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 13 December 2004
- Accepted 8 May 2005
Abstract
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.
Footnotes
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This work was supported in part by a grant from the National Cancer Institute (U19 CA79689) for The HMO Cancer Research Network (CRN2). The authors acknowledge the work of Jack Hollis, Tom Vogt, Jonathan Winicoff, Ted Palen, Russ Glascow, Sabina Smith, Joan Hollup, Donna Rusinak, and Alanna Rahm, for their assistance in developing the knowledge used by MediClass for smoking cessation care assessment. They thank Rajesh Zade, Steve Balch, Mark Schmidt, Ron Norman, and Ping Shi for their help implementing the system. Jen Coury provided valuable assistance editing this manuscript. They thank Prakash Nadkarni for providing details about the NegFinder system and making publicly available the lexical and grammatical structures used by that system.
