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Cohort Identification for Clinical Research: Querying Federated Electronic Healthcare Records Using Controlled Vocabularies and Semantic Types
In the United Kingdom (UK), local initiatives have started to federate electronic healthcare records from different primary care clinical systems, mainly for the purposes of ensuring that health care services effectively meet the needs of the population. The use of such information is being investig...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Medical Informatics Association
2012
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3392063/ https://www.ncbi.nlm.nih.gov/pubmed/22779039 |
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author | Lim Choi Keung, Sarah N. Zhao, Lei Tyler, Edward Taweel, Adel Delaney, Brendan Peterson, Kevin A. Speedie, Stuart M. Richard Hobbs, F.D. Arvanitis, Theodoros N. |
author_facet | Lim Choi Keung, Sarah N. Zhao, Lei Tyler, Edward Taweel, Adel Delaney, Brendan Peterson, Kevin A. Speedie, Stuart M. Richard Hobbs, F.D. Arvanitis, Theodoros N. |
author_sort | Lim Choi Keung, Sarah N. |
collection | PubMed |
description | In the United Kingdom (UK), local initiatives have started to federate electronic healthcare records from different primary care clinical systems, mainly for the purposes of ensuring that health care services effectively meet the needs of the population. The use of such information is being investigated for clinical research, notably in patient cohort identification and recruitment. To achieve these aims, it is essential that the information from different systems can be searched from a single interface. While interoperability is a widely researched topic, interoperable methods and data sources in primary care are largely missing. This paper describes our approach to enabling primary care data in England to be searchable on a platform developed for performing large national collaborative primary care research studies throughout the United States. |
format | Online Article Text |
id | pubmed-3392063 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | American Medical Informatics Association |
record_format | MEDLINE/PubMed |
spelling | pubmed-33920632012-07-09 Cohort Identification for Clinical Research: Querying Federated Electronic Healthcare Records Using Controlled Vocabularies and Semantic Types Lim Choi Keung, Sarah N. Zhao, Lei Tyler, Edward Taweel, Adel Delaney, Brendan Peterson, Kevin A. Speedie, Stuart M. Richard Hobbs, F.D. Arvanitis, Theodoros N. AMIA Jt Summits Transl Sci Proc Articles In the United Kingdom (UK), local initiatives have started to federate electronic healthcare records from different primary care clinical systems, mainly for the purposes of ensuring that health care services effectively meet the needs of the population. The use of such information is being investigated for clinical research, notably in patient cohort identification and recruitment. To achieve these aims, it is essential that the information from different systems can be searched from a single interface. While interoperability is a widely researched topic, interoperable methods and data sources in primary care are largely missing. This paper describes our approach to enabling primary care data in England to be searchable on a platform developed for performing large national collaborative primary care research studies throughout the United States. American Medical Informatics Association 2012-03-19 /pmc/articles/PMC3392063/ /pubmed/22779039 Text en ©2012 AMIA - All rights reserved. This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose |
spellingShingle | Articles Lim Choi Keung, Sarah N. Zhao, Lei Tyler, Edward Taweel, Adel Delaney, Brendan Peterson, Kevin A. Speedie, Stuart M. Richard Hobbs, F.D. Arvanitis, Theodoros N. Cohort Identification for Clinical Research: Querying Federated Electronic Healthcare Records Using Controlled Vocabularies and Semantic Types |
title | Cohort Identification for Clinical Research: Querying Federated Electronic Healthcare Records Using Controlled Vocabularies and Semantic Types |
title_full | Cohort Identification for Clinical Research: Querying Federated Electronic Healthcare Records Using Controlled Vocabularies and Semantic Types |
title_fullStr | Cohort Identification for Clinical Research: Querying Federated Electronic Healthcare Records Using Controlled Vocabularies and Semantic Types |
title_full_unstemmed | Cohort Identification for Clinical Research: Querying Federated Electronic Healthcare Records Using Controlled Vocabularies and Semantic Types |
title_short | Cohort Identification for Clinical Research: Querying Federated Electronic Healthcare Records Using Controlled Vocabularies and Semantic Types |
title_sort | cohort identification for clinical research: querying federated electronic healthcare records using controlled vocabularies and semantic types |
topic | Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3392063/ https://www.ncbi.nlm.nih.gov/pubmed/22779039 |
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