TY - JOUR
T1 - Best practices in the real-world data life cycle
AU - Zhang, Joe
AU - Symons, Joshua
AU - Agapow, Paul
AU - Teo, James T.
AU - Paxton, Claire A.
AU - Abdi, Jordan
AU - Mattie, Heather
AU - Davie, Charlie
AU - Torres, Aracelis Z.
AU - Folarin, Amos
AU - Sood, Harpreet
AU - Celi, Leo A.
AU - Halamka, John
AU - Eapen, Sara
AU - Budhdeo, Sanjay
N1 - Publisher Copyright:
© 2022 Zhang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2022/1
Y1 - 2022/1
N2 - AU With: increasing Pleaseconfirmthatallheadinglevelsarerepresentedcorrectly 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. HoweverAU , use: PleasecheckandconfirmthattheeditstothesentenceHowever cases for RWD continue to grow in number, moving beyond ; thearticleemphasizestheimportance 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.
AB - AU With: increasing Pleaseconfirmthatallheadinglevelsarerepresentedcorrectly 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. HoweverAU , use: PleasecheckandconfirmthattheeditstothesentenceHowever cases for RWD continue to grow in number, moving beyond ; thearticleemphasizestheimportance 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.
UR - http://www.scopus.com/inward/record.url?scp=85133550355&partnerID=8YFLogxK
U2 - 10.1371/journal.pdig.0000003
DO - 10.1371/journal.pdig.0000003
M3 - Review article
AN - SCOPUS:85133550355
SN - 2767-3170
VL - 1
JO - PLOS digital health
JF - PLOS digital health
IS - 1 January
M1 - e0000003
ER -