Feasibility of a multimodal AI-based clinical assessment platform in emergency care: an exploratory pilot study
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Frontiers
Abstract
Background: Overcrowding in emergency departments (EDs) is a key challenge
in modern healthcare, affecting not only patient and staff comfort but also
mortality rates and quality of care. Artificial intelligence (AI) offers the
potential to optimize ED workflows by automating processes such as triage,
history-taking and documentation. To explore a potential approach to
overcrowding, we developed a multimodal and modular AI-based platform
that integrates these functions into a single system. This exploratory pilot
study investigated the feasibility of implementing the platform, focusing
particularly on usability and patient trust in the system.
Methods: Ambulatory patients triaged as non-urgent at the Marburg University
Hospital ED were recruited. After providing written consent, they underwent an
AI-supported initial assessment, including vital sign monitoring, automated
triage, suspected diagnosis and automatic report generation. Participants then
completed validated questionnaires on usability, Trust in Automation (TiA),
and a supplementary self-developed survey.
Results: A total of 20 patients were enrolled (70% female, 30% male; mean age
45.1 years), with an average interaction time of 10.6 min. The majority (80%)
reported feeling safe, satisfied, and willing to recommend the system, while
areas for improvement were identified regarding patient inclusion in decisionmaking
and the perceived quality of information received. Usability was rated
as excellent, with a mean System Usability Scale (SUS) score of 90.6.
Although familiarity with the system was low, trust-related measures assessed
using the TiA questionnaire were generally high.
Conclusion: This exploratory pilot study demonstrates the feasibility and user
acceptance of a multimodal AI platform in an ED setting. The system
achieved high patient satisfaction, excellent usability, and a generally high
level of trust. While these findings are limited to feasibility and perception,
they indicate that such systems could serve as a basis for multicenter studies
that directly evaluate impacts on triage accuracy, patient engagement, and
clinical efficiency.
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Except where otherwise noted, this item's license is described as Attribution 4.0 International