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Purpose of Review <p>Pediatric emergencies in general and community hospitals, where most children are seen, are infrequent and cognitively demanding, and deviation from resuscitation algorithms is common despite certification. This review treats that as a failure of execution under load rather than a knowledge deficit, and reads together two separately developed literatures: head-mounted augmented reality (AR) guidance, which externalizes the algorithm, and real-time physiological sensing, which estimates the clinician’s cognitive state.</p> Recent Findings <p>Controlled trials of head-mounted guidance report improved guideline adherence and fewer dosing errors in simulation, but effects are inconsistent across outcomes, samples are small, endpoints are process measures, and some interventions slow performance or raise workload. Cognitive-state classification is accurate within individuals but generalizes poorly across them, and the signals index arousal and effort rather than load. The adaptive combination has been proposed but never tested.</p> Summary <p>Adaptive guidance needs controlled comparison against static guidance with cognitive load and performance as co-primary outcomes, models that generalize across clinicians, and patient-level rather than simulation-only endpoints.</p>

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Adaptive Technologies for Pediatric Emergency Readiness: Cognitive State Sensing, Augmented Reality Guidance, and the Evidence Gap

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年8月4日

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  • Vishnunarayan Girishan Prabhu,
  • Roger Azevedo,
  • Yuliya Pecheny,
  • Ann Dietrich,
  • Ronald Pirrallo,
  • Jerzy Rozenblit,
  • Shiva Kalidindi

摘要

Purpose of Review

Pediatric emergencies in general and community hospitals, where most children are seen, are infrequent and cognitively demanding, and deviation from resuscitation algorithms is common despite certification. This review treats that as a failure of execution under load rather than a knowledge deficit, and reads together two separately developed literatures: head-mounted augmented reality (AR) guidance, which externalizes the algorithm, and real-time physiological sensing, which estimates the clinician’s cognitive state.

Recent Findings

Controlled trials of head-mounted guidance report improved guideline adherence and fewer dosing errors in simulation, but effects are inconsistent across outcomes, samples are small, endpoints are process measures, and some interventions slow performance or raise workload. Cognitive-state classification is accurate within individuals but generalizes poorly across them, and the signals index arousal and effort rather than load. The adaptive combination has been proposed but never tested.

Summary

Adaptive guidance needs controlled comparison against static guidance with cognitive load and performance as co-primary outcomes, models that generalize across clinicians, and patient-level rather than simulation-only endpoints.