Startseite Advancing diagnostic stewardship in contemporary medical practice
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Advancing diagnostic stewardship in contemporary medical practice

  • Isaac K.S. Ng EMAIL logo , Zhen Jonathan Liang , Wilson G.W. Goh , Adrian C.L. Kee und Tow Keang Lim
Veröffentlicht/Copyright: 12. August 2025
Diagnosis
Aus der Zeitschrift Diagnosis

Abstract

Over-testing, or the inappropriate use of diagnostic tests, is a highly prevalent phenomenon in modern medical practice, driven by a variety of physician, patient/disease, practice environment and system-related factors. Most of the time, physician proclivities to over-investigate fundamentally reflects a flawed clinical reasoning process under real-world practice conditions. Over the years, various educational campaigns and interventions such as the Choosing Wisely programme have been attempted to varying success, without clear evidence of a sustained reduction in the excessive/inappropriate use of diagnostic tests in clinical practice. Therefore, in this article, we hope to provide fresh perspectives on the concept of “diagnostic stewardship”, which has hitherto been largely confined to the realm of infectious diseases to govern responsible clinical use of antimicrobials, by explaining its role in the delivery of patient-centered and sustainable healthcare. We further explore the underlying contributors to over-testing and how it relates to flaws in clinical reasoning in real-world settings. Lastly, we discuss multi-level educational and practice-based interventions that may help to curb the tide of profligate diagnostic testing behaviors.


Corresponding author: Dr. Isaac K. S. Ng, MBBS, MRCP, Division of Rheumatology, Department of Medicine, National University Hospital Singapore, Singapore 119228, E-mail:

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

  3. Author contributions: IKSN wrote the manuscript. IKSN and TKL conceived the study idea. ZJL, WGWG, ACKL, TKL critically reviewed and edited the manuscript.

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interest: The authors state no conflict of interest.

  6. Research funding: None declared.

  7. Data availability: Not applicable.

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Received: 2025-05-12
Accepted: 2025-07-16
Published Online: 2025-08-12

© 2025 Walter de Gruyter GmbH, Berlin/Boston

Heruntergeladen am 29.10.2025 von https://www.degruyterbrill.com/document/doi/10.1515/dx-2025-0071/pdf
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