Accepted for/Published in: JMIR Human Factors
Date Submitted: Jan 5, 2021
Date Accepted: Oct 11, 2021
Date Submitted to PubMed: Nov 29, 2021
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
AI-tool for cardiac arrhythmia prediction to support clinical decisions: Near-live feasibility study
ABSTRACT
Background:
Artificial intelligence (AI), such as machine learning, shows great promise for improving clinical decision-making in cardiac diseases by outperforming statistical-based models. However, few AI-based tools have been implemented in cardiology clinics because of the sociotechnical challenges during transitioning from algorithm development to real world implementation.
Objective:
This study explored how an AI-tool for prediction of ventricular tachycardia and ventricular fibrillation (VT/VF) could support clinical decision-making in remote monitoring of patients with an implantable cardioverter defibrillator (ICD).
Methods:
Seven experienced electrophysiologists participated in a near-live feasibility study, which included walkthroughs of 5 blinded, retrospective patient cases; questionnaires and interview questions; and use of the AI-tool. All sessions were video recorded and sessions evaluating the AI-tool were transcribed verbatim. Data was analyzed through an inductive qualitative approach based on grounded theory.
Results:
The AI-tool was found to have potential for supporting decision-making in ICD remote monitoring by providing reassurance, increasing confidence, acting as a second opinion, reducing information search time, and by enabling delegation of decisions to nurses and technicians. However, the AI-tool did not lead to changes in clinical action and was found less useful in cases where quality of data was poor or when VT/VF predictions were found irrelevant for evaluating the patient.
Conclusions:
When transitioning from AI development to testing its feasibility for clinical implementation: expectations must be aligned with the intended use of AI; trust in the AI-tool is likely to emerge from real-world use; and AI-accuracy is relational and dependent on available information and local workflows. Addressing the socio-technical gap between development and implementation of AI-tools for clinical decision-support in cardiac care is essential for succeeding with adoption. It is suggested that clinical end-users, their clinical contexts, and workflows are included throughout design, development, and implementation.
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