Browsing by Author "Weiner, Mark G."

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    Developing real‐world evidence from real‐world data: Transforming raw data into analytical datasets
    (Wiley, 2021-10-14) Bastarache, Lisa; Brown, Jeffrey S.; Cimino, James J.; Dorr, David A.; Embi, Peter J.; Payne, Philip R. O.; Wilcox, Adam B.; Weiner, Mark G.; Medicine, School of Medicine
    Development of evidence-based practice requires practice-based evidence, which can be acquired through analysis of real-world data from electronic health records (EHRs). The EHR contains volumes of information about patients-physical measurements, diagnoses, exposures, and markers of health behavior-that can be used to create algorithms for risk stratification or to gain insight into associations between exposures, interventions, and outcomes. But to transform real-world data into reliable real-world evidence, one must not only choose the correct analytical methods but also have an understanding of the quality, detail, provenance, and organization of the underlying source data and address the differences in these characteristics across sites when conducting analyses that span institutions. This manuscript explores the idiosyncrasies inherent in the capture, formatting, and standardization of EHR data and discusses the clinical domain and informatics competencies required to transform the raw clinical, real-world data into high-quality, fit-for-purpose analytical data sets used to generate real-world evidence.
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    Use of Electronic Health Records to Support a Public Health Response to the COVID-19 Pandemic in the United States: A Perspective from Fifteen Academic Medical Centers
    (Oxford University Press, 2020-11-03) Madhavan, Subha; Bastarache, Lisa; Brown, Jeffrey S.; Dorr, David A.; Embi, Peter J.; Friedman, Charles P.; Johnson, Kevin B.; Moore, Jason H.; Kohane, Isaac S.; Payne, Philip R.O.; Tenenbaum, Jessica D.; Weiner, Mark G.; Wilcox, Adam B.; Ohno-Machado, Lucila; Butte, Atul J.; Medicine, School of Medicine
    Our goal is to summarize the collective experience of 15 organizations in dealing with uncoordinated efforts that result in unnecessary delays in understanding, predicting, preparing for, containing, and mitigating the COVID-19 pandemic in the US. Response efforts involve the collection and analysis of data corresponding to healthcare organizations, public health departments, socioeconomic indicators, as well as additional signals collected directly from individuals and communities. We focused on electronic health record (EHR) data, since EHRs can be leveraged and scaled to improve clinical care, research, and to inform public health decision-making. We outline the current challenges in the data ecosystem and the technology infrastructure that are relevant to COVID-19, as witnessed in our 15 institutions. The infrastructure includes registries and clinical data networks to support population-level analyses. We propose a specific set of strategic next steps to increase interoperability, overall organization, and efficiencies