SynthesisJournal of medical systems2021
The Potential of Big Data Research in HealthCare for Medical Doctors' Learning.
Synthesis in Journal of medical systems, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- Study on Ultrasonic Imaging of Nursing Care for Preventing and Treating Clinical Infection of Hemodialysis Patients Based on Smart Medical Big Data.Contrast media & molecular imaging · 2021Trial
- When Healthcare Professionals Use AI: Exploring Work Well-Being Through Psychological Needs Satisfaction and Job Complexity.Behavioral sciences (Basel, Switzerland) · 2025Article
- The Potential of Evidence-Based Clinical Intake Tools to Discover or Ground Prevalence of Symptoms Using Real-Life Digital Health Encounters: Retrospective Cohort Study.Journal of medical Internet research · 2024Article
- Health 4.0 in the medical sector: a narrative review.Revista da Associacao Medica Brasileira (1992) · 2024Article
- Greater agreement is required to harness the potential of health intelligence: a critical interpretive synthesis.Journal of clinical epidemiology · 2023Review
- Research in tourism sustainability: A comprehensive bibliometric analysis from 1990 to 2022.Heliyon · 2023Review
- Comprehensive Analysis of Global Research on Erectile Dysfunction from 2002 to 2021: A Scientometric Approach.International journal of general medicine · 2023Article
- Quality Assurance of a Cross-Border and Sub-Specialized Teleradiology Service.Healthcare (Basel, Switzerland) · 2022Article
- Big Data for Biomedical Education with a Focus on the COVID-19 Era: An Integrative Review of the Literature.International journal of environmental research and public health · 2021Review
- Article
- Machine and deep learning based on magnetic resonance imaging to segment glioblastoma and predict the spread of recurrence: a multicenter retrospective protocol.Frontiers in neurologyArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.