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      Real-world Evidence versus Randomized Controlled Trial: Clinical Research Based on Electronic Medical Records

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          Abstract

          Real-world evidence (RWE) and randomized control trial (RCT) data are considered mutually complementary. However, compared with RCT, the outcomes of RWE continue to be assigned lower credibility. It must be emphasized that RWE research is a real-world practice that does not need to be executed as RCT research for it to be reliable. The advantages and disadvantages of RWE must be discerned clearly, and then the proper protocol can be planned from the beginning of the research to secure as many samples as possible. Attention must be paid to privacy protection. Moreover, bias can be reduced meaningfully by reducing the number of dropouts through detailed and meticulous data quality management. RCT research, characterized as having the highest reliability, and RWE research, which reflects the actual clinical aspects, can have a mutually supplementary relationship. Indeed, once this is proven, the two could comprise the most powerful evidence-based research method in medicine.

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          Most cited references22

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          Artificial Intelligence and Machine Learning in Radiology: Opportunities, Challenges, Pitfalls, and Criteria for Success

          Worldwide interest in artificial intelligence (AI) applications, including imaging, is high and growing rapidly, fueled by availability of large datasets ("big data"), substantial advances in computing power, and new deep-learning algorithms. Apart from developing new AI methods per se, there are many opportunities and challenges for the imaging community, including the development of a common nomenclature, better ways to share image data, and standards for validating AI program use across different imaging platforms and patient populations. AI surveillance programs may help radiologists prioritize work lists by identifying suspicious or positive cases for early review. AI programs can be used to extract "radiomic" information from images not discernible by visual inspection, potentially increasing the diagnostic and prognostic value derived from image datasets. Predictions have been made that suggest AI will put radiologists out of business. This issue has been overstated, and it is much more likely that radiologists will beneficially incorporate AI methods into their practices. Current limitations in availability of technical expertise and even computing power will be resolved over time and can also be addressed by remote access solutions. Success for AI in imaging will be measured by value created: increased diagnostic certainty, faster turnaround, better outcomes for patients, and better quality of work life for radiologists. AI offers a new and promising set of methods for analyzing image data. Radiologists will explore these new pathways and are likely to play a leading role in medical applications of AI.
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            Combining electronic healthcare databases in Europe to allow for large-scale drug safety monitoring: the EU-ADR Project.

            In this proof-of-concept paper we describe the framework, process, and preliminary results of combining data from European electronic healthcare record (EHR) databases for large-scale monitoring of drug safety. Aggregated demographic, clinical, and prescription data from eight databases in four countries (Denmark, Italy, Netherlands, the UK) were pooled using a distributed network approach by generation of common input data followed by local aggregation through custom-built software, Jerboa(©). Comparison of incidence rates of upper gastrointestinal bleeding (UGIB) and nonsteroidal anti-inflammatory drug (NSAID) utilization patterns were used to evaluate data harmonization and quality across databases. The known association of NSAIDs and UGIB was employed to demonstrate sensitivity of the system by comparing incidence rate ratios (IRRs) of UGIB during NSAID use to UGIB during all other person-time. The study population for this analysis comprised 19,647,445 individuals corresponding to 59,929,690 person-years of follow-up. 39,967 incident cases of UGIB were identified during the study period. Crude incidence rates varied between 38.8 and 109.5/100,000 person-years, depending on country and type of database, while age-standardized rates ranged from 25.1 to 65.4/100,000 person-years. NSAID use patterns were similar for databases within the same country but heterogeneous among different countries. A statistically significant age- and gender-adjusted association between use of any NSAID and increased risk for UGIB was confirmed in all databases, IRR from 2.0 (95%CI:1.7-2.2) to 4.3 (95%CI: 4.1-4.5). Combining data from EHR databases of different countries to identify drug-adverse event associations is feasible and can set the stage for changing and enlarging the scale for drug safety monitoring. Copyright © 2010 John Wiley & Sons, Ltd.
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              Integrating real-life studies in the global therapeutic research framework.

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                Author and article information

                Journal
                J Korean Med Sci
                J. Korean Med. Sci
                JKMS
                Journal of Korean Medical Science
                The Korean Academy of Medical Sciences
                1011-8934
                1598-6357
                26 June 2018
                20 August 2018
                : 33
                : 34
                : e213
                Affiliations
                [1 ]Department of Medical Informatics, The Catholic University of Korea College of Medicine, Seoul, Korea.
                [2 ]Division of Endocrinology and Metabolism, Department of Internal Medicine, Seoul St. Mary's Hospital, The Catholic University of Korea College of Medicine, Seoul, Korea.
                [3 ]Division of Biomedical Informatics, Systems Biomedical Informatics Research Center, Seoul National University College of Medicine, Seoul, Korea.
                Author notes
                Address for Correspondence: Ju Han Kim, MD, PhD. Division of Biomedical Informatics, Systems Biomedical Informatics Research Center, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul 03080, Korea. juhan@ 123456snu.ac.kr
                Author information
                https://orcid.org/0000-0002-7002-7300
                https://orcid.org/0000-0003-0651-6481
                https://orcid.org/0000-0003-1522-9038
                Article
                10.3346/jkms.2018.33.e213
                6097073
                31044574
                5c368c33-f388-45a1-b94b-b8e786411e87
                © 2018 The Korean Academy of Medical Sciences.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( https://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 17 April 2018
                : 24 May 2018
                Categories
                Opinion
                Medical Informatics

                Medicine
                real-world evidence,randomized control trial,real-world data
                Medicine
                real-world evidence, randomized control trial, real-world data

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