Human-centred AI for emergency cardiac care: Evaluating RAPIDx AI with PROLIFERATE_AI

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dc.contributor.authorPinero de Plaza, M.A.
dc.contributor.authorLambrakis, K.
dc.contributor.authorMarmolejo-Ramos, F.
dc.contributor.authorBeleigoli, A.
dc.contributor.authorArchibald, M.
dc.contributor.authorYadav, L.
dc.contributor.authorMcMillan, P.
dc.contributor.authorClark, R.
dc.contributor.authorLawless, M.
dc.contributor.authorMorton, E.
dc.contributor.authorHendriks, J.
dc.contributor.authorKitson, A.
dc.contributor.authorVisvanathan, R.
dc.contributor.authorChew, D.P.
dc.contributor.authorBarrera Causil, C.J.
dc.date.issued2025
dc.description.abstractBackground Chest pain diagnosis in emergency care is hindered by overlapping cardiac and non-cardiac symptoms, causing diagnostic uncertainty. Artificial Intelligence, such as RAPIDx AI, aims to enhance accuracy through clinical and biochemical data integration, but its adoption relies on addressing usability, explainability, and seamless workflow integration without disrupting care. Objective Evaluate RAPIDx AI’s integration into clinical workflows, address usability barriers, and optimise its adoption in emergencies. Methods The PROLIFERATE_AI framework was implemented across 12 EDs (July 2022–January 2024) with 39 participants: 15 experts co-designed a survey via Expert Knowledge Elicitation (EKE), applied to 24 ED clinicians to assess RAPIDx AI usability and adoption. Bayesian inference, using priors, estimated comprehension, emotional engagement, usage, and preference, while Monte Carlo simulations quantified uncertainty and variability, generating posterior means and 95% bootstrapped confidence intervals. Qualitative thematic analysis identified barriers and optimisation needs, with data triangulated through the PROLIFERATE_AI scoring system to rate RAPIDx AI’s performance by user roles and demographics. Results Registrars exhibited the highest comprehension (median: 0.466, 95 % CI: 0.41–0.51) and preference (median: 0.458, 95 % CI: 0.41–0.48), while residents/interns scored the lowest in comprehension (median: 0.198, 95 % CI: 0.17–0.26) and emotional engagement (median: 0.112, 95 % CI: 0.09–0.14). Registered nurses showed strong emotional engagement (median: 0.379, 95 % CI: 0.35–0.45). Novice users faced usability and workflow integration barriers, while experienced clinicians suggested automation and streamlined workflows. RAPIDx AI scored “Good Impact,” excelling with trained users but requiring targeted refinements for novices. Conclusion RAPIDx AI enhances diagnostic accuracy and efficiency for experienced users, but usability challenges for novices highlight the need for targeted training and interface refinements. The PROLIFERATE_AI framework offers a robust methodology for evaluating and scaling AI solutions, addressing the evolving needs of sociotechnical systems.
dc.description.statementofresponsibilityMaria Alejandra Pinero de Plaza, Kristina Lambrakis, Fernando Marmolejo-Ramos, Alline Beleigoli, Mandy Archibald, Lalit Yadav, Penelope McMillan, Robyn Clark, Michael Lawless, Erin Morton, Jeroen Hendriks, Alison Kitson, Renuka Visvanathan, Derek P. Chew, Carlos Javier Barrera Causil
dc.identifier.citationInternational Journal of Medical Informatics, 2025; 196:105810-1-105810-10
dc.identifier.doi10.1016/j.ijmedinf.2025.105810
dc.identifier.issn1386-5056
dc.identifier.issn1872-8243
dc.identifier.orcidMarmolejo-Ramos, F. [0000-0003-4680-1287]
dc.identifier.orcidBeleigoli, A. [0000-0002-7848-3183]
dc.identifier.orcidYadav, L. [0000-0002-7055-3247]
dc.identifier.orcidHendriks, J. [0000-0003-4326-9256]
dc.identifier.orcidVisvanathan, R. [0000-0002-1303-9479]
dc.identifier.urihttps://hdl.handle.net/2440/147031
dc.language.isoen
dc.publisherElsevier
dc.relation.granthttp://purl.org/au-research/grants/nhmrc/GNT1191914
dc.rights©2025 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/bync/4.0/).
dc.source.urihttps://doi.org/10.1016/j.ijmedinf.2025.105810
dc.subjectAdoption
dc.subjectArtificial intelligence
dc.subjectCardiac biomarkers
dc.subjectDecision support
dc.subjectEmergency medicine
dc.subjectHuman-centred evaluation
dc.subjectUsability
dc.subject.meshChest Pain
dc.subject.meshBayes Theorem
dc.subject.meshEmergency Service, Hospital
dc.subject.meshEmergency Medical Services
dc.subject.meshSurveys and Questionnaires
dc.titleHuman-centred AI for emergency cardiac care: Evaluating RAPIDx AI with PROLIFERATE_AI
dc.typeJournal article
pubs.publication-statusPublished

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