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

Affiliations of the authors: Kaiser Permanente Center for Health Research, Portland, OR (BH, VJS), HRF Engineering, New London, NH (HRF); Department of Medical Informatics, Kaiser Permanente, Northwest, Portland, OR (DFS).

Correspondence and reprints: Brian L. Hazlehurst, PhD, Center for Health Research, 3800 N. Interstate Ave., Portland, OR 97227; e-mail: <brian.hazlehurst{at}kpchr.org>.

Received for publication: 12/13/04; accepted for publication: 05/08/05.

MediClass 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.

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