Imperial College London
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MR JOSHUA SYMONS

Faculty of Medicine, Institute of Global Health Innovation

Honorary Research Fellow
 
 
 
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Contact

 

j.symons Website

 
 
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Location

 

Queen Elizabeth and Queen Mary HospitalSt Mary's Campus

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Summary

 

Publications

Citation

BibTex format

@article{Zhang:2022:10.1371/journal.pdig.0000003,
author = {Zhang, J and Symons, J and Agapow, P and Teo, JT and Paxton, CA and Abdi, J and Mattie, H and Davie, C and Torres, AZ and Folarin, A and Sood, H and Celi, LA and Halamka, J and Eapen, S and Budhdeo, S},
doi = {10.1371/journal.pdig.0000003},
journal = {PLOS Digit Health},
title = {Best practices in the real-world data life cycle.},
url = {http://dx.doi.org/10.1371/journal.pdig.0000003},
volume = {1},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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.
AU  - Zhang,J
AU  - Symons,J
AU  - Agapow,P
AU  - Teo,JT
AU  - Paxton,CA
AU  - Abdi,J
AU  - Mattie,H
AU  - Davie,C
AU  - Torres,AZ
AU  - Folarin,A
AU  - Sood,H
AU  - Celi,LA
AU  - Halamka,J
AU  - Eapen,S
AU  - Budhdeo,S
DO  - 10.1371/journal.pdig.0000003
PY  - 2022///
TI  - Best practices in the real-world data life cycle.
T2  - PLOS Digit Health
UR  - http://dx.doi.org/10.1371/journal.pdig.0000003
UR  - https://www.ncbi.nlm.nih.gov/pubmed/36812509
VL  - 1
ER  -
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