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Health Data-powered Discovery

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The goal of the Health Data-Powered Discovery research group is to target a limited set of large health databases, learn their strengths and weaknesses, improve them when possible (e.g,. by applying standardized codes), gain insights from the data, and share our expertise with other researchers. This interest stems from NLM’s role as “A Platform for Biomedical Discovery and Data-Powered Health” defined in the recently published NLM strategic plan, and more specifically in Goal 1 of the plan, “Accelerate discovery and advance health through data-driven research”.




Clinical Data Entry Tools

The goal of this project is to develop a tool that can generate data entry forms dynamically based on specifications stored in a database. The development platform is Ruby on Rails, an open-source web application framework. Developers are using this tool in the data capture function of personal health records. They are also using several terminology resources from the UMLS (e.g. RxNORM, ICD9-CM) in data entry fields that require a set of controlled terms. Further development will involve work with very large databases of de-identified patient data.


Clinical Vocabulary Standards

Multiple projects in this area continue to promote the development, enhancement, and adoption of clinical vocabulary standards. Inter-terminology mapping promotes the use of standard terminologies by creating maps to administrative terminologies, which allows re-use of encoded clinical data.


CMS’s Virtual Research Data Center (VRDC)

Provided by the Center for Medicare and Medicaid Services (CMS), the VRDC now carries 17 years of Parts A and B claims data including diagnoses, proce

Discoveries from Clinical Data small image waveform

Discoveries from MIMIC II/III and Other Sources

Large database collections of clinical data -- from longitudinal research projects, electronic medical records, and health information exchanges -- provide opportunities to examine controversial findings from smaller scale clinical studies and to conduct retrospective epidemiological studies in areas that lack clinical trials.