title: Toward a Learning Health-care System - Knowledge Delivery at the Point of Care Empowered by Big Data and NLP.
abstract: The concept of optimizing health care by understanding and generating knowledge from previous evidence, ie, the Learning Health-care System (LHS), has gained momentum and now has national prominence. Meanwhile, the rapid adoption of electronic health records (EHRs) enables the data collection required to form the basis for facilitating LHS. A prerequisite for using EHR data within the LHS is an infrastructure that enables access to EHR data longitudinally for health-care analytics and real time for knowledge delivery. Additionally, significant clinical information is embedded in the free text, making natural language processing (NLP) an essential component in implementing an LHS. Herein, we share our institutional implementation of a big data-empowered clinical NLP infrastructure, which not only enables health-care analytics but also has real-time NLP processing capability. The infrastructure has been utilized for multiple institutional projects including the MayoExpertAdvisor, an individualized care recommendation solution for clinical care. We compared the advantages of big data over two other environments. Big data infrastructure significantly outperformed other infrastructure in terms of computing speed, demonstrating its value in making the LHS a possibility in the near future.
authors.affiliation: Division of Information Management and Analytics, Mayo Clinic, Rochester, MN, USA.; Biomedical Informatics and Computational Biology, University of Minnesota, Rochester, MN, USA.
authors.forename: Vinod C
authors.initials: VC
authors.lastname: Kaggal
authors.affiliation: Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
authors.forename: Ravikumar Komandur
authors.initials: RK
authors.lastname: Elayavilli
authors.affiliation: Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
authors.forename: Saeed
authors.initials: S
authors.lastname: Mehrabi
authors.affiliation: Division of Information Management and Analytics, Mayo Clinic, Rochester, MN, USA.
authors.forename: Joshua J
authors.initials: JJ
authors.lastname: Pankratz
authors.affiliation: Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
authors.forename: Sunghwan
authors.initials: S
authors.lastname: Sohn
authors.affiliation: Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
authors.forename: Yanshan
authors.initials: Y
authors.lastname: Wang
authors.affiliation: Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
authors.forename: Dingcheng
authors.initials: D
authors.lastname: Li
authors.affiliation: Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
authors.forename: Majid Mojarad
authors.initials: MM
authors.lastname: Rastegar
authors.affiliation: Division of Information Management and Analytics, Mayo Clinic, Rochester, MN, USA.
authors.forename: Sean P
authors.initials: SP
authors.lastname: Murphy
authors.affiliation: Division of Information Management and Analytics, Mayo Clinic, Rochester, MN, USA.
authors.forename: Jason L
authors.initials: JL
authors.lastname: Ross
authors.affiliation: Department of Medicine, Mayo Clinic, Rochester, MN, USA.
authors.forename: Rajeev
authors.initials: R
authors.lastname: Chaudhry
authors.affiliation: Division of Information Management and Analytics, Mayo Clinic, Rochester, MN, USA.
authors.forename: James D
authors.initials: JD
authors.lastname: Buntrock
authors.affiliation: Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
authors.forename: Hongfang
authors.initials: H
authors.lastname: Liu
keywords: big data; health-care analytics; learning health-care system; natural language processing