title: MediClass: A system for detecting and classifying encounter-based clinical events in any electronic medical record.
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.
authors.affiliation: Center for Health Research, 3800 N. Interstate Ave., Portland, OR 97227, USA. brian.hazlehurst@kpchr.org
authors.forename: Brian
authors.initials: B
authors.lastname: Hazlehurst
authors.forename: H Robert
authors.initials: HR
authors.lastname: Frost
authors.forename: Dean F
authors.initials: DF
authors.lastname: Sittig
authors.forename: Victor J
authors.initials: VJ
authors.lastname: Stevens
mesh_terms: D001185:Artificial Intelligence; D005568:Forms and Records Control / Q000379:methods*; D006302:Health Services Research; D006801:Humans; D016347:Medical Records Systems, Computerized / Q000145:classification*; D009323:Natural Language Processing; D016540:Smoking Cessation; D018511:Systems Integration; D017432:Unified Medical Language System; D018875:Vocabulary, Controlled