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Importance: The effect of higher-dose fluvoxamine in reducing symptom duration among outpatients with mild to moderate COVID-19 remains uncertain., Objective: To assess the effectiveness of fluvoxamine, 100 mg twice daily, compared with placebo, for treating mild to moderate COVID-19., Design, Setting, and Participants: The ACTIV-6 platform randomized clinical trial aims to evaluate repurposed medications for mild to moderate COVID-19. Between August 25, 2022, and January 20, 2023, a total of 1175 participants were enrolled at 103 US sites for evaluating fluvoxamine; participants were 30 years or older with confirmed SARS-CoV-2 infection and at least 2 acute COVID-19 symptoms for 7 days or less., Interventions: Participants were randomized to receive fluvoxamine, 50 mg twice daily on day 1 followed by 100 mg twice daily for 12 additional days (n = 601), or placebo (n = 607)., Main Outcomes and Measures: The primary outcome was time to sustained recovery (defined as at least 3 consecutive days without symptoms). Secondary outcomes included time to death; time to hospitalization or death; a composite of hospitalization, urgent care visit, emergency department visit, or death; COVID-19 clinical progression scale score; and difference in mean time unwell. Follow-up occurred through day 28., Results: Among 1208 participants who were randomized and received the study drug, the median (IQR) age was 50 (40-60) years, 65.8% were women, 45.5% identified as Hispanic/Latino, and 76.8% reported receiving at least 2 doses of a SARS-CoV-2 vaccine. Among 589 participants who received fluvoxamine and 586 who received placebo included in the primary analysis, differences in time to sustained recovery were not observed (adjusted hazard ratio [HR], 0.99 [95% credible interval, 0.89-1.09]; P for efficacy = .40]). Additionally, unadjusted median time to sustained recovery was 10 (95% CI, 10-11) days in both the intervention and placebo groups. No deaths were reported. Thirty-five participants reported health care use events (a priori defined as death, hospitalization, or emergency department/urgent care visit): 14 in the fluvoxamine group compared with 21 in the placebo group (HR, 0.69 [95% credible interval, 0.27-1.21]; P for efficacy = .86) There were 7 serious adverse events in 6 participants (2 with fluvoxamine and 4 with placebo) but no deaths., Conclusions and Relevance: Among outpatients with mild to moderate COVID-19, treatment with fluvoxamine does not reduce duration of COVID-19 symptoms., Trial Registration: ClinicalTrials.gov Identifier: NCT04885530.

The COVID-19 pandemic exposed multiple weaknesses in the nation's public health system. Therefore, the American College of Medical Informatics selected "Rebuilding the Nation's Public Health Informatics Infrastructure" as the theme for its annual symposium. Experts in biomedical informatics and public health discussed strategies to strengthen the US public health information infrastructure through policy, education, research, and development. This article summarizes policy recommendations for the biomedical informatics community postpandemic. First, the nation must perceive the health data infrastructure to be a matter of national security. The nation must further invest significantly more in its health data infrastructure. Investments should include the education and training of the public health workforce as informaticians in this domain are currently limited. Finally, investments should strengthen and expand health data utilities that increasingly play a critical role in exchanging information across public health and healthcare organizations., (© The Author(s) 2023. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Importance: The effectiveness of ivermectin to shorten symptom duration or prevent hospitalization among outpatients in the US with mild to moderate symptomatic COVID-19 is unknown., Objective: To evaluate the efficacy of ivermectin, 400 μg/kg, daily for 3 days compared with placebo for the treatment of early mild to moderate COVID-19., Design, Setting, and Participants: ACTIV-6, an ongoing, decentralized, double-blind, randomized, placebo-controlled platform trial, was designed to evaluate repurposed therapies in outpatients with mild to moderate COVID-19. A total of 1591 participants aged 30 years and older with confirmed COVID-19, experiencing 2 or more symptoms of acute infection for 7 days or less, were enrolled from June 23, 2021, through February 4, 2022, with follow-up data through May 31, 2022, at 93 sites in the US., Interventions: Participants were randomized to receive ivermectin, 400 μg/kg (n = 817), daily for 3 days or placebo (n = 774)., Main Outcomes and Measures: Time to sustained recovery, defined as at least 3 consecutive days without symptoms. There were 7 secondary outcomes, including a composite of hospitalization or death by day 28., Results: Among 1800 participants who were randomized (mean [SD] age, 48 [12] years; 932 women [58.6%]; 753 [47.3%] reported receiving at least 2 doses of a SARS-CoV-2 vaccine), 1591 completed the trial. The hazard ratio (HR) for improvement in time to recovery was 1.07 (95% credible interval [CrI], 0.96-1.17; posterior P value [HR >1] = .91). The median time to recovery was 12 days (IQR, 11-13) in the ivermectin group and 13 days (IQR, 12-14) in the placebo group. There were 10 hospitalizations or deaths in the ivermectin group and 9 in the placebo group (1.2% vs 1.2%; HR, 1.1 [95% CrI, 0.4-2.6]). The most common serious adverse events were COVID-19 pneumonia (ivermectin [n = 5]; placebo [n = 7]) and venous thromboembolism (ivermectin [n = 1]; placebo [n = 5])., Conclusions and Relevance: Among outpatients with mild to moderate COVID-19, treatment with ivermectin, compared with placebo, did not significantly improve time to recovery. These findings do not support the use of ivermectin in patients with mild to moderate COVID-19., Trial Registration: ClinicalTrials.gov Identifier: NCT04885530.

