NDLI: Digital Health and Big Data Analytics: Implications of Real-World Evidence for Clinicians and Policymakers

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Digital Health and Big Data Analytics: Implications of Real-World Evidence for Clinicians and Policymakers

Content Provider	MDPI
Author	Teresa, Magalhães Dinis-Oliveira, Ricardo Jorge Taveira-Gomes, Tiago
Copyright Year	2022
Description	Real world data (RWD) and real-world evidence (RWE) plays an increasingly important role in clinical research since scientific knowledge is obtained during routine clinical large-scale practice and not experimentally as occurs in the highly controlled traditional clinical trials. Particularly, the electronic health records (EHRs) are a relevant source of data. Nevertheless, there are also significant challenges in the correct use and interpretation of EHRs data, such as bias, heterogeneity of the population, and missing or non-standardized data formats. Despite the RWD and RWE recognized difficulties, these are easily outweighed by the benefits of ensuring the efficacy, safety, and cost-effectiveness in complement to the gold standards of the randomized controlled trial (RCT), namely by providing a complete picture regarding factors and variables that can guide robust clinical decisions. Their relevance can be even further evident as healthcare units develop more accurate EHRs always in the respect for the privacy of patient data. This editorial is an overview of the RWD and RWE major aspects of the state of the art and supports the Special Issue on “Digital Health and Big Data Analytics: Implications of Real-World Evidence for Clinicians and Policymakers” aimed to explore all the potential and the utility of RWD and RWE in offering insights on diseases in a broad spectrum.
Starting Page	8364
e-ISSN	16604601
DOI	10.3390/ijerph19148364
Journal	International Journal of Environmental Research and Public Health
Issue Number	14
Volume Number	19
Language	English
Publisher	MDPI
Publisher Date	2022-07-08
Access Restriction	Open
Subject Keyword	International Journal of Environmental Research and Public Health Medical Informatics Real-world Evidence Real-world Data Big Data Digital Health Healthcare
Content Type	Text

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