title: Mining complex clinical data for patient safety research: a framework for event discovery.
abstract: Successfully addressing patient safety requires detecting medical events effectively. Given the volume of patients seen at medical centers, detecting events automatically from data that are already available electronically would greatly facilitate patient safety work. We have created a framework for electronic detection. Key steps include: selecting target events, assessing what information is available electronically, transforming raw data such as narrative notes into a coded format, querying the transformed data, verifying the accuracy of event detection, characterizing the events using systems and cognitive approaches, and using what is learned to improve detection.
authors.affiliation: Department of Biomedical Informatics, Columbia University, 622 West 168th Street, New York, NY 10032, USA. hripcsak@columbia.edu
authors.forename: George
authors.initials: G
authors.lastname: Hripcsak
authors.forename: Suzanne
authors.initials: S
authors.lastname: Bakken
authors.forename: Peter D
authors.initials: PD
authors.lastname: Stetson
authors.forename: Vimla L
authors.initials: VL
authors.lastname: Patel
mesh_terms: D003628:Database Management Systems; D016208:Databases, Factual; D003661:Decision Support Techniques; D004282:Documentation; D005103:Expert Systems; D016247:Information Storage and Retrieval / Q000379:methods*; D008485:Medical Audit / Q000379:methods*; D019300:Medical Errors / Q000379:methods* / Q000517:prevention & control / Q000706:statistics & numerical data; D016347:Medical Records Systems, Computerized; D015233:Models, Statistical; D010346:Patient Care Management / Q000379:methods; D018570:Risk Assessment / Q000379:methods*; D012308:Risk Management / Q000379:methods*; D017751:Safety Management / Q000379:methods*; D013223:Statistics as Topic / Q000379:methods