Telemedicine Use Predictors Among Cardiology Healthcare Professionals, 112 Respondents

57% of 112 surveyed German healthcare professionals treating cardiology patients reported using telemedicine. This dataset contains predictors of telemedicine use identified via Bayesian Model Averaging and an XGBoost model achieving 0.88 AUROC, created by Pascal Petit and last updated in April 2026. It likely includes variables related to professional role, knowledge, attitudes, and demographics.

Use Cases

• Predicting telemedicine adoption likelihood among healthcare professionals based on knowledge and attitude variables.
• Analyzing the relative importance of factors like specialty, gender, and patient volume on technology use.
• Training interpretable machine learning models for healthcare behavior using Shapley value explanations.

Strengths

• Model performance is quantified with a specific AUROC of 0.88 and accuracy of 0.79.
• Analysis is based on a defined sample of 112 healthcare professionals, with 64 users identified.
• Methodology is detailed, including nested cross-validation to prevent overfitting and Shapley explanations for interpretability.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The dataset is very small at 5.5 KB, indicating limited scope.

Provenance

Source

Secondary analysis of a German cross-sectional survey.

Collection Method

Survey data analyzed with Bayesian Model Averaging and XGBoost machine learning.

Time Range

null

Freshness

Last updated 2026-04-20 17:49:18; freshness should be verified.

Geography

Germany