This paper is in the following e-collection/theme issue:

Focus Groups and Qualitative Research for Human Factors Research (880) Artificial Intelligence (1421) Decision Support for Health Professionals (1210) Clinical Information and Decision Making (1434) Machine Learning (1501) Methods and Feasibility Studies (384) Cardiac Arrhythmia, Atrial Fibrillation (87) Implantable Cardioverter Defibrillator (ICD) Care and Innovations (13)

Published on in Vol 8, No 4 (2021): Oct-Dec

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/26964, first published .
Clinician Preimplementation Perspectives of a Decision-Support Tool for the Prediction of Cardiac Arrhythmia Based on Machine Learning: Near-Live Feasibility and Qualitative Study

Clinician Preimplementation Perspectives of a Decision-Support Tool for the Prediction of Cardiac Arrhythmia Based on Machine Learning: Near-Live Feasibility and Qualitative Study

Clinician Preimplementation Perspectives of a Decision-Support Tool for the Prediction of Cardiac Arrhythmia Based on Machine Learning: Near-Live Feasibility and Qualitative Study

Authors of this article:

Stina Matthiesen1, 2 Author Orcid Image ;   Søren Zöga Diederichsen2, 3 Author Orcid Image ;   Mikkel Klitzing Hartmann Hansen2 Author Orcid Image ;   Christina Villumsen2 Author Orcid Image ;   Mats Christian Højbjerg Lassen2 Author Orcid Image ;   Peter Karl Jacobsen3 Author Orcid Image ;   Niels Risum3 Author Orcid Image ;   Bo Gregers Winkel3 Author Orcid Image ;   Berit T Philbert3 Author Orcid Image ;   Jesper Hastrup Svendsen3, 4 Author Orcid Image ;   Tariq Osman Andersen1, 2 Author Orcid Image
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