Pandemic Telemedicine Adoption Trends in a Predominantly Rural Integrated Health System.

Between March 2020 and February 2022, use of telemedicine services in the U.S. shifted dramatically in response to the evolving SARS-CoV2 pandemic. The initial wave caused many non-emergent clinical services to be postponed, including specialty care clinic visits, which were rapidly converted to telemedicine encounters. Telemedicine use ebbed and flowed with subsequent pandemic waves. This paper describes trends in telemedicine use from March 2020-February 2022 at Geisinger, a predominantly rural integrated health system. It highlights characteristics of 5,390 virtual vs. 15,740 in-person clinic visits to neurosurgery and gastroenterology specialists in December 2021 and January 2022. Differences in ordering of diagnostic testing and prescription medications, as well as post-clinic-visit utilization, varied by specialty. Virtual visits in these specialties saved patients from traveling over 174,700 miles/month to attend appointments. Analyzing telemedicine use patterns can inform future resource allocation and determine when virtual encounters can complement or replace in-person specialty care visits.

Associations between Google Search Trends for Symptoms and COVID-19 Confirmed and Death Cases in the United States.

We utilize functional data analysis techniques to investigate patterns of COVID-19 positivity and mortality in the US and their associations with Google search trends for COVID-19-related symptoms. Specifically, we represent state-level time series data for COVID-19 and Google search trends for symptoms as smoothed functional curves. Given these functional data, we explore the modes of variation in the data using functional principal component analysis (FPCA). We also apply functional clustering analysis to identify patterns of COVID-19 confirmed case and death trajectories across the US. Moreover, we quantify the associations between Google COVID-19 search trends for symptoms and COVID-19 confirmed case and death trajectories using dynamic correlation. Finally, we examine the dynamics of correlations for the top nine Google search trends of symptoms commonly associated with COVID-19 confirmed case and death trajectories. Our results reveal and characterize distinct patterns for COVID-19 spread and mortality across the US. The dynamics of these correlations suggest the feasibility of using Google queries to forecast COVID-19 cases and mortality for up to three weeks in advance. Our results and analysis framework set the stage for the development of predictive models for forecasting COVID-19 confirmed cases and deaths using historical data and Google search trends for nine symptoms associated with both outcomes.