Public health faces unprecedented challenges in its efforts to control COVID-19 through a national vaccination campaign. Addressing these challenges will require fundamental changes to public health data systems. For example, of the core data systems for immunization campaigns is the immunization information system (IIS); however, IISs were designed for tracking the vaccinated, not finding the patients who are high risk and need to be vaccinated. Health systems have this data in their electronic health records (EHR) systems and often have a greater capacity for outreach. Clearly, a partnership is needed. However, successful collaborations will require public health to change from its historical hierarchical information supply chain model to an ecosystem model with a peer-to-peer exchange with population health providers. Examples of the types of informatics innovations necessary to support such an ecosystem include a national patient identifier, population-level data exchange for immunization data, and computable electronic quality measures. Rather than think of these components individually, a comprehensive approach to rapidly adaptable tools for collaboration is needed., (© The Author(s) 2021. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Objective: The rapidly evolving COVID-19 pandemic has created a need for timely data from the healthcare systems for research. To meet this need, several large new data consortia have been developed that require frequent updating and sharing of electronic health record (EHR) data in different common data models (CDMs) to create multi-institutional databases for research. Traditionally, each CDM has had a custom pipeline for extract, transform, and load operations for production and incremental updates of data feeds to the networks from raw EHR data. However, the demands of COVID-19 research for timely data are far higher, and the requirements for updating faster than previous collaborative research using national data networks have increased. New approaches need to be developed to address these demands., Methods: In this article, we describe the use of the Fast Healthcare Interoperability Resource (FHIR) data model as a canonical data model and the automated transformation of clinical data to the Patient-Centered Outcomes Research Network (PCORnet) and Observational Medical Outcomes Partnership (OMOP) CDMs for data sharing and research collaboration on COVID-19., Results: FHIR data resources could be transformed to operational PCORnet and OMOP CDMs with minimal production delays through a combination of real-time and postprocessing steps, leveraging the FHIR data subscription feature., Conclusions: The approach leverages evolving standards for the availability of EHR data developed to facilitate data exchange under the 21st Century Cures Act and could greatly enhance the availability of standardized datasets for research., (© The Author(s) 2021. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Objectives: We describe our approach in using health information technology to provide a continuum of services during the coronavirus disease 2019 (COVID-19) pandemic. COVID-19 challenges and needs required health systems to rapidly redesign the delivery of care., Materials and Methods: Our health system deployed 4 COVID-19 telehealth programs and 4 biomedical informatics innovations to screen and care for COVID-19 patients. Using programmatic and electronic health record data, we describe the implementation and initial utilization., Results: Through collaboration across multidisciplinary teams and strategic planning, 4 telehealth program initiatives have been deployed in response to COVID-19: virtual urgent care screening, remote patient monitoring for COVID-19-positive patients, continuous virtual monitoring to reduce workforce risk and utilization of personal protective equipment, and the transition of outpatient care to telehealth. Biomedical informatics was integral to our institutional response in supporting clinical care through new and reconfigured technologies. Through linking the telehealth systems and the electronic health record, we have the ability to monitor and track patients through a continuum of COVID-19 services., Discussion: COVID-19 has facilitated the rapid expansion and utilization of telehealth and health informatics services. We anticipate that patients and providers will view enhanced telehealth services as an essential aspect of the healthcare system. Continuation of telehealth payment models at the federal and private levels will be a key factor in whether this new uptake is sustained., Conclusions: There are substantial benefits in utilizing telehealth during the COVID-19, including the ability to rapidly scale the number of patients being screened and providing continuity of care., (© The Author(s) 2020. