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.

补充表1：消息周转时间与消息数量分析的模型指标。本表格汇总了分别用于评估消息周转时间和消息数量的线性回归（linear regression）模型与负二项回归（negative binomial regression, NBR）的关键指标。其中，线性回归模型以均方根误差（Root Mean Squared Error, RMSE）和决定系数（R²）作为模型拟合效果的评估指标；负二项回归模型则纳入伪决定系数（pseudo-R²）、赤池信息准则（Akaike Information Criterion, AIC）、贝叶斯信息准则（Bayesian Information Criterion, BIC）以及偏差（Deviance），用以评判模型性能与拟合优度。

补充表2：本研究纳入的皮肤科教职医师特征，涵盖性别、专业方向、职称、从业年限、消息数量、消息周转时间以及周接诊量。