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Big Data for Biomedical Education with a Focus on the COVID-19 Era: An Integrative Review of the Literature

Author

Listed:
  • Rola Khamisy-Farah

    (Clalit Health Services, Haifa & Western Galilee, Azrieli Faculty of Medicine, Bar-Ilan University, Safed 13100, Israel)

  • Peter Gilbey

    (Clalit Health Services, Haifa & Western Galilee, Azrieli Faculty of Medicine, Bar-Ilan University, Safed 13100, Israel)

  • Leonardo B. Furstenau

    (Department of Industrial Engineering, Federal University of Rio Grande do Sul, Porto Alegre 90035-190, Brazil)

  • Michele Kremer Sott

    (Business School, Unisinos University, Porto Alegre 91330-002, Brazil)

  • Raymond Farah

    (Department of Internal Medicine B, Ziv Medical Center, Azrieli Faculty of Medicine, Bar-Ilan University, Safed 13100, Israel)

  • Maurizio Viviani

    (TransHumanGene, MedicaSwiss, 6330 Cham, Switzerland)

  • Maurizio Bisogni

    (TransHumanGene, MedicaSwiss, 6330 Cham, Switzerland)

  • Jude Dzevela Kong

    (Laboratory for Industrial and Applied Mathematics (LIAM), Department of Mathematics and Statistics, York University, Toronto, ON M3J 1P3, Canada)

  • Rosagemma Ciliberti

    (Section of History of Medicine and Bioethics, Department of Health Sciences (DISSAL), University of Genoa, 16132 Genoa, Italy)

  • Nicola Luigi Bragazzi

    (Laboratory for Industrial and Applied Mathematics (LIAM), Department of Mathematics and Statistics, York University, Toronto, ON M3J 1P3, Canada)

Abstract

Medical education refers to education and training delivered to medical students in order to become a practitioner. In recent decades, medicine has been radically transformed by scientific and computational/digital advances—including the introduction of new information and communication technologies, the discovery of DNA, and the birth of genomics and post-genomics super-specialties (transcriptomics, proteomics, interactomics, and metabolomics/metabonomics, among others)—which contribute to the generation of an unprecedented amount of data, so-called ‘big data’. While these are well-studied in fields such as medical research and methodology, translational medicine, and clinical practice, they remain overlooked and understudied in the field of medical education. For this purpose, we carried out an integrative review of the literature. Twenty-nine studies were retrieved and synthesized in the present review. Included studies were published between 2012 and 2021. Eleven studies were performed in North America: specifically, nine were conducted in the USA and two studies in Canada. Six studies were carried out in Europe: two in France, two in Germany, one in Italy, and one in several European countries. One additional study was conducted in China. Eight papers were commentaries/theoretical or perspective articles, while five were designed as a case study. Five investigations exploited large databases and datasets, while five additional studies were surveys. Two papers employed visual data analytical/data mining techniques. Finally, other two papers were technical papers, describing the development of software, computational tools and/or learning environments/platforms, while two additional studies were literature reviews (one of which being systematic and bibliometric).The following nine sub-topics could be identified: (I) knowledge and awareness of big data among medical students; (II) difficulties and challenges in integrating and implementing big data teaching into the medical syllabus; (III) exploiting big data to review, improve and enhance medical school curriculum; (IV) exploiting big data to monitor the effectiveness of web-based learning environments among medical students; (V) exploiting big data to capture the determinants and signatures of successful academic performance and counteract/prevent drop-out; (VI) exploiting big data to promote equity, inclusion, and diversity; (VII) exploiting big data to enhance integrity and ethics, avoiding plagiarism and duplication rate; (VIII) empowering medical students, improving and enhancing medical practice; and, (IX) exploiting big data in continuous medical education and learning. These sub-themes were subsequently grouped in the following four major themes/topics: namely, (I) big data and medical curricula; (II) big data and medical academic performance; (III) big data and societal/bioethical issues in biomedical education; and (IV) big data and medical career. Despite the increasing importance of big data in biomedicine, current medical curricula and syllabuses appear inadequate to prepare future medical professionals and practitioners that can leverage on big data in their daily clinical practice. Challenges in integrating, incorporating, and implementing big data teaching into medical school need to be overcome to facilitate the training of the next generation of medical professionals. Finally, in the present integrative review, state-of-art and future potential uses of big data in the field of biomedical discussion are envisaged, with a focus on the still ongoing “Coronavirus Disease 2019” (COVID-19) pandemic, which has been acting as a catalyst for innovation and digitalization.

Suggested Citation

  • Rola Khamisy-Farah & Peter Gilbey & Leonardo B. Furstenau & Michele Kremer Sott & Raymond Farah & Maurizio Viviani & Maurizio Bisogni & Jude Dzevela Kong & Rosagemma Ciliberti & Nicola Luigi Bragazzi, 2021. "Big Data for Biomedical Education with a Focus on the COVID-19 Era: An Integrative Review of the Literature," IJERPH, MDPI, vol. 18(17), pages 1-16, August.
  • Handle: RePEc:gam:jijerp:v:18:y:2021:i:17:p:8989-:d:622410
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    References listed on IDEAS

    as
    1. Rola Khamisy-Farah & Leonardo B. Furstenau & Jude Dzevela Kong & Jianhong Wu & Nicola Luigi Bragazzi, 2021. "Gynecology Meets Big Data in the Disruptive Innovation Medical Era: State-of-Art and Future Prospects," IJERPH, MDPI, vol. 18(10), pages 1-13, May.
    2. Tal Baron & Robert I Grossman & Steven B Abramson & Martin V Pusic & Rafael Rivera & Marc M Triola & Itai Yanai, 2020. "Signatures of medical student applicants and academic success," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-13, January.
    3. Amir Khorram-Manesh & Maxim A. Dulebenets & Krzysztof Goniewicz, 2021. "Implementing Public Health Strategies—The Need for Educational Initiatives: A Systematic Review," IJERPH, MDPI, vol. 18(11), pages 1-21, May.
    4. Kefan Xie & Benbu Liang & Maxim A. Dulebenets & Yanlan Mei, 2020. "The Impact of Risk Perception on Social Distancing during the COVID-19 Pandemic in China," IJERPH, MDPI, vol. 17(17), pages 1-17, August.
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    1. Diana Lungeanu & Alina Petrica & Raluca Lupusoru & Adina Maria Marza & Ovidiu Alexandru Mederle & Bogdan Timar, 2022. "Beyond the Digital Competencies of Medical Students: Concerns over Integrating Data Science Basics into the Medical Curriculum," IJERPH, MDPI, vol. 19(23), pages 1-11, November.

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