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Value is equal to outcome/costs: how to apply to laboratory medicine

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Veröffentlicht/Copyright: 10. September 2025

Abstract

The concept of value, defined as health outcomes achieved per monetary unit spent, has profoundly reshaped modern healthcare delivery. While Value-Based Healthcare models have permeated many clinical disciplines, laboratory medicine has been slow to integrate this paradigm shift. In this opinion paper, we argue for a strategic repositioning of clinical laboratories as core enablers of value in healthcare systems. Laboratory diagnostics, long considered ancillary, should be reframed as pivotal tools that support outcome-based, cost-effective decision-making. We explore how laboratory parameters contribute to clinical value through predictive accuracy, diagnostic specificity, and operational appropriateness – factors that directly influence patient outcomes and resource allocation. Examples such as vitamin D testing, albumin as a biomarker of biological age, and NT-proBNP in heart failure demonstrate the potential and pitfalls of volume-driven laboratory utilization. Beyond technical excellence, we emphasize the importance of interpretive collaboration, health literacy, and ethical stewardship of diagnostic resources. Structural challenges, including commoditization, delocalization via point-of-care testing, and the limited use of patient-reported outcomes in laboratory settings, are critically examined. Finally, we highlight emerging policy frameworks across Europe that align reimbursement models with measurable outcomes, advocating for the integration of laboratories in clinical governance and value-based procurement. In this renewed perspective, laboratories are not merely data providers but agents of personalized, sustainable, and patient-centered care.


Corresponding author: Rossella Tomaiuolo, IRCCS Galeazzi Sant’Ambrogio, Milan, Italy, E-mail:

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

  3. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  4. Use of Large Language Models, AI and Machine Learning Tools: To improve language, we used Grammarly, a writing enhancement tool that provides grammar and style suggestions. No generative AI or large language model (LLM) was used for content generation or interpretation.

  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: 2025-06-06
Accepted: 2025-09-01
Published Online: 2025-09-10
Published in Print: 2026-01-29

© 2025 Walter de Gruyter GmbH, Berlin/Boston

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