Best practices in the real-world data life cycle.
With increasing digitization of healthcare, real-world data (RWD) are available in greater quantity and scope than ever before. Since the 2016 United States 21st Century Cures Act, innovations in the RWD life cycle have taken tremendous strides forward, largely driven by demand for regulatory-grade...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2022-01-01
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| Series: | PLOS Digital Health |
| Online Access: | https://journals.plos.org/digitalhealth/article/file?id=10.1371/journal.pdig.0000003&type=printable |
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| author | Joe Zhang Joshua Symons Paul Agapow James T Teo Claire A Paxton Jordan Abdi Heather Mattie Charlie Davie Aracelis Z Torres Amos Folarin Harpreet Sood Leo A Celi John Halamka Sara Eapen Sanjay Budhdeo |
| author_facet | Joe Zhang Joshua Symons Paul Agapow James T Teo Claire A Paxton Jordan Abdi Heather Mattie Charlie Davie Aracelis Z Torres Amos Folarin Harpreet Sood Leo A Celi John Halamka Sara Eapen Sanjay Budhdeo |
| author_sort | Joe Zhang |
| collection | DOAJ |
| description | With increasing digitization of healthcare, real-world data (RWD) are available in greater quantity and scope than ever before. Since the 2016 United States 21st Century Cures Act, innovations in the RWD life cycle have taken tremendous strides forward, largely driven by demand for regulatory-grade real-world evidence from the biopharmaceutical sector. However, use cases for RWD continue to grow in number, moving beyond drug development, to population health and direct clinical applications pertinent to payors, providers, and health systems. Effective RWD utilization requires disparate data sources to be turned into high-quality datasets. To harness the potential of RWD for emerging use cases, providers and organizations must accelerate life cycle improvements that support this process. We build on examples obtained from the academic literature and author experience of data curation practices across a diverse range of sectors to describe a standardized RWD life cycle containing key steps in production of useful data for analysis and insights. We delineate best practices that will add value to current data pipelines. Seven themes are highlighted that ensure sustainability and scalability for RWD life cycles: data standards adherence, tailored quality assurance, data entry incentivization, deploying natural language processing, data platform solutions, RWD governance, and ensuring equity and representation in data. |
| format | Article |
| id | doaj-art-ea045cecf4374435890e00d60e2b7788 |
| institution | OA Journals |
| issn | 2767-3170 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLOS Digital Health |
| spelling | doaj-art-ea045cecf4374435890e00d60e2b77882025-08-20T02:23:18ZengPublic Library of Science (PLoS)PLOS Digital Health2767-31702022-01-0111e000000310.1371/journal.pdig.0000003Best practices in the real-world data life cycle.Joe ZhangJoshua SymonsPaul AgapowJames T TeoClaire A PaxtonJordan AbdiHeather MattieCharlie DavieAracelis Z TorresAmos FolarinHarpreet SoodLeo A CeliJohn HalamkaSara EapenSanjay BudhdeoWith increasing digitization of healthcare, real-world data (RWD) are available in greater quantity and scope than ever before. Since the 2016 United States 21st Century Cures Act, innovations in the RWD life cycle have taken tremendous strides forward, largely driven by demand for regulatory-grade real-world evidence from the biopharmaceutical sector. However, use cases for RWD continue to grow in number, moving beyond drug development, to population health and direct clinical applications pertinent to payors, providers, and health systems. Effective RWD utilization requires disparate data sources to be turned into high-quality datasets. To harness the potential of RWD for emerging use cases, providers and organizations must accelerate life cycle improvements that support this process. We build on examples obtained from the academic literature and author experience of data curation practices across a diverse range of sectors to describe a standardized RWD life cycle containing key steps in production of useful data for analysis and insights. We delineate best practices that will add value to current data pipelines. Seven themes are highlighted that ensure sustainability and scalability for RWD life cycles: data standards adherence, tailored quality assurance, data entry incentivization, deploying natural language processing, data platform solutions, RWD governance, and ensuring equity and representation in data.https://journals.plos.org/digitalhealth/article/file?id=10.1371/journal.pdig.0000003&type=printable |
| spellingShingle | Joe Zhang Joshua Symons Paul Agapow James T Teo Claire A Paxton Jordan Abdi Heather Mattie Charlie Davie Aracelis Z Torres Amos Folarin Harpreet Sood Leo A Celi John Halamka Sara Eapen Sanjay Budhdeo Best practices in the real-world data life cycle. PLOS Digital Health |
| title | Best practices in the real-world data life cycle. |
| title_full | Best practices in the real-world data life cycle. |
| title_fullStr | Best practices in the real-world data life cycle. |
| title_full_unstemmed | Best practices in the real-world data life cycle. |
| title_short | Best practices in the real-world data life cycle. |
| title_sort | best practices in the real world data life cycle |
| url | https://journals.plos.org/digitalhealth/article/file?id=10.1371/journal.pdig.0000003&type=printable |
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