Objective:

To identify factors associated with telemedicine (TM) adoption among healthcare professionals in Germany's outpatient sector using advanced statistical and machine learning methods.

Approach:

• Study Design: A secondary analysis of a web-based survey conducted among cardiologists, internists, and general practitioners in Germany, utilizing Bayesian model averaging (BMA) and extreme gradient boosting (XGBoost) for data analysis.

Key Findings:

• High acceptance of telemedicine among healthcare professionals, but significant challenges exist regarding technical infrastructure and reimbursement.
• Four distinct user clusters identified: 'pioneers,' 'focused practitioners,' 'using skeptics,' and 'uninformed distanced,' each with unique attitudes and barriers toward TM.

Interpretation:

The study provides insights into factors influencing TM adoption, exploring predictive associations through advanced statistical methods.

Limitations:

• Response rate cannot be calculated due to the distribution method of the survey.
• Sample size and model performance constraints may affect the interpretability and utility of the predictive models.

Conclusion:

The study identifies predictors of TM adoption in outpatient care.

Sources:

• Gehrmann et al. (2025)