A Framework to Support the Sharing and Reuse of Computable Phenotype Definitions Across Health Care Delivery and Clinical Research Applications.
Abstract
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.
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https://hdl.handle.net/10161/12455Published Version (Please cite this version)
10.13063/2327-9214.1232Publication Info
Richesson, RL; Smerek, MM; & Cameron, CB (2016). A Framework to Support the Sharing and Reuse of Computable Phenotype Definitions Across
Health Care Delivery and Clinical Research Applications. EGEMS (Wash DC), 4(3). pp. 1232. 10.13063/2327-9214.1232. Retrieved from https://hdl.handle.net/10161/12455.This is constructed from limited available data and may be imprecise. To cite this
article, please review & use the official citation provided by the journal.
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Show full item recordScholars@Duke
Blake Cameron
Associate Professor of Medicine
Rachel L Richesson
Associate Professor in the School of Nursing
Rachel Richesson, PhD, MPH, a noted informaticist, joined the DUSON faculty in December
2011. Dr. Richesson earned her BS (Biology) at the University of Massachusetts in
1991, and holds graduate degrees in Community Health (MPH, 1995) and Health Informatics
(MS, 2000 and PhD, 2003) from the University of Texas Health Sciences Center in Houston.
Her dissertation involved the integration of heterogeneous data from multiple emergency
departments. Dr. Richesson spent 7 years as at the University
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