Viveiros, et. al (2025). "Electronic Patient Message Burdens: An Analysis of Factors Associated with Electronic Patient Message Quantity and Turnaround Time in Dermatology Journal of the American Academy of Dermatology", Mendeley Supplemental Tables

Supplemental Table 1. Model metrics for turnaround time and message quantity analyses. This table summarizes key metrics from the linear regression (LR) and negative binomial regression (NBR) models evaluating message turnaround times and message quantity, respectively. The linear regression model reports the Root Mean Squared Error (RMSE) and R2 as measures of fit. The negative binomial regression model includes pseudo-R2, Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and Deviance to assess model performance and fit.

Supplemental Table 2. Characteristics of faculty dermatologists included in this study, including gender, specialization, rank, years in practice, message quantity, message turnaround time, and weekly patient volume.