A Framework to Support the Sharing and Reuse of Computable Phenotype Definitions Across Health Care Delivery and Clinical Research Applications.
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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.
Published Version (Please cite this version)
Richesson, RL, MM Smerek and CB Cameron (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). p. 1232. 10.13063/2327-9214.1232 Retrieved from https://hdl.handle.net/10161/12455.
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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 of South Florida College of Medicine directing strategy for the identification and implementation of data standards for a variety of multi-national multi-site clinical research and epidemiological studies housed within the USF Department of Pediatrics, including the NIH Rare Diseases Clinical Research Network (RDCRN) and The Environmental Determinants of Diabetes in the Young (TEDDY) study.
Dr. Richesson has conducted original research on the quality and usability of various terminological data standards, particularly in the context of clinical research, and has presented dozens of posters and invited talks on the topic of data standards in clinical research. She has fostered numerous interdisciplinary research collaborations and is nationally and internationally recognized for her extensive clinical informatics experiences. In 2012, she edited Clinical Research Informatics, the first textbook dedicated to this topic, and co-authored several chapters.
Dr. Richesson is particularly interested in new applications and technologies and standards specifications that will increase the efficiency of clinical research data collection and analysis, and that will enable interoperability between clinical research and health care systems. She co-leads the Phenotyping, Data Standards, and Data Quality Core for the NIH Health Care Systems Research Collaboratory, a demonstration program for the transformation of clinical trials based upon use of electronic health records (EHRs) and healthcare systems partnerships. In this role, she is developing standard approaches and guidance for the extraction of clinical data to support research and learning healthcare systems. She is also the co-lead of the Rare Diseases Task Force for the national distributed Patient Centered Outcomes Research Network (PCORnet), specifically promoting standardized EHR-based condition definitions (“computable phenotypes”) for rare diseases, and helping to develop a national research infrastructure that can support observational and interventional research for various types of conditions.
At DUSON, Dr. Richesson teaches Health Information Exchange Standards, Methods and Models (N410) and Health Information Systems (N409), supports informatics practica (N498), and co-teaches Data-Driven Health Care Improvements (N653). She also engages in informatics-focused initiatives across the Duke campus, particularly within the Duke Center for Health Informatics and Duke Clinical Research Institute programs.
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