2 resultados para Research Infrastructure
em Duke University
Resumo:
The Veterans Health Administration (VHA) in the Department of Veteran Affairs (VA) has emerged as a national and international leader in the delivery and research of telehealth-based treatment. Several unique characteristics of care in VA settings intersect to create an ideal environment for telehealth modalities and research. However, the value of telehealth experience and initiatives in VA settings is limited if telehealth strategies cannot be widely exported to other public or private systems. Whereas a hierarchical organization, such as VA, can innovate and fund change relatively quickly based on provider and patient preferences and a growing knowledge base, other health provider organizations and third-party payers may likely require replicable scientific findings over time before incremental investments will be made to create infrastructure, reform regulatory barriers, and amend laws to accommodate expansion of telehealth modalities. Accordingly, large-scale scientifically rigorous telehealth research in VHA settings is essential not only to investigate the efficacy of existing and future telehealth practices in VHA, but also to hasten the development of telehealth infrastructure in private and other public health settings. We propose an expanded partnership between the VA, NIH, and other funding agencies to investigate creative and pragmatic uses of telehealth technology. To this end, we identify six specific areas of research we believe to be particularly relevant to the efficient development of telehealth modalities in civilian and military contexts outside VHA.
Resumo:
INTRODUCTION: The ability to reproducibly identify clinically equivalent patient populations is critical to the vision of learning health care systems that implement and evaluate evidence-based treatments. The use of common or semantically equivalent phenotype definitions across research and health care use cases will support this aim. Currently, there is no single consolidated repository for computable phenotype definitions, making it difficult to find all definitions that already exist, and also hindering the sharing of definitions between user groups. METHOD: Drawing from our experience in an academic medical center that supports a number of multisite research projects and quality improvement studies, we articulate a framework that will support the sharing of phenotype definitions across research and health care use cases, and highlight gaps and areas that need attention and collaborative solutions. FRAMEWORK: An infrastructure for re-using computable phenotype definitions and sharing experience across health care delivery and clinical research applications includes: access to a collection of existing phenotype definitions, information to evaluate their appropriateness for particular applications, a knowledge base of implementation guidance, supporting tools that are user-friendly and intuitive, and a willingness to use them. NEXT STEPS: We encourage prospective researchers and health administrators to re-use existing EHR-based condition definitions where appropriate and share their results with others to support a national culture of learning health care. There are a number of federally funded resources to support these activities, and research sponsors should encourage their use.