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Veterans Health Administration (VHA) services are most frequently used by patients 65 years and older, an age group that is disproportionally affected by COVID-19. Here we describe a modular Clinical Trial Informatics Solution (CTIS) that was rapidly developed and deployed to support a multi-hospital embedded pragmatic clinical trial in COVID-19 patients within the VHA. Our CTIS includes tools for patient eligibility screening, informed consent tracking, treatment randomization, EHR data transformation for reporting and interfaces for patient outcome and adverse event tracking. We hope our CTIS component descriptions and practical lessons learned will serve as a useful building block for others creating their own clinical trial tools and have made application and database code publicly available.

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.

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.

Background: There is wide interest in developing prognostic models in non–small-cell lung cancer (NSCLC) due to the heterogeneity of the disease. Models developed at other healthcare institutions may not be directly applicable for patients treated at the Department of Veterans Affairs (VA). External validation of a candidate prognostic model among VA patients would be crucial before it can be implemented to aid clinical decision-making. Methods: A prognostic model for mortality developed in the Military Health System (MHS) was applied to data from the VA Precision Oncology Data Repository (VA-PODR), which is available to researchers inside and outside the VA at the Veterans Precision Oncology Data Commons (VPODC). Measures of discrimination and calibration were calculated for the MHS model. The MHS model was also refitted in VA-PODR data using the same risk factors to compare the effect of specific factors and predictive performance when the model is developed using VA data. Results: Time-dependent AUC of the MHS prognostic model was 0.788, 0.806, 0.780, and 0.779 for predicting survival at 1, 2, 3, and 5 years following diagnosis, respectively. Significant discrepancies were found between predicted and observed rates of survival, particularly for later years. When the model is refit in VA-PODR data, it achieved cross-validated AUCs of 0.739, 0.773, 0.769, and 0.807 at the same time points, and discrepancies between predicted and observed survival were reduced. Conclusions: Validation of the MHS prognostic model in VA-PODR demonstrates that its discrimination remains strong when applied to VA patients. Nevertheless, further calibration to VA data may be needed to improve its risk estimation performance. This study highlights the utility of VA-PODR and the VPODC as a national resource for developing analytic tools that are well adapted to the Veteran population.

The Department of Veterans Affairs (VA) has a strong track record providing high-quality, evidence-based care to cancer patients. In order to accelerate discoveries that will further improve care for Veterans with cancer, the VA has partnered with the Center for Translational Data Science at the University of Chicago and the Open Commons Consortium to establish a data sharing platform, the Veterans Precision Oncology Data Commons (VPODC). The VPODC makes clinical, genomic, and imaging data from the VA available to the research community at large. In this paper, we detail our motivation for data sharing, describe the VPODC, and outline our collaboration model. By transforming VA data into a national resource for research in precision oncology, the VPODC seeks to foster innovation through collaboration and resource sharing that will ultimately lead to improved care for Veterans with cancer.

Summary The Veterans Affairs Precision Oncology Data Repository (VA-PODR) is a large, nationwide repository of de-identified data on patients diagnosed with cancer at the Department of Veterans Affairs (VA). Data include longitudinal clinical data from the VA's nationwide electronic health record system and the VA Central Cancer Registry, targeted tumor sequencing data, and medical imaging data including computed tomography (CT) scans and pathology slides. A subset of the repository is available at the Genomic Data Commons (GDC) and The Cancer Imaging Archive (TCIA), and the full repository is available through the Veterans Precision Oncology Data Commons (VPODC). By releasing this de-identified dataset, we aim to advance Veterans' health care through enabling translational research on the Veteran population by a wide variety of researchers., Highlights • Presents a large-scale repository on cancer patients with EHR, imaging, and genomic data • Describes a method for de-identification of different types of health data • Enables researchers to use data science tools to apply to the data, The Bigger Picture Accelerating the speed of innovation and discoveries in health care requires the liberation of real-world data from their silos. One of the greatest challenges in the application of artificial intelligence and machine learning to health care is the validation of new algorithms beyond where they were created. We present the Veterans Affairs Precision Oncology Data Repository (VA-PODR), a large-scale repository of de-identified data on patients diagnosed with cancer at the Department of Veterans Affairs (VA). VA-PODR includes longitudinal clinical, genomic, and imaging data originating from the VA's electronic health record system, the VA Central Cancer Registry, and other sources. VA-PODR enables researchers around the world to validate their algorithms and advance cancer research and health care in general. In addition, VA-PODR enhances Veterans' health care by facilitating development of algorithms that are well tuned to the Veteran population and ready for deployment inside the VA., The Veterans Affairs Precision Oncology Data Repository (VA-PODR) is a large, nationwide repository of real-world de-identified data on patients diagnosed with cancer at the Department of Veteran's Affairs. The repository contains longitudinal clinical data from the VA's nationwide electronic health record system and the VA Central Cancer Registry, targeted tumor sequencing data, and medical imaging data. By releasing this dataset, we aim to advance Veterans' health care through enabling translational research on the Veteran population by a wide variety of researchers.