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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Main Authors: 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
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2022-01-01
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.
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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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