Overview of a User-Centered, Mixed-Methods Process for Designing Interconnected and Focused Mobile Applications on Patient Care Environment (InterFACE): Augmented-Reality Decision Support System for Pediatric Resuscitation
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,058 | 0,036 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,002 |
| Méta-épidémiologie (sens large) | 0,001 | 0,003 |
| Bibliométrie | 0,004 | 0,002 |
| Études des sciences et des technologies | 0,003 | 0,002 |
| Communication savante | 0,005 | 0,002 |
| Science ouverte | 0,003 | 0,004 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».