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      Digital Education in Health Professions: The Need for Overarching Evidence Synthesis

      research-article
      , MD, PhD, FRCPE, FFPH 1 , , , MD, PhD 2 , , MD, PhD 3 , 4 , , PhD 5 , , PhD 6 , , MD, PhD 7 , 8 , 9 , , MBBS, FRCP 10 , 11 , , MBBS, MD 12 , , MPH, MSc 13 , Digital Health Education Collaboration
      (Reviewer), (Reviewer), (Reviewer), (Reviewer)
      Journal of Medical Internet Research
      JMIR Publications
      methods, education, medical, systematic reviews, evidence-based, education, distance, education, professional

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          Abstract

          Synthesizing evidence from randomized controlled trials of digital health education poses some challenges. These include a lack of clear categorization of digital health education in the literature; constantly evolving concepts, pedagogies, or theories; and a multitude of methods, features, technologies, or delivery settings. The Digital Health Education Collaboration was established to evaluate the evidence on digital education in health professions; inform policymakers, educators, and students; and ultimately, change the way in which these professionals learn and are taught. The aim of this paper is to present the overarching methodology that we use to synthesize evidence across our digital health education reviews and to discuss challenges related to the process. For our research, we followed Cochrane recommendations for the conduct of systematic reviews; all reviews are reported according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidance. This included assembling experts in various digital health education fields; identifying gaps in the evidence base; formulating focused research questions, aims, and outcome measures; choosing appropriate search terms and databases; defining inclusion and exclusion criteria; running the searches jointly with librarians and information specialists; managing abstracts; retrieving full-text versions of papers; extracting and storing large datasets, critically appraising the quality of studies; analyzing data; discussing findings; drawing meaningful conclusions; and drafting research papers. The approach used for synthesizing evidence from digital health education trials is commonly regarded as the most rigorous benchmark for conducting systematic reviews. Although we acknowledge the presence of certain biases ingrained in the process, we have clearly highlighted and minimized those biases by strictly adhering to scientific rigor, methodological integrity, and standard operating procedures. This paper will be a valuable asset for researchers and methodologists undertaking systematic reviews in digital health education.

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          Most cited references29

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          Technology-enhanced simulation for health professions education: a systematic review and meta-analysis.

          Although technology-enhanced simulation has widespread appeal, its effectiveness remains uncertain. A comprehensive synthesis of evidence may inform the use of simulation in health professions education. To summarize the outcomes of technology-enhanced simulation training for health professions learners in comparison with no intervention. Systematic search of MEDLINE, EMBASE, CINAHL, ERIC, PsychINFO, Scopus, key journals, and previous review bibliographies through May 2011. Original research in any language evaluating simulation compared with no intervention for training practicing and student physicians, nurses, dentists, and other health care professionals. Reviewers working in duplicate evaluated quality and abstracted information on learners, instructional design (curricular integration, distributing training over multiple days, feedback, mastery learning, and repetitive practice), and outcomes. We coded skills (performance in a test setting) separately for time, process, and product measures, and similarly classified patient care behaviors. From a pool of 10,903 articles, we identified 609 eligible studies enrolling 35,226 trainees. Of these, 137 were randomized studies, 67 were nonrandomized studies with 2 or more groups, and 405 used a single-group pretest-posttest design. We pooled effect sizes using random effects. Heterogeneity was large (I(2)>50%) in all main analyses. In comparison with no intervention, pooled effect sizes were 1.20 (95% CI, 1.04-1.35) for knowledge outcomes (n = 118 studies), 1.14 (95% CI, 1.03-1.25) for time skills (n = 210), 1.09 (95% CI, 1.03-1.16) for process skills (n = 426), 1.18 (95% CI, 0.98-1.37) for product skills (n = 54), 0.79 (95% CI, 0.47-1.10) for time behaviors (n = 20), 0.81 (95% CI, 0.66-0.96) for other behaviors (n = 50), and 0.50 (95% CI, 0.34-0.66) for direct effects on patients (n = 32). Subgroup analyses revealed no consistent statistically significant interactions between simulation training and instructional design features or study quality. In comparison with no intervention, technology-enhanced simulation training in health professions education is consistently associated with large effects for outcomes of knowledge, skills, and behaviors and moderate effects for patient-related outcomes.
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            The assessment of clinical skills/competence/performance.

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              Presenting Results and‘Summary of Findings’ Tables

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                Author and article information

                Contributors
                Journal
                J Med Internet Res
                J. Med. Internet Res
                JMIR
                Journal of Medical Internet Research
                JMIR Publications (Toronto, Canada )
                1439-4456
                1438-8871
                February 2019
                14 February 2019
                : 21
                : 2
                : e12913
                Affiliations
                [1 ] Centre for Population Health Sciences Lee Kong Chian School of Medicine Nanyang Technological University Singapore Singapore Singapore
                [2 ] Nanyang Institute of Technology in Health and Medicine Nanyang Technological University Singapore Singapore Singapore
                [3 ] Family Medicine and Primary Care Lee Kong Chian School of Medicine Nanyang Technological University Singapore Singapore Singapore
                [4 ] Department of Primary Care and Public Health School of Public Health Imperial College London London United Kingdom
                [5 ] Centre for Population Health Sciences Lee Kong Chian School of Medicine Nanyang Technological University Singapore Singapore
                [6 ] Bristol Medical School University of Bristol Bristol United Kingdom
                [7 ] Medical Education Research and Scholarship Unit Lee Kong Chian School of Medicine Nanyang Technological University Singapore Singapore
                [8 ] Department of Learning, Informatics, Management and Ethics Karolinska Institutet Stockholm Sweden
                [9 ] International Medical Simulation Centre Mohammed VI University of Health Sciences Casablanca Morocco
                [10 ] Harvard TH Chan School of Public Health Harvard University Boston, MA United States
                [11 ] Harvard Medical School Harvard University Boston, MA United States
                [12 ] Department of Primary Care & Public Health School of Public Health Imperial College London London United Kingdom
                [13 ] Health Workforce Department World Health Organization Geneva Switzerland
                Author notes
                Corresponding Author: Josip Car josip.car@ 123456ntu.edu.sg
                Author information
                http://orcid.org/0000-0001-8969-371X
                http://orcid.org/0000-0001-7784-5296
                http://orcid.org/0000-0001-8414-7664
                http://orcid.org/0000-0002-5153-3654
                http://orcid.org/0000-0003-1138-5682
                http://orcid.org/0000-0001-8999-6999
                http://orcid.org/0000-0002-1531-5983
                http://orcid.org/0000-0002-2357-9858
                http://orcid.org/0000-0002-2294-2547
                Article
                v21i2e12913
                10.2196/12913
                6393775
                30762583
                d4d2ef25-c0ad-4e2a-a5a0-24f4fbdee594
                ©Josip Car, Jan Carlstedt-Duke, Lorainne Tudor Car, Pawel Posadzki, Penny Whiting, Nabil Zary, Rifat Atun, Azeem Majeed, James Campbell, Digital Health Education Collaboration. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 14.02.2019.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/.as well as this copyright and license information must be included.

                History
                : 28 November 2018
                : 20 December 2018
                : 13 January 2019
                : 15 January 2019
                Categories
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                Medicine
                methods,education, medical,systematic reviews,evidence-based,education, distance,education, professional

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