• DocumentCode
    3603386
  • Title

    METEOR: An Enterprise Health Informatics Environment to Support Evidence-Based Medicine

  • Author

    Puppala, Mamta ; Tiancheng He ; Shenyi Chen ; Ogunti, Richard ; Xiaohui Yu ; Fuhai Li ; Jackson, Robert ; Wong, Stephen T. C.

  • Volume
    62
  • Issue
    12
  • fYear
    2015
  • Firstpage
    2776
  • Lastpage
    2786
  • Abstract
    Goal: The aim of this paper is to propose the design and implementation of next-generation enterprise analytics platform developed at the Houston Methodist Hospital (HMH) system to meet the market and regulatory needs of the healthcare industry. Methods: For this goal, we developed an integrated clinical informatics environment, i.e., Methodist environment for translational enhancement and outcomes research (METEOR). The framework of METEOR consists of two components: the enterprise data warehouse (EDW) and a software intelligence and analytics (SIA) layer for enabling a wide range of clinical decision support systems that can be used directly by outcomes researchers and clinical investigators to facilitate data access for the purposes of hypothesis testing, cohort identification, data mining, risk prediction, and clinical research training. Results: Data and usability analysis were performed on METEOR components as a preliminary evaluation, which successfully demonstrated that METEOR addresses significant niches in the clinical informatics area, and provides a powerful means for data integration and efficient access in supporting clinical and translational research. Conclusion: METEOR EDW and informatics applications improved outcomes, enabled coordinated care, and support health analytics and clinical research at HMH. Significance: The twin pressures of cost containment in the healthcare market and new federal regulations and policies have led to the prioritization of the meaningful use of electronic health records in the United States. EDW and SIA layers on top of EDW are becoming an essential strategic tool to healthcare institutions and integrated delivery networks in order to support evidence-based medicine at the enterprise level.
  • Keywords
    data analysis; data integration; data mining; data warehouses; decision support systems; electronic health records; health care; information retrieval; HMH; Houston Methodist Hospital system; METEOR EDW; SIA layers; clinical decision support systems; clinical informatics area; clinical research training; cohort identification; cost containment; data access; data analysis; data integration; data mining; electronic health records; enterprise analytics platform; enterprise data warehouse; enterprise health informatics environment; enterprise level; evidence-based medicine; health analytics; healthcare industry; healthcare institutions; healthcare market; hypothesis testing; informatics applications; integrated clinical informatics environment; integrated delivery networks; methodist environment for translational enhancement and outcomes research; preliminary evaluation; risk prediction; software intelligence and analytics layer; usability analysis; Data mining; Data warehouses; Databases; Hospitals; Informatics; Natural language processing; Clinical Data Warehouse; Clinical data warehouse; Cohort Identification; Natural Language Processing; Outcomes Research; Readmission Risk; Smartphone Health App; cohort identification; natural language processing (NLP); outcomes research; readmission risk; smartphone health app;
  • fLanguage
    English
  • Journal_Title
    Biomedical Engineering, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0018-9294
  • Type

    jour

  • DOI
    10.1109/TBME.2015.2450181
  • Filename
    7137654