A machine learning-based decision-support tool for the prediction of cardiac arrhythmia was evaluated for clinician preimplementation perspectives, but no quantitative results were provided in the excerpt.
Does a machine learning-based decision-support tool for predicting VT/VF support clinical decision-making in the remote monitoring of patients with an ICD?
An ML-based decision-support tool for predicting VT/VF in ICD patients showed potential to support clinical decision-making by increasing confidence, though it did not change clinical actions in this feasibility study.
BACKGROUND: Artificial intelligence (AI), such as machine learning (ML), 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 ML-based tool for predicting ventricular tachycardia and ventricular fibrillation (VT/VF) could support clinical decision-making in the remote monitoring of patients with an implantable cardioverter defibrillator (ICD). METHODS: Seven experienced electrophysiologists participated in a near-live feasibility and qualitative study, which included walkthroughs of 5 blinded retrospective patient cases, use of the prediction tool, and questionnaires and interview questions. All sessions were video recorded, and sessions evaluating the prediction tool were transcribed verbatim. Data were analyzed through an inductive qualitative approach based on grounded theory. RESULTS: The prediction 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 enabling delegation of decisions to nurses and technicians. However, the prediction tool did not lead to changes in clinical action and was found less useful in cases where the quality of data was poor or when VT/VF predictions were found to be irrelevant for evaluating the patient. CONCLUSIONS: When transitioning from AI development to testing its feasibility for clinical implementation, we need to consider the following: expectations must be aligned with the intended use of AI; trust in the prediction tool is likely to emerge from real-world use; and AI accuracy is relational and dependent on available information and local workflows. Addressing the sociotechnical gap between the development and implementation of clinical decision-support tools based on ML in cardiac care is essential for succeeding with adoption. It is suggested to include clinical end-users, clinical contexts, and workflows throughout the overall iterative approach to design, development, and implementation.
Matthiesen et al. (2021) studied Cardiac Arrhythmia. Machine learning-based decision-support tool was evaluated. A machine learning-based decision-support tool for the prediction of cardiac arrhythmia was evaluated for clinician preimplementation perspectives, but no quantitative results were provided in the excerpt.