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We examined 3,046,538 acute respiratory infection (ARI) encounters with 6,103 national telehealth physicians from January 2019 to October 2021. The antibiotic prescribing rates were 44% for all ARIs; 46% were antibiotic appropriate; 65% were potentially appropriate; 19% resulted from inappropriate diagnoses; and 10% were related to coronavirus disease 2019 (COVID-19) diagnosis.

Objectives. To examine whether the addition of telehealth data to existing surveillance infrastructure can improve forecasts of cases and mortality. Methods. 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. Results. 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% ( P  = .06) and mortality forecasting by 6.4 percentage points to 26.9% ( P  < .048). Conclusions. Modest improvements in forecasting can be gained from adding telehealth data to syndromic surveillance infrastructure. ( Am J Public Health. 2024;114(2):218-225. https://doi.org/10.2105/AJPH.2023.307499).

Background: The CDC estimates that over 40% of Urgent Care visits are for acute respiratory infections (ARI), more than half involving inappropriate antibiotic prescriptions. Previous randomized trials in primary care clinics resulted in reductions in inappropriate antibiotic prescribing, but antibiotic stewardship interventions in telehealth have not been systematically assessed. To better understand how best to decrease inappropriate antibiotic prescribing for ARIs in telehealth, we are conducting a large randomized quality improvement trial testing both patient- and physician-facing feedback and behavioral nudges embedded in the electronic health record., Methods: Teladoc® clinicians are assigned to one of 9 arms in a 3 × 3 randomized trial. Each clinician is assigned to one of 3 Commitment groups (Public, Private, Control) and one of 3 Performance Feedback groups (Benchmark Peer Comparison, Trending, Control). After randomly selecting ⅓ of states and associated clinicians required for patient-facing components of the Public Commitment intervention, remaining clinicians are randomized to the Control and Private Commitment arms. Clinicians are randomized to the Performance Feedback conditions. The primary outcome is change from baseline in antibiotic prescribing rate for qualifying ARI visits. Secondary outcomes include changes in inappropriate prescribing and revisit rates. Secondary analyses include investigation of heterogeneity of treatment effects. With 1530 clinicians and an intra-clinician correlation in antibiotic prescribing rate of 0.5, we have >80% power to detect 1-7% absolute differences in antibiotic prescribing among groups., Discussion: Findings from this trial may help inform telehealth stewardship strategies, determine whether significant differences exist between Commitment and Feedback interventions, and provide guidance for clinicians and patients to encourage safe and effective antibiotic use., Clinicaltrials: gov: NCT05138874., (Copyright © 2022. Published by Elsevier Inc.)