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Related Experiment Video

Updated: Jun 24, 2026

The Immersive Cleveland Clinic Virtual Reality Shopping Platform for the Assessment of Instrumental Activities of Daily Living

The Immersive Cleveland Clinic Virtual Reality Shopping Platform for the Assessment of Instrumental Activities of Daily Living

Published on: July 28, 2022

Automated video-based AVPU assessment within a FHIR-enabled clinical decision support framework.

João C Ferreira^1,2,3, Pedro Faria⁴

¹ISCTE - Instituto Universitário de Lisboa, Lisbon, Portugal. joao.c.amaro.ferreira@himolde.no.

Scientific Reports

|June 22, 2026

Summary

Consc.ia, a video-based platform, automates consciousness assessment using AVPU scale (Alert, Verbal, Pain, Unresponsive) documentation, improving emergency care. This proof-of-concept offers a feasible, interoperable solution for standardizing patient monitoring.

Keywords:

AVPU scale Clinical decision support system Computer vision Consciousness assessment Edge computing Emergency care HL7 FHIR Technology adoption modelling

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Last Updated: Jun 24, 2026

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Published on: July 12, 2024

Area of Science:

Medical Informatics
Computer Vision in Healthcare
Clinical Decision Support Systems

Background:

Inconsistent documentation of the AVPU scale (Alert, Verbal, Pain, Unresponsive) due to high workload and fragmented workflows impacts patient safety.
Accurate consciousness assessment is crucial for emergency triage, care escalation, and overall patient safety.

Purpose of the Study:

To develop and evaluate Consc.ia, a video-based clinical decision-support platform for automated AVPU scale inference.
To enable seamless, interoperable documentation through HL7 FHIR integration.
To assess technology adoption and deployment scenarios for emergency and hospital settings.

Main Methods:

Developed a video-based platform (Consc.ia) using edge-computing computer vision for real-time AVPU assessment.
Integrated a clinician-in-the-loop validation layer and HL7 FHIR for EHR interoperability.
Simulated AVPU dataset with 136 videos from 58 healthcare professionals; modelled technology adoption using Rogers' and Bass Diffusion models.

Main Results:

The Consc.ia architecture achieves low-latency inference with privacy-by-design.
Stakeholder validation confirmed workflow fit but highlighted documentation gaps during EMS-to-hospital transitions.
Bass modelling projects gradual adoption, reaching ~50% of Intermediate Care wards by 2037, with early clinical evidence and FHIR integration as key adoption accelerators.

Conclusions:

Consc.ia presents a feasible, interoperable proof-of-concept for standardizing AVPU documentation and enhancing early warning systems.
The platform addresses digitalization gaps in emergency care by combining video analytics, edge computing, clinician validation, and FHIR integration.
Further validation through empirical model evaluation against expert-annotated clinical recordings is required for clinical translation.