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Review
. 2022 Jan 18;1(1):e0000003.
doi: 10.1371/journal.pdig.0000003. eCollection 2022 Jan.

Best practices in the real-world data life cycle

Joe Zhang  1   2 , Joshua Symons  3 , Paul Agapow  4 , James T Teo  5   6 , Claire A Paxton  7 , Jordan Abdi  8 , Heather Mattie  9 , Charlie Davie  10   11   12 , Aracelis Z Torres  7 , Amos Folarin  13 , Harpreet Sood  14 , Leo A Celi  9   15 , John Halamka  16 , Sara Eapen  17 , Sanjay Budhdeo  18   19   20
Affiliations
Review

Best practices in the real-world data life cycle

Joe Zhang et al. PLOS Digit Health. .

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.

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Conflict of interest statement

In accordance with the journal’s policy, the authors of this manuscript have the following competing interests to declare: CD has received an honorarium for work with Merck. JTT has previously received research grant support from Innovate UK, NHSX, Office of Life Sciences, Bristol-Meyers-Squibb and Pfizer; has received honorarium from Bayer, Bristol-Meyers-Squibb and Goldman Sachs; holds stock in Amazon, Alphabet, Nvidia, Glaxo Smith Kline; and receives royalties from Wiley-Blackwell Publishing. SB holds equity in Owkin. SB was formerly employed by Boston Consulting Group and Owkin. JZ receives funding from the Wellcome Trust (203928/Z/16/Z) and acknowledges support from the National Institute for Health Research (NIHR) Biomedical Research Centre based at Imperial College NHS Trust and Imperial College London. SB receives funding from the Wellcome Trust (566701). LAC receives funding from the National Institute of Health (NIBIB R01 EB017205). Listed bodies had no role in funding this study, and views expressed are authors’ own.

Figures

Fig 1
Fig 1. The illustrated life cycle is a series of necessary or recommended steps that produce RWD usable for analysis, from raw data generated by clinical encounters or operational workflows.
Insights gained from data use can be returned to the life cycle, enriching future generation of clinical data. RWD, real-world data.
Fig 2
Fig 2. An example data platform incorporating multiple best practices discussed in this article including natural language processing, generation of data warehouses and data marts, and ADM.
ADM, augmented data management; COVID-19, Coronavirus Disease 2019; EHR, electronic health record.
See this image and copyright information in PMC

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