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Background: As SARS-CoV-2, the virus that causes COVID-19, spread rapidly across the United States in the spring of 2020, institutions of higher education faced numerous challenges associated with minimizing risk of exposure to COVID-19 among their students, faculty, staff, and surrounding communities. This paper describes the protocol, South Carolina (SC) Safer Together, developed by Clemson University (Clemson) to design, deploy, and evaluate multi-level communication and dissemination and implementation (D&I) strategies in line with recommendations from governmental and educational agencies to mitigate the risk of exposure to COVID-19. Safer Together was enhanced by the addition of the Google/Apple Exposure Notification app, an alternative strategy to support a recommendation of COVID-19 testing outcomes: contact tracing, isolation, and quarantine. Objective: This study aimed to (1) describe the content and intended audiences of D&I strategies used to deploy recommended COVID-19 mitigation strategies on a major college campus; (2) determine the reach, acceptability, adoption, and use of D&I strategies among target audiences among university students, faculty, and staff; and (3) characterize barriers and facilitators to the implementation and use of recommended mitigation strategies. Methods: The study team incorporated elements of the Health Belief Model, the Technology Acceptance Model, communication and social marketing models, and the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework to identify and develop appropriate constructs and specific outcomes for inclusion in our approach to evaluate the communication, dissemination and implementation processes related to deployment of Safer Together at Clemson. A parallel convergent mixed methods design was used to (1) inform implementation strategies used to launch the program and (2) evaluate program reach, acceptability, adoption, and use guided by the RE-AIM framework. Data collection tools include surveys, data analytics--tracking, and focus groups or interviews with key stakeholders (students, employees, and university leadership). Results: Rigorously studying both the dissemination and implementation of Safer Together in a national public university setting is expected to yield lessons that will be valuable at many organizational and governmental settings. On a local level, broad adoption and use of the Safer Together may help reduce COVID-19 transmission and keep the university "open." On a larger scale, lessons learned on how to influence student and employee behavior with respect to the use of a public health outbreak prevention tool including Safer Together may be applicable in future pandemic and outbreak situations. Conclusions: This study proposes a structured, theory-driven approach to evaluate dissemination and implementation strategies associated with the deployment of Safer Together in a university setting from the viewpoint of students, employees, and university leadership. Our results will inform future implementation of apps such as Safer Together at major state universities in SC. [ABSTRACT FROM AUTHOR]

Objective: In an effort to improve the efficiency of computer algorithms applied to screening for coronavirus disease 2019 (COVID-19) testing, we used natural language processing and artificial intelligence-based methods with unstructured patient data collected through telehealth visits.Materials and Methods: After segmenting and parsing documents, we conducted analysis of overrepresented words in patient symptoms. We then developed a word embedding-based convolutional neural network for predicting COVID-19 test results based on patients' self-reported symptoms.Results: Text analytics revealed that concepts such as smell and taste were more prevalent than expected in patients testing positive. As a result, screening algorithms were adapted to include these symptoms. The deep learning model yielded an area under the receiver-operating characteristic curve of 0.729 for predicting positive results and was subsequently applied to prioritize testing appointment scheduling.Conclusions: Informatics tools such as natural language processing and artificial intelligence methods can have significant clinical impacts when applied to data streams early in the development of clinical systems for outbreak response. [ABSTRACT FROM AUTHOR]

The novel coronavirus disease 2019 infection poses serious challenges to the healthcare system that are being addressed through the creation of new unique and advanced systems of care with disjointed care processes (eg, telehealth screening, drive-through specimen collection, remote testing, telehealth management). However, our current regulations on the flows of information for clinical care and research are antiquated and often conflict at the state and federal levels. We discuss proposed changes to privacy regulations such as the Health Insurance Portability and Accountability Act designed to let health information seamlessly and frictionlessly flow among the health entities that need to collaborate on treatment of patients and, also, allow it to flow to researchers trying to understand how to limit its impacts. [ABSTRACT FROM AUTHOR]