title: Big Data and Analytics in Healthcare.
abstract: This editorial is part of the Focus Theme of Methods of Information in Medicine on "Big Data and Analytics in Healthcare". The amount of data being generated in the healthcare industry is growing at a rapid rate. This has generated immense interest in leveraging the availability of healthcare data (and "big data") to improve health outcomes and reduce costs. However, the nature of healthcare data, and especially big data, presents unique challenges in processing and analyzing big data in healthcare. This Focus Theme aims to disseminate some novel approaches to address these challenges. More specifically, approaches ranging from efficient methods of processing large clinical data to predictive models that could generate better predictions from healthcare data are presented.
authors.affiliation: Sharon Swee-Lin Tan, Centre for Health Informatics, Department of Information Systems, National University of Singapore, Singapore, E-mail: distans@nus.edu.sg.
authors.forename: S S-L
authors.initials: SS
authors.lastname: Tan
authors.forename: G
authors.initials: G
authors.lastname: Gao
authors.forename: S
authors.initials: S
authors.lastname: Koch
mesh_terms: D057225:Data Mining / Q000379:methods*; D066264:Datasets as Topic / Q000706:statistics & numerical data*; D057286:Electronic Health Records / Q000145:classification / Q000458:organization & administration*; D062527:Meaningful Use / Q000458:organization & administration*; D008498:Medical Record Linkage / Q000379:methods*
keywords: Healthcare analytics; big data; healthcare informatics; natural language processing; predictive analytics