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Introduction: Hepatitis C, which affects 71 million persons worldwide, is the most common blood-borne pathogen in the United States. Chronic infections can be treated effectively thanks to the availability of modern direct-acting antiviral (DAA) therapies. Real-world data on the duration of DAA therapy, which can be used to optimize and guide the course of therapy, may also be useful in determining quality of life enhancements based upon total required supply of medication and long-term improvements to quality of life. We developed a machine learning model to identify patient characteristics associated with prolonged DAA treatment duration., Methods: A nationwide U.S. commercial managed care plan with claims data that covers about 60 million beneficiaries from 2009 to 2019 were used in the retrospective study. We examined differences in age, gender, and multiple comorbidities among patients treated with different durations of DAA treatment. We also examined the performance of machine learning models for predicting a prolonged course of DAA based on the area under the receiver operating characteristic curve (AUC)., Results: We identified 3943 cases with hepatitis C who received sofosbuvir/ledipasvir as the first course of DAA and were eligible for the study. Patients receiving prolonged treatment (n = 240, 6.1%) were more likely to have compensated cirrhosis, decompensated cirrhosis, and other comorbidities (P < 0.001). For distinguishing patients who received prolonged DAA treatment for hepatitis C from patients received standard treatment, the optimal predictive model, constructed with XGBoost, had an AUC of 0.745 ± 0.031 (P < 0.001)., Conclusions: The risk of antiviral resistance and the cost of DAA are strong motivators to ensure that first-round DAA therapy is effective. For the dominant DAA treatment during the course of this analysis, we present a model that identifies factors already captured in established guidelines and adds to those age, comorbidity burden, and type 2 diabetes status; patient characteristics that are predictive of extended treatment., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Objective: Reducing risk of coronavirus disease 2019 (COVID-19) infection among healthcare personnel requires a robust occupational health response involving multiple disciplines. We describe a flexible informatics solution to enable such coordination, and we make it available as open-source software., Materials and Methods: We developed a stand-alone application that integrates data from several sources, including electronic health record data and data captured outside the electronic health record., Results: The application facilitates workflows from different hospital departments, including Occupational Health and Infection Control, and has been used extensively. As of June 2020, 4629 employees and 7768 patients and have been added for tracking by the application, and the application has been accessed over 46 000 times., Discussion: Data captured by the application provides both a historical and real-time view into the operational impact of COVID-19 within the hospital, enabling aggregate and patient-level reporting to support identification of new cases, contact tracing, outbreak investigations, and employee workforce management., Conclusions: We have developed an open-source application that facilitates communication and workflow across multiple disciplines to manage hospital employees impacted by the COVID-19 pandemic., (Published by Oxford University Press on behalf of the American Medical Informatics Association 2020. This work is written by US Government employees and is in the public domain in the US.)