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Harnessing Real-World Data for Regulatory Use and Applying Innovative Applications
A vast quantity of real-world data (RWD) are available to healthcare researchers. Such data come from diverse sources such as electronic health records, insurance claims and billing activity, product and disease registries, medical devices used in the home, and applications on mobile devices. The an...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Dove
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7383026/ https://www.ncbi.nlm.nih.gov/pubmed/32801731 http://dx.doi.org/10.2147/JMDH.S262776 |
Sumario: | A vast quantity of real-world data (RWD) are available to healthcare researchers. Such data come from diverse sources such as electronic health records, insurance claims and billing activity, product and disease registries, medical devices used in the home, and applications on mobile devices. The analysis of RWD produces real-world evidence (RWE), which is clinical evidence that provides information about usage and potential benefits or risks of a drug. This review defines and explains RWD, and it also details how regulatory authorities are using RWD and RWE. The main challenges in harnessing RWD include collating and analyzing numerous disparate types or categories of available information including both structured (eg, field entries) and unstructured (eg, doctor notes, discharge summaries, social media posts) data. Although the use of artificial intelligence to capture, amalgamate, standardize, and analyze RWD is still evolving, it has the potential to support the increased use of RWE to improve global health and healthcare. |
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