Use of Telehealth Information for Early Detection: Insights From the COVID-19 Pandemic.

Abstract

<b>Objectives.</b> To examine whether the addition of telehealth data to existing surveillance infrastructure can improve forecasts of cases and mortality. <b>Methods.</b> In this observational study, we compared accuracy of 14-day forecasts using real-time data available to the National Syndromic Surveillance Program (standard forecasts) to forecasts that also included telehealth information (telehealth forecasts). The study was performed in a national telehealth service provider in 2020 serving 50 US states and the District of Columbia. <b>Results.</b> Among 10.5 million telemedicine encounters, 169 672 probable COVID-19 cases were diagnosed by 5050 clinicians, with a rate between 0.79 and 47.8 probable cases per 100 000 encounters per day (mean = 8.37; SD = 10.75). Publicly reported case counts ranged from 0.5 to 237 916 (mean: 53 913; SD = 47 466) and 0 to 2328 deaths (mean = 1035; SD = 550) per day. Telehealth-based forecasts improved 14-day case forecasting accuracy by 1.8 percentage points to 30.9% (<i>P</i> = .06) and mortality forecasting by 6.4 percentage points to 26.9% (<i>P</i> < .048). <b>Conclusions.</b> Modest improvements in forecasting can be gained from adding telehealth data to syndromic surveillance infrastructure. (<i>Am J Public Health.</i> 2024;114(2):218-225. https://doi.org/10.2105/AJPH.2023.307499).