Transforming modern healthcare: from real-world data to trusted real world evidence

Zin, Che Suraya (2027) Transforming modern healthcare: from real-world data to trusted real world evidence. Journal of Pharmacy, 6 (2). pp. 145-151. E-ISSN 2773-5664

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Abstract

Healthcare is undergoing a profound transformation driven by the digitalisation of healthcare systems, the availability of real-world data (RWD), and advances in artificial intelligence (AI). While randomized controlled trials (RCTs) remain the cornerstone of evidence-based medicine, they are not designed to answer every question encountered in routine clinical practice, particularly among diverse patient populations with multimorbidity, polypharmacy, and complex healthcare needs. Consequently, real-world evidence (RWE), generated through the rigorous analysis of healthcare data, has emerged as an essential complement to evidence from RCTs. This editorial discusses the evolving role of RWE in supporting clinical practice, regulatory decision-making, health technology assessment, and healthcare policy. The availability of electronic health records, disease registries, administrative databases, pharmacy records, and digital health resources is creating unprecedented opportunities to understand treatment effectiveness, medication safety, healthcare utilisation, and patient outcomes in routine care. It further examines the transformative role of AI in accelerating data curation, extraction, and analysis, while emphasising that technological advances cannot substitute for rigorous scientific methodology. Robust pharmacoepidemiological principles, including rigorous study design, appropriate analytical methods, transparency, and reproducibility, remain fundamental to generating trustworthy evidence from complex healthcare data. Finally, the editorial discusses governance, multidisciplinary collaboration, workforce development, and responsible innovation in building sustainable RWE ecosystems. As healthcare continues to evolve, the future of evidence generation will depend not on the volume of data or the sophistication of computational tools alone, but on the ability to transform RWD into credible evidence that improves patient outcomes, informs healthcare decisions, and strengthens public trust.

Item Type:	Article (Journal)
Uncontrolled Keywords:	randomized controlled trials, real-world data, artificial intelligence, real-world evidence
Subjects:	R Medicine > RS Pharmacy and materia medica
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Pharmacy > Department of Pharmacy Practice
Depositing User:	Prof Dr Che Suraya Mohd Zin
Date Deposited:	15 Sep 2026 12:49
Last Update:	15 Sep 2026 12:49
Queue Number:	2026-09-Q5086
URI:	http://irep.iium.edu.my/id/eprint/131242
Indexed In:	Google Scholar, MyCite

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