{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T18:43:14Z","timestamp":1776710594038,"version":"3.51.2"},"reference-count":59,"publisher":"Public Library of Science (PLoS)","issue":"4","license":[{"start":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T00:00:00Z","timestamp":1776643200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosdigitalhealth.org"],"crossmark-restriction":false},"short-container-title":["PLOS Digit Health"],"abstract":"<jats:p>In this secondary analysis of a German cross-sectional survey data, we investigated key determinants and predictors of telemedicine (TM) use among healthcare professionals (HCPs) treating cardiology patients. We applied Bayesian Model Averaging (BMA) for explanatory analysis and Machine Learning (ML) for predictive modeling. BMA identified TM determinants after excluding collinear variables and selecting variables based on LASSO regression. The extreme gradient boosting (XGBoost) ML algorithm predicted TM use and identified key predictors, using nested cross-validation to prevent overfitting. ML model performance was assessed via area under the receiver operating characteristic curve (AUROC), while predictor importance was evaluated using Shapley additive explanations. Among 112 HCPs, 64 (57%) used TM. BMA identified 12 determinants, including positive associations with TM knowledge, being a cardiologist, female gender, and perceiving TM as suitable for heart failure and for monitoring events. Negative associations included concerns about insufficient patient benefits, perceptions that TM is less suitable for acute events, and skepticism regarding its relevance for extending aftercare intervals. The XGBoost model showed strong predictive performance (AUROC: 0.88 [95% CI: 0.75; 1.00], accuracy: 0.79) for TM use. Key promoting factors included TM knowledge, being a cardiologist, female gender, number of average patients per quarter, and perceiving TM as suitable for arrhythmias, device follow-up, and heart failure. Limiting factors included older age, personal use of TM for one\u2019s own health, and skepticism about TM\u2019s relevance in acute situations. These findings emphasize the importance of knowledge and attitudes in shaping TM adoption and show that ML can accurately identify healthcare professionals most likely to use TM, supporting targeted interventions and safer implementation in cardiology.<\/jats:p>","DOI":"10.1371\/journal.pdig.0001359","type":"journal-article","created":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T17:48:51Z","timestamp":1776707331000},"page":"e0001359","update-policy":"https:\/\/doi.org\/10.1371\/journal.pdig.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Telemedicine adoption in cardiology: Determinants and predictors identified using Bayesian Model Averaging and Machine 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