improvement researchers. Recommendations from the expert panel were brought to a consensus meeting where existing guidelines were reviewed and recommendations made. Steering Committee members reviewed all recommendations, reconciled differences, and made final recommendations, which were piloted by experienced simulation and quality improvement researchers.ResultsFifteen Steering Committee members, 59 experts in simulation and quality improvement research, and 86 consensus meeting attendees reviewed SQUIRE 2.0 reporting guidelines and ultimately recommended simulation-based reporting guidelines for 22 of the 41 (54%) SQUIRE 2.0 guidelines. Those items for which simulation-based extensions were identified were: Notes to Authors, 1 (Title), 2a (Abstract), 2b (Abstract), 4 (Introduction: Available knowledge), 5 (Introduction: Rationale), 7 and 8a & b (Methods: Context and intervention), 9a (Methods - Study of the intervention), 9b (Methods - Study of the intervention), 10a (Methods - Measures), 10b (Methods-Measures), 10c (Methods-Measures), 11b (Methods- Analysis), 12 (Methods - Ethical considerations), 13a (Results), 13e (Results), 14b (Discussion - Summary), 15a-e (Discussion - Interpretation), 16a (Discussion - Limitations), 16b (Discussion - Limitations), 17c (Discussion - Conclusions), and 17d (Discussion - Conclusions).ConclusionsWe created simulation-based extensions to SQUIRE 2.0 reporting guidelines to improve the quality and standardization of reporting on simulation-specific elements of healthcare improvement research.

The International Liaison Committee on Resuscitation conducts continuous reviews of new, peer-reviewed, published cardiopulmonary resuscitation science and publishes more comprehensive reviews every 5 years. The Education, Implementation, and Teams chapter of the 2025 International Liaison Committee on Resuscitation Consensus on Science With Treatment Recommendations describes all published resuscitation evidence reviewed by the International Liaison Committee on Resuscitation's Education, Implementation, and Teams Task Force science experts since 2020. This summary addresses the evidence in 4 subchapters: (1) training populations, (2) faculty development, (3) knowledge translation and implementation, and (4) instructional design. Members from the Education, Implementation, and Teams Task Force have assessed, discussed, and debated the quality of the evidence, based on Grading of Recommendations, Assessment, Development, and Evaluation criteria, and their statements include consensus treatment recommendations. Insights into the deliberations of the task force are provided in the Justification and Evidence-to-Decision Framework Highlights sections. Priority knowledge gaps for further research are listed.

Introduction Large language model-based generative AI tools, such as the Chat Generative Pre-trained Transformer (ChatGPT) platform, have been used to assist with writing academic manuscripts. Little is known about ChatGPT's ability to accurately cite relevant references in health care simulation-related scholarly manuscripts. In this study, we sought to: (1) determine the reference accuracy and citation relevance among health care simulation debriefing articles generated by 2 different models of ChatGPT and (2) determine if ChatGPT models can be trained with specific prompts to improve reference accuracy and citation relevance. Methods The ChatGPT-4 and ChatGPT o1 models were asked to generate scholarly articles with appropriate references based upon three different article titles about health care simulation debriefing. Five articles with references were generated for each article title—3 ChatGPT-4 training conditions and 2 ChatGPT o1 training conditions. Each article was assessed independently by 2 blinded reviewers for reference accuracy and citation relevance. Results Fifteen articles were generated in total: 9 articles by ChatGPT-4 and 6 articles by ChatGPT o1. A total of 60.4% of the 303 references generated across 5 training conditions were classified as accurate, with no significant difference in reference accuracy between the 5 conditions. A total of 22.2% of the 451 citations were classified as highly relevant, with no significant difference in citation relevance across the 5 conditions. Conclusions Among debriefing articles generated by ChatGPT-4 and ChatGPT o1, both ChatGPT models are unreliable with respect to reference accuracy and citation relevance. Reference accuracy and citation relevance for debriefing articles do not improve even with some degree of training built into ChatGPT prompts.

