The Potential of Big Data Research in HealthCare for Medical Doctors' Learning.

The main goal of this article is to identify the main dimensions of a model proposal for increasing the potential of big data research in Healthcare for medical doctors' (MDs') learning, which appears as a major issue in continuous medical education and learning. The paper employs a systematic liter...

Descripción completa

Detalles Bibliográficos
Publicado en:Journal of Medical Systems Vol. 45; no. 1; pp. 1 - 15
Autores principales: Au-Yong-Oliveira, Manuel, Pesqueira, Antonio, Sousa, Maria José, Dal Mas, Francesca, Soliman, Mohammad
Formato: research systematic review tables/charts Journal Article
Publicado: Springer Nature 2021
Acceso en línea:Ver este registro en EBSCOhost
fields @attributes:
  recordID: 1
pdfLink:
plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=147997210&site=ehost-live
header:
  @attributes:
    shortDbName: ccm
    uiTerm: 147997210
    longDbName: CINAHL Complete
    uiTag: AN
  controlInfo:
    bkinfo:
    dissinfo:
    jinfo:
      jid:
        01485598
        4N0
      jtl: Journal of Medical Systems
      issn: 01485598
      maglogo: N
    pubinfo:
      dt: 2021
      vid: 45
      iid: 1
      pid: 237
      pub: Springer Nature
      place: New York, New York
    artinfo:
      ui:
        147997210
        147997210
        147997210
        10.1007/s10916-020-01691-7
        147997210
      ppf: 1
      ppct: 14
      formats:
        fmt:
          – @attributes:
              type: T
          – @attributes:
              type: P
      tig:
        atl: The Potential of Big Data Research in HealthCare for Medical Doctors' Learning.
      aug:
        au:
          Au-Yong-Oliveira, Manuel
          Pesqueira, Antonio
          Sousa, Maria José
          Dal Mas, Francesca
          Soliman, Mohammad
        affil: INESC TEC, GOVCOPP, Department of Economics, Management, Industrial Engineering and Tourism, University of Aveiro, Aveiro, Portugal
      sug:
        subj:
          Physicians Education
          Learning
          Health Care Delivery
          Data Analytics
          Research, Medical
          Models, Theoretical
          Human
          Systematic Review
          PubMed
          Education, Medical, Continuing
          Data Analysis Software
          Authorship
          Forecasting
          Diffusion of Innovation
          Intelligence
          Decision Making, Clinical
          Disease Diagnosis
          Disease Therapy
          Quality Improvement
          Medical Practice, Evidence-Based
          Resource Databases
          Health Care Industry
          Medline
          Funding Source
      ab: The main goal of this article is to identify the main dimensions of a model proposal for increasing the potential of big data research in Healthcare for medical doctors' (MDs') learning, which appears as a major issue in continuous medical education and learning. The paper employs a systematic literature review of main scientific databases (PubMed and Google Scholar), using the VOSviewer software tool, which enables the visualization of scientific landscapes. The analysis includes a co-authorship data analysis as well as the co-occurrence of terms and keywords. The results lead to the construction of the learning model proposed, which includes four health big data key areas for MDs' learning: 1) data transformation is related to the learning that occurs through medical systems; 2) health intelligence includes the learning regarding health innovation based on predictions and forecasting processes; 3) data leveraging regards the learning about patient information; and 4) the learning process is related to clinical decision-making, focused on disease diagnosis and methods to improve treatments. Practical models gathered from the scientific databases can boost the learning process and revolutionise the medical industry, as they store the most recent knowledge and innovative research.
      pubtype: Academic Journal
      doctype:
        research
        systematic review
        tables/charts
        Journal Article
      ougenre: Article
    language: English
    refInfo:
    holdings:
      @attributes:
        islocal: N