Browsing by Author "Silcox, Christina"

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    A National Decision Point: Effective Testing and Screening for Covid-19
    (2020-09-09) McClellan, Mark; Silcox, Christina; Anderson, David; Zavodszky, Anna; Borre, Ethan; Dentzer, Susan; Aspinall, Mara
    This Duke-Margolis report out provides a framework for public health officials and community leaders in schools, businesses and other institutions on how to use Covid-19 screening test strategies to operate safely and prevent further spread of the virus. A National Decision Point: Effective Testing and Screening for Covid-19, was developed with funding from the Rockefeller Foundation, and aims to be a useful tool to help officials customize screening strategies of asymptomatic people to local circumstances and risk – with a particular focus on higher-risk populations and suppressing community spread.
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    From Development to Market: Understanding COVID-19 Testing and Its Challenges
    (2020-08-19) McClellan, Mark; Schneider, Monika; Dentzer, Susan; Sheehan, Sarah; Silcox, Christina; Hamilton Lopez, Marianne; Wosinska, Marta
    Amid the SARS-CoV-2 pandemic and a crisis over inadequate and delayed testing, this report describes COVID-19 testing methods and applications, the regulatory process for approving tests, how tests are paid for, and how access to testing is obtained. The report also highlights the challenges that stakeholders are facing and will face in the coming months around COVID-19 testing.
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    Legislative and Regulatory Steps for a National COVID-19 Testing Strategy
    (2020-08-05) McClellan, Mark; Rivers, Caitlin; Silcox, Christina
    The Duke-Margolis Center for Health Policy report, “Legislative and Regulatory Steps for a National COVID-19 Testing Strategy,” outlines legislative actions and federal appropriation targets that Duke-Margolis and its collaborating experts believe are needed to get a robust, diversified testing strategy in place for the nation by fall 2020.
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    Legislative and Regulatory Steps for a National COVID-19 Testing Strategy
    (2020-08-05) McClellan, Mark; Caitlin Rivers, Caitlin; Silcox, Christina
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    Testing as an Alternative to Quarantining: Key Considerations and Best Practices for Implementing Test to Stay
    (Duke-Margolis Center for Health Policy, 2022-01-19) McClellan, Mark; Silcox, Christina; Roades, Thomas; Thoumi, Andrea
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    The potential for artificial intelligence to transform healthcare: perspectives from international health leaders.
    (NPJ digital medicine, 2024-04) Silcox, Christina; Zimlichmann, Eyal; Huber, Katie; Rowen, Neil; Saunders, Robert; McClellan, Mark; Kahn, Charles N; Salzberg, Claudia A; Bates, David W
    Artificial intelligence (AI) has the potential to transform care delivery by improving health outcomes, patient safety, and the affordability and accessibility of high-quality care. AI will be critical to building an infrastructure capable of caring for an increasingly aging population, utilizing an ever-increasing knowledge of disease and options for precision treatments, and combatting workforce shortages and burnout of medical professionals. However, we are not currently on track to create this future. This is in part because the health data needed to train, test, use, and surveil these tools are generally neither standardized nor accessible. There is also universal concern about the ability to monitor health AI tools for changes in performance as they are implemented in new places, used with diverse populations, and over time as health data may change. The Future of Health (FOH), an international community of senior health care leaders, collaborated with the Duke-Margolis Institute for Health Policy to conduct a literature review, expert convening, and consensus-building exercise around this topic. This commentary summarizes the four priority action areas and recommendations for health care organizations and policymakers across the globe that FOH members identified as important for fully realizing AI's potential in health care: improving data quality to power AI, building infrastructure to encourage efficient and trustworthy development and evaluations, sharing data for better AI, and providing incentives to accelerate the progress and impact of AI.