Revisiting real-world data studies: Progress, value, and challenges

<p>This review highlights the indispensable role of real-world data studies (RWS) in complementing randomized controlled trials by generating real-world evidence (RWE) that reflects diverse patient populations and clinical settings. It explores the origins and regulatory frameworks of RWS, the evolution of real-world data sources, and their expanding applications in evaluating post-marketing medical products, optimizing pre-marketing medical product development, measuring disease burden, assessing medical professional competence, evaluating healthcare service quality, and informing clinical guidelines and public health policies. The contributions of RWE to personalized medicine, healthcare resource management, and regulatory decisions underscore its significance in evidence-based practice. Despite its potential, RWS faces challenges such as data quality, purpose-driven data sharing, ethical standards, RWE validity and transparency, RWE translation, and multidisciplinary expertise, and this review proposes some strategies to advance these fields. By addressing these challenges, RWS can enhance their impact on healthcare innovation and translate into better patient outcomes globally.</p>

Authors

Publication Details

Journal
The Innovation Medicine
Published
2025-01-01
DOI
https://doi.org/10.59717/j.xinn-med.2025.100143
Citations
3
Primary Topic
Machine Learning in Healthcare
Type
article
Field-Weighted Citation Impact
7.44
Controls
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article

Revisiting real-world data studies: Progress, value, and challenges

Hou-Yu Zhao, Huakang Tu, Zhirong Yang, Meng Zhang et al.
3 citations
The Innovation Medicine
Machine Learning in Healthcare
7.44
article

Revisiting real-world data studies: Progress, value, and challenges

Hou-Yu Zhao, Huakang Tu, Zhirong Yang, Meng Zhang, Feng Sun, Hongping Xiang
article en
3 citations

Abstract

<p>This review highlights the indispensable role of real-world data studies (RWS) in complementing randomized controlled trials by generating real-world evidence (RWE) that reflects diverse patient populations and clinical settings. It explores the origins and regulatory frameworks of RWS, the evolution of real-world data sources, and their expanding applications in evaluating post-marketing medical products, optimizing pre-marketing medical product development, measuring disease burden, assessing medical professional competence, evaluating healthcare service quality, and informing clinical guidelines and public health policies. The contributions of RWE to personalized medicine, healthcare resource management, and regulatory decisions underscore its significance in evidence-based practice. Despite its potential, RWS faces challenges such as data quality, purpose-driven data sharing, ethical standards, RWE validity and transparency, RWE translation, and multidisciplinary expertise, and this review proposes some strategies to advance these fields. By addressing these challenges, RWS can enhance their impact on healthcare innovation and translate into better patient outcomes globally.</p>

The Innovation MedicineVol. 3(3)
Openalex Percentile: Top 3%
Machine Learning in Healthcare
7.44
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