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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: Since the advent of COVID-19, accelerated adoption of systems that reduce face-to-face encounters has outpaced training and best practices. Electronic consultations (eConsults), structured communications between PCPs and specialists regarding a case, have been effective in reducing face-to-face specialist encounters. As the health system rapidly adapts to multiple new practices and communication tools, new mechanisms to measure and improve performance in this context are needed., Objective: To test whether feedback comparing physicians to top performing peers using co-specialists' ratings improves performance., Design: Cluster-randomized controlled trial PARTICIPANTS: Eighty facility-specialty clusters and 214 clinicians INTERVENTION: Providers in the feedback arms were sent messages that announced their membership in an elite group of "Top Performers" or provided actionable recommendations with feedback for providers that were "Not Top Performers.", Main Measures: The primary outcomes were changes in peer ratings in the following performance dimensions after feedback was received: (1) elicitation of information from primary care practitioners; (2) adherence to institutional clinical guidelines; (3) agreement with peer's medical decision-making; (4) educational value; (5) relationship building., Key Results: Specialists showed significant improvements on 3 of the 5 consultation performance dimensions: medical decision-making (odds ratio 1.52, 95% confidence interval 1.08-2.14, p<.05), educational value (1.86, 1.17-2.96) and relationship building (1.63, 1.13-2.35) (both p<.01)., Conclusions: The pandemic has shed light on clinicians' commitment to professionalism and service as we rapidly adapt to changing paradigms. Interventions that appeal to professional norms can help improve the efficacy of new systems of practice. We show that specialists' performance can be measured and improved with feedback using aspirational norms., Trial Registration: clinicaltrials.gov NCT03784950., (© 2021. Society of General Internal Medicine.)

Deficiencies in data sharing capabilities limit Social Determinants of Health (SDoH) analysis as part of COVID-19 research. The National COVID Cohort Collaborative (N3C) is an example of an Electronic Health Record (EHR) database of patients tested for COVID-19 that could benefit from a SDoH elements framework that captures various screening instruments in EHR data warehouse systems. This paper uses the University of Washington Enterprise Data Warehouse (a data contributor to N3C) to demonstrate how SDoH can be represented and managed to be made available within an OMOP common data model. We found that these data varied by type of social determinants data and where it was collected, in the time period that it was collected, and in how it was represented., (©2021 AMIA - All rights reserved.)

Objective: To utilize, in an individual and institutional privacy-preserving manner, electronic health record (EHR) data from 202 hospitals by analyzing answers to COVID-19-related questions and posting these answers online., Materials and Methods: We developed a distributed, federated network of 12 health systems that harmonized their EHRs and submitted aggregate answers to consortia questions posted at https://www.covid19questions.org. Our consortium developed processes and implemented distributed algorithms to produce answers to a variety of questions. We were able to generate counts, descriptive statistics, and build a multivariate, iterative regression model without centralizing individual-level data., Results: Our public website contains answers to various clinical questions, a web form for users to ask questions in natural language, and a list of items that are currently pending responses. The results show, for example, that patients who were taking angiotensin-converting enzyme inhibitors and angiotensin II receptor blockers, within the year before admission, had lower unadjusted in-hospital mortality rates. We also showed that, when adjusted for, age, sex, and ethnicity were not significantly associated with mortality. We demonstrated that it is possible to answer questions about COVID-19 using EHR data from systems that have different policies and must follow various regulations, without moving data out of their health systems., Discussion and Conclusions: We present an alternative or a complement to centralized COVID-19 registries of EHR data. We can use multivariate distributed logistic regression on observations recorded in the process of care to generate results without transferring individual-level data outside the health systems., (© The Author(s) 2021. Published by Oxford University Press on behalf of the American Medical Informatics Association.)