Objectives To evaluate the effectiveness of in situ simulation for cardiopulmonary resuscitation (CPR) training on clinical and educational outcomes. Methods Randomised controlled trials (RCT) and non-randomised studies evaluating in situ simulation for cardiopulmonary resuscitation CPR training of healthcare workers in any setting compared to traditional training and reporting data on patients’ survival, patients’ outcomes, clinical performance and teamwork in actual or simulated resuscitation and resources needed were included. PubMed, Embase and Cochrane were searches from inception to October 28th 2024 (PROSPERO CRD42024521780). The assessment of risk of bias was done using RoB2 or ROBINS-I and the certainty of evidence was assessed by the GRADE approach. Meta-analysis was not possible due to significant heterogeneity in setting, interventions, control, and outcome definitions. The evidence was summarised according to the Synthesis Without Meta-Analysis (SwiM) reporting guidelines. No funding has been obtained. Results From 1062 records, 10 articles were included after full-text review (4 RCTs, 6 non-randomised). The risk of bias was judged as high or some concerns for RCTs and critical or serious for non-randomised studies. The certainty of evidence was very low for all the evaluated outcomes mainly due to risk of bias, inconsistency and imprecision. Two non-randomised studies reported data on patient survival, while two other non-randomized studies provided data on the review outcome of ’patient outcomes’, suggesting a potential benefit of in situ simulation or no difference. Four non-randomised studies reported improving or no difference in clinical performance in actual resuscitation. One study reported improved teamwork in actual resuscitation while another reported no difference. Most included studies reported improved clinical performance, teamwork and CPR skill in simulated resuscitation after in situ simulation training vs. traditional training. No study evaluated the resources needed. Conclusion The heterogenous evidence suggests that in situ simulation should be considered as an option for CPR training. The certainty of evidence is very low and cost-benefit balance is uncertain due to lack of data about resource needed.

Developed by the American Heart Association, these Guidelines represent the first comprehensive update of education recommendations since 2020. Incorporating the results of structured evidence reviews from the International Liaison Committee on Resuscitation, these are guidelines for the design and delivery of resuscitation training for health care professionals and lay rescuers. This update emphasizes the continuous evolution of evidence evaluation and the necessity of adapting educational strategies to local needs and diverse community demographics. Existing guidelines remain relevant unless specifically updated in this publication. Key topics that are new, are substantially revised, or have significant new literature include the use of cardiopulmonary resuscitation feedback devices in training, rapid-cycle deliberate practice, teamwork and leadership training, manikin fidelity, gamified learning, virtual and augmented reality, use of cognitive aids, stepwise training, blended learning, scripted debriefing, instructor training, alternative objects for lay rescuer chest compression training, and special considerations for training in the management of opioid overdose. How certain personal considerations may influence the overall impact of education are also reviewed, including disparities accordingly related to gender, race, socioeconomic status, and language; the impact of training for school children; and factors that act as barriers or facilitators to lay rescuer willingness to perform cardiopulmonary resuscitation. We conclude with a summary of current knowledge gaps in resuscitation education science and a discussion of future directions for optimizing the impact of resuscitation training programs.

Generative artificial intelligence (AI) tools have been selectively adopted across the academic community to help researchers complete tasks in a more efficient manner. The widespread release of the Chat Generative Pre-trained Transformer (ChatGPT) platform in 2022 has made these tools more accessible to scholars around the world. Despite their tremendous potential, studies have uncovered that large language model (LLM)-based generative AI tools have issues with plagiarism, AI hallucinations, and inaccurate or fabricated references. This raises legitimate concern about the utility, accuracy, and integrity of AI when used to write academic manuscripts. Currently, there is little clear guidance for healthcare simulation scholars outlining the ways that generative AI could be used to legitimately support the production of academic literature. In this paper, we discuss how widely available, LLM-powered generative AI tools (e.g. ChatGPT) can help in the academic writing process. We first explore how academic publishers are positioning the use of generative AI tools and then describe potential issues with using these tools in the academic writing process. Finally, we discuss three categories of specific ways generative AI tools can be used in an ethically sound manner and offer four key principles that can help guide researchers to produce high-quality research outputs with the highest of academic integrity.

Aim Cardiopulmonary resuscitation (CPR) quality is often substandard to guidelines for resuscitation teams. We aimed to investigate if the use of a CPR coach as part of the resuscitation team can improve teamwork, quality of care, and patient outcomes during simulated and clinical cardiac arrest resuscitation. Methods We searched PubMed, Embase, and Cochrane from inception until October 9, 2024 for randomized trials and observational studies. We assessed risk of bias using Cochrane tools and assessed the certainty of evidence using the Grading of Recommendations Assessment, Development and Evaluation approach. PROSPERO CRD42024603212. Results We screened 505 records and included 7 studies. Overall, 6 were randomized studies involving pediatric resuscitation of which 4 studies were secondary analyses of one simulation-based trial, and one was an observational study on adult out-of-hospital cardiac arrest. Reported outcomes were: CPR performance in a simulated setting (n=3), workload in a simulated setting (n=2), adherence to guidelines in a simulated setting (n=1), team communication in a simulated setting (n=1), and clinical CPR performance (n=1). All studies suggested improved CPR quality and guideline adherence when using a CPR coach compared to not using a coach. Risk of bias varied from low to critical and the certainty of evidence across outcomes was low or very low. Conclusions We identified low- to very-low certainty of evidence supporting the use of a CPR coach as part of the resuscitation team in order to improve CPR quality and guideline adherence. However, further research is needed, in particular for clinical performance and patient outcomes.