Roger Daglius Dias

Assessing task workload during military En Route Critical Care (ERCC) and other high-risk clinical operations remains challenging. This study evaluated heart rate variability (HRV) and body motion as digital biomarkers of task workload among Critical Care Air Transport (CCAT) teams during simulated ERCC missions. Seventy-two U.S. Air Force clinicians participated in 26 high-fidelity simulations. HRV and body motion were continuously captured via a chest-strap wearable sensor, and the NASA Task Load Index (NASA-TLX) was used to assess self-reported workload. Results demonstrated high feasibility (>90% HRV, 100% motion retention) with high wearable comfort ratings. Both HRV and Motion metrics showed strong construct validity, significantly differentiating resting baseline from active simulation (all p < 0.001, effect sizes d = 0.54–1.22 for HRV, r = 0.92–1.00 for motion). Convergent validity was selective: only baseline-adjusted root mean square of successive differences (RMSSD) demonstrated significant associations with NASA-TLX mental demand (β = −0.250, p = 0.005) and temporal demand (β = −0.233, p = 0.003). Baseline-adjusted RMSSD effectively indexes perceived workload, while Motion metrics provide complementary information about physical task demands. These findings support multimodal approaches integrating physiological reactivity, motion patterns, and self-reported ratings for comprehensive workload assessment in complex operational settings.

Purpose: Oxygen delivery (DO ₂ ) during cardiopulmonary bypass (CPB) is critical in preventing postoperative complications in adult cardiac surgery. This systematic review aimed to assess the relationship between intraoperative DO ₂ during CPB, particularly within Goal-directed Perfusion (GDP) strategies, and associated clinical outcomes. Methods: A systematic search of MEDLINE, Embase, Web of Science, PsycINFO, CINAHL, PROSPERO, and Cochrane was conducted from database inception through December 2024, adhering to PRISMA 2020 guidelines. Studies reported intraoperative DO ₂ measurements and their relationship with clinical outcomes among adults undergoing cardiac surgery with CPB. Data extraction and quality assessment were performed independently by two reviewers. Results: Thirty-nine studies (71,050 patients) were included, with acute kidney injury (AKI) being the most frequently studied outcome (84.6% of studies). A consistent association was found between lower intraoperative DO ₂ and increased risk of AKI, intraoperative lactate elevations, and prolonged mechanical ventilation. Five randomized controlled trials (RCTs) demonstrated that maintaining DO ₂ levels, indexed to body surface area (iDO ₂ ), above a threshold of 270–300 mL/min/m ² significantly reduced the risk of postoperative AKI. However, evidence linking DO ₂ management directly to reductions in mortality or neurologic complications remains limited, as well as studies reporting compliance with GDP strategies. Conclusion: Maintaining adequate iDO ₂ during CPB significantly reduces postoperative complications, especially AKI. These findings underscore the clinical relevance of GDP strategies, highlighting the importance of individualized perfusion management to optimize outcomes. Further large-scale RCTs are needed to confirm these benefits, standardize specific iDO ₂ threshold levels that are beneficial, and to explore strategies that impact mortality and neurologic outcomes, as well as investigate the role that temperature management plays in DO ₂ threshold determination.

Coaches are vital for effective collaboration, but cost and resource constraints often limit their availability during real-world tasks. This limitation poses serious challenges in life-critical domains that rely on effective teamwork, such as healthcare and disaster response. To address this gap, we propose and realize an innovative application of AI: task-time team coaching. Specifically, we introduce Socratic, a novel AI system that complements human coaches by providing real-time guidance during task execution. Socratic monitors team behavior, detects misalignments in team members' shared understanding, and delivers automated interventions to improve team performance. We validated Socratic through two human subject experiments involving dyadic collaboration. The results demonstrate that the system significantly enhances team performance with minimal interventions. Participants also perceived Socratic as helpful and trustworthy, supporting its potential for adoption. Our findings also suggest promising directions both for AI research and its practical applications to enhance human teamwork.

Background: Delivering high-quality prehospital emergency care remains challenging, especially in resource-limited settings where real-time clinical decision support is limited. Augmented reality (AR) has emerged as a promising health care technology, offering potential solutions to enhance decision-making, care processes, and emergency medical service (EMS) training. Objective: This systematic review assesses the effectiveness of AR in improving clinical decision-making, care delivery, and educational outcomes for EMS providers. Methods: We searched databases including PubMed, Cochrane CENTRAL, Web of Science, Institute of Electrical and Electronics Engineers (IEEE), Embase, PsycInfo, and Association for Computing Machinery (ACM). Studies were selected based on their focus on AR in prehospital care. A total of 14 randomized controlled trials were selected from an initial screening of 2081 manuscripts. Included studies focused on AR use by EMS personnel, examining clinical and educational impacts. Data such as study demographics, intervention type, outcomes, and methodologies were extracted using a standardized form. Primary outcomes assessed included clinical task accuracy, response times, and training efficacy. A narrative synthesis was conducted, and bias was evaluated using Cochrane’s risk of bias tool. Improvements in AR-assisted interventions and their limitations were analyzed. Results: AR significantly improved clinical decision-making accuracy and EMS training outcomes, reducing response times in simulations and real-world applications. However, small sample sizes and challenges in integrating AR into workflows limit the generalizability of the findings. Conclusions: AR holds promise for transforming prehospital care by enhancing real-time decision-making and EMS training. Future research should address technological integration and scalability to fully realize AR’s potential in EMS.

The proceedings of the Hamlyn Symposium on Medical Robotics only contain accepted papers submitted to...

The proceedings of the Hamlyn Symposium on Medical Robotics only contain accepted papers submitted to...

Background:. Currently, there is no standardization in ED-based-Ultrasound-guided-nerve-blocks (UGNB) training, credentialing, or procedural quality assurance nation-wide. This study aims to investigate objective measures of procedural competency for UGNB. Methods: Novice and expert users performed UGNB in a simulation-based setting. Participants were fitted with a head-motion tracking headband and their performance graded with a traditional OSCE. Head motion metrics were captured using both accelerometer and gyroscope sensors. Results: 11 novices and 7 experts were recruited. OSCE scores demonstrated a statistically significant difference between groups. Significant correlations were observed for accelerometer data in the z-axis and gyroscope data in the y-and z-axes, with positive correlation between accelerometer z-axis RMS values and OSCE scores(r=0.36), and gyroscope z-axis RMS values and OSCE scores(r=0.29). Conclusion: Objective measures of head motion in the z-axis demonstrated significant differences between novice and expert POCUS. Our data suggests that computer-based metrics may be reliable measures of procedural competency.

Marginalized populations experience high prevalence of chronic health conditions - many of which require optimal medication adherence to avoid significant consequences for mortality and morbidity. Yet, marginalization and its complex sequelae create barriers for adherence and access to care, creating a cycle of health inequity. Individuals are not fully benefiting from evidence-based behavioral adherence interventions, like “Life-steps”, potentially due to lack of embedded experiential learning, which is key for individuals experiencing complex barriers to medication adherence. Leveraging interactive artificial intelligence technologies through immersive virtual reality is a promising avenue to bolster behavioral adherence interventions. We present our current proof-of-concept work for “Life-steps VR”, an integration of such technologies into the empiric behavioral medication adherence intervention. We then discuss our next steps for further refinement and testing of the technology with consideration for equity, democratization, and accessibility of health technologies. We conclude with a discussion of future potential iterations of Life-steps VR.