Telemedicine adoption in cardiology: Determinants and predictors identified using Bayesian Model Averaging and Machine Learning

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’s own health, and skepticism about TM’s 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.Author summary: In our study, we wanted to better understand why some healthcare professionals in Germany use telemedicine for cardiology patients, while others do not. We surveyed doctors about their experiences and opinions on using digital tools in heart care. To understand better what drives or hindered participants we used two types of analysis: one that finds important influencing factors, and one that predicts which professionals are most likely to use telemedicine. We found that healthcare professionals who had strong knowledge of telemedicine and who perceived it as relevant for patient care were much more likely to adopt it. Being a cardiologist and self-identifying as female were also consistently associated with higher use. In contrast, those who expressed doubts about its patient benefits, considered TM unsuitable for acute events, or reported insufficient knowledge were less likely to engage with it. Our predictive model achieved high accuracy in identifying those most likely to adopt telemedicine. While these results may appear intuitive, our use of advanced statistical and machine learning methods provides robust, data-driven evidence for patterns that often seem self-evident in practice. By quantifying these associations, our work offers a stronger foundation for healthcare policy, such as promoting education on telemedicine and addressing barriers for those who feel less confident using digital tools.