Zhang, Joe;
Symons, Joshua;
Agapow, Paul;
Teo, James T;
Paxton, Claire A;
Abdi, Jordan;
Mattie, Heather;
... Budhdeo, Sanjay; + view all
(2022)
Best practices in the real-world data life cycle.
PLOS Digital Health
, 1
(1)
, Article e0000003. 10.1371/journal.pdig.0000003.
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Abstract
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.
| Type: | Article |
|---|---|
| Title: | Best practices in the real-world data life cycle |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1371/journal.pdig.0000003 |
| Publisher version: | https://doi.org/10.1371/journal.pdig.0000003 |
| Language: | English |
| Additional information: | Copyright © 2022 Zhang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
| Keywords: | Life cycles, Natural language processing, Electronic medical records, Medical risk factors, Vendors, COVID 19, Data management, Regulations |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Clinical and Movement Neurosciences |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10160374 |
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