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MediClass: A System for Detecting and Classifying Encounter-based Clinical Events in Any Electronic Medical Record

MediClass: A System for Detecting and Classifying Encounter-based Clinical Events in Any... AbstractMediClass is a knowledge-based system that processes both free-text and coded data to automatically detect clinical events in electronic medical records (EMRs). This technology aims to optimize both clinical practice and process control by automatically coding EMR contents regardless of data input method (e.g., dictation, structured templates, typed narrative). We report on the design goals, implemented functionality, generalizability, and current status of the system. MediClass could aid both clinical operations and health services research through enhancing care quality assessment, disease surveillance, and adverse event detection. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of the American Medical Informatics Association Oxford University Press

MediClass: A System for Detecting and Classifying Encounter-based Clinical Events in Any Electronic Medical Record

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Publisher
Oxford University Press
Copyright
American Medical Informatics Association
ISSN
1067-5027
eISSN
1527-974X
DOI
10.1197/jamia.M1771
pmid
15905485
Publisher site
See Article on Publisher Site

Abstract

AbstractMediClass is a knowledge-based system that processes both free-text and coded data to automatically detect clinical events in electronic medical records (EMRs). This technology aims to optimize both clinical practice and process control by automatically coding EMR contents regardless of data input method (e.g., dictation, structured templates, typed narrative). We report on the design goals, implemented functionality, generalizability, and current status of the system. MediClass could aid both clinical operations and health services research through enhancing care quality assessment, disease surveillance, and adverse event detection.

Journal

Journal of the American Medical Informatics Association – Oxford University Press

Published: Sep 1, 2005

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