The Evolution of Telemedicine in Pre-Post COVID-19 Period (Epidemiology, Biostatistics, and Public Health, 2025)
IntroductionIn recent years, telemedicine has become increasingly widespread, with a marked rise in adoption during the COVID-19 pandemic. During the period of restrictions, telemedicine resulted to be particularly useful for managing various types of outpatient visits, both initial and follow-up. Several clinical studies have assessed the impact of telemedicine on patient compliance during the COVID-19 pandemic [1]. However, it remains unclear whether its use continued to grow in the post-pandemic period. This may depend on factors such as the efficiency of the telemedicine tools provided, the presence of a supportive organizational structure or patient’s attitudes toward telemedicine [2].ObjectivesThis study aimed to identify the typical patient using online monitoring, analysing demographics such as age, gender, and region, along with physician characteristics. It also examined which clinical units achieved the largest growth in telemedicine use. A secondary goal was to explore how patient preferences evolved over time. The analysis focused on telemedicine data from San Raffaele Hospital in Milan. The study period spanned from March 2020 to December 2024.Methods Longitudinal data on visit frequency for patients aged over 18 years were analyzed, focusing on individuals with at least two visits during the study period. Generalized linear mixed models (GLMMs) with a random intercept for patient were used to account for within-subject correlation [3]. The outcome variable (visit count) was modeled using a negative binomial distribution due to overdispersion [4]. Two separate models were estimated to evaluate the characteristics of the visit and the ones of the patient. For the first case, the fixed effects included clinical unit, visit type, physician characteristics, and semester. For the second case, fixed effects included patient characteristics, region, and semester. To further investigate the variability in the number of visits, aggregated visit counts were calculated by clinical unit, gender, geographical area, and year. A GLMM was fitted, incorporating a random intercept for clinical unit to account for repeated measures within each statistical unit. In this analysis, the number of visits was modeled using a Poisson distribution, given the absence of overdispersion.ResultsIn general, the clinical unit with the highest number of visits was psychology, likely due to the nature of the visits themselves. Consequently, it was selected as the reference category in subsequent analyses. Results from the GLMM focusing on visit characteristics revealed the highest number of visits per patient in diabetology unit compared to psychology unit (p=0.013). The semester variable was always positively significant, indicating a constant increase in visits over time compared to the reference category, i.e. the first semester of 2021 (all p