Value is equal to outcome/costs: how to apply to laboratory medicine
-
Rossella Tomaiuolo
und
Giuseppe Banfi
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.
-
Research ethics: Not applicable.
-
Informed consent: Not applicable.
-
Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
-
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.
-
Conflict of interest: The authors state no conflict of interest.
-
Research funding: None declared.
-
Data availability: Not applicable.
References
1. Porter, ME. What is value in health care? N Engl J Med 2010;363:2477–81. https://doi.org/10.1056/nejmp1011024.Suche in Google Scholar
2. Pennestrì, F, Banfi, G. Value-based healthcare: the role of laboratory medicine. Clin Chem Lab Med 2019;57:798–801. https://doi.org/10.1515/cclm-2018-1245.Suche in Google Scholar PubMed
3. Plebani, M, Cadamuro, J, Vermeersch, P, Jovičić, S, Ozben, T, Trenti, T, et al.. A vision to the future: value-based laboratory medicine. Clin Chem Lab Med 2024;62:2373–87. https://doi.org/10.1515/cclm-2024-1022.Suche in Google Scholar PubMed
4. Tomaiuolo, R, Banfi, G. From volume to value: a watershed moment. Clin Chem Lab Med 2023;62:593–6. https://doi.org/10.1515/cclm-2023-0870.Suche in Google Scholar PubMed
5. Giustina, A, Bilezikian, JP, Adler, RA, Banfi, G, Bikle, DD, Binkley, NC, et al.. Consensus statement on vitamin D status assessment and supplementation: whys, whens, and hows. Endocr Rev 2024;45:625–54. https://doi.org/10.1210/endrev/bnae009.Suche in Google Scholar PubMed PubMed Central
6. Plebani, M, Coskun, A. Promoting value-based laboratory medicine: moving towards an innovative model of the clinical laboratory. Clin Chim Acta 2025;572:120269. https://doi.org/10.1016/j.cca.2025.120269.Suche in Google Scholar PubMed
7. Plebani, M. A value-based score for clinical laboratories: promoting the work of the new EFLM committee. Clin Chem Lab Med 2025. https://doi.org/10.1515/cclm-2025-0490.Suche in Google Scholar PubMed
8. Cabitza, F, Banfi, G. Machine learning in laboratory medicine: waiting for the flood? Clin Chem Lab Med 2018;56:516–24. https://doi.org/10.1515/cclm-2017-0287.Suche in Google Scholar PubMed
9. Briguglio, M, Bona, A, Crespi, G, Ravera, L, Giussani, C, Perazzo, P, et al.. Malnutrition and disability: a retrospective study on 2258 adult patients undergoing elective spine surgery. Medicina 2025;61:413. https://doi.org/10.3390/medicina61030413.Suche in Google Scholar PubMed PubMed Central
10. Foy, BH, Liu, S, Chen, Y, Zeng, Y, Brunner, J, Westover, MB, et al.. Haematological are a stable and patient-specific deep phenotype. Nature 2025;637:430–8.10.1038/s41586-024-08264-5Suche in Google Scholar PubMed PubMed Central
11. Porcari, S, Zandona, A, Malosio, ML, Di Ruscio, F, Scaldaferri, F, Cammarota, G, et al.. International consensus statement on microbiome testing in clinical practice. Lancet Gastroenterol Hepatol 2025;10:154–67. https://doi.org/10.1016/S2468-1253-24-00311-X.Suche in Google Scholar
12. Giustina, A, Gadelha, MR, Bronstein, MD, Barkan, A, Casanueva, FF, Chanson, P, et al.. Diagnosis and management of acromegaly: consolidated recommendations from the pituitary society. Pituitary 2024;27:7–22.10.1007/s11102-024-01397-wSuche in Google Scholar PubMed PubMed Central
13. European Association for the Study of the Liver (EASL). EASL clinical practice guidelines. J Hepatol 2017;67:145–72. https://doi.org/10.1016/j.jhep.2017.03.022.Suche in Google Scholar PubMed
14. Tsutsui, H, Yamamoto, K, Asano, R, Shiraishi, Y, Harada, M, Suzuki, T, et al.. Natriuretic peptides in the diagnosis and management of heart failure: expert consensus from the heart failure Association. J Card Fail 2023;29:787–804.10.1016/j.cardfail.2023.02.009Suche in Google Scholar PubMed
15. Finati, L, Patelli, F, Cattaneo, G, Rossi, M, Frattini, P, Sanguedolce, F, et al.. Prostate cancer outcomes in immunocompromised patients: a systematic review and meta-analysis. Eur Urol Oncol 2024;7:1535–42.10.1016/j.euo.2024.06.014Suche in Google Scholar PubMed
16. Zibetti, M, Di Resta, C, Banfi, G, Tomaiuolo, R. Value-based health care implementation: the case study of mTBI biomarkers. J Pers Med 2024;14:634. https://doi.org/10.3390/jpm14060634.Suche in Google Scholar PubMed PubMed Central
17. Thierriault, J, Tissot, C, Morin, A, Ducharme, S, Chouinard, PA, Pascoal, TA, et al.. Biomarkers of aging in Alzheimer’s disease: insights from a systematic review. Nat Aging 2024;11:1529–37.Suche in Google Scholar
18. Sakamoto, D, Sotomi, Y, Matsuoka, Y, Nakatani, D, Okada, K, Sunaga, A, et al.. Prognostic utility and cutoff differences in NT-proBNP levels across subgroups in heart failure with preserved ejection fraction: insights from the PURSUIT-HFpEF registry. J Card Fail 2025;31:771–80. https://doi.org/10.1016/j.cardfail.2024.10.440.Suche in Google Scholar PubMed
19. El Sayed, NA, Aleppo, G, Aroda, VR, Bannuru, RR, Brown, FM, Cherrington, A, et al.. Standards of medical care in diabetes – 2024. Diabetes Care 2024;47:S179–218. https://doi.org/10.2337/dc24-S010.Suche in Google Scholar PubMed PubMed Central
20. Briguglio, M, Bona, A, Ravera, L, Crespi, G, Gianola, S, Banfi, G, et al.. Preoperative hemoglobin as predictor of spinal surgery outcomes: a frontiers study. Front Surg 2022;9:850342.10.3389/fsurg.2022.850342Suche in Google Scholar PubMed PubMed Central
21. Wuestenfeld, JC, Kastner, T, Hesse, J, Fesseler, L, Frohberg, F, Rossbach, C, et al.. Differences in troponin I and troponin T release in high-performance athletes outside of competition. Int J Mol Sci 2024;25:1062. https://doi.org/10.3390/ijms25021062.Suche in Google Scholar PubMed PubMed Central
22. Shah, ASV, Keene, SJ, Pennells, L, Kaptoge, S, Kimenai, DM, Walker, M, et al.. Cardiac troponins and cardiovascular disease risk prediction: an individual-participant-data meta-analysis. J Am Coll Cardiol 2025;85:1471–84. https://doi.org/10.1016/j.jacc.2025.02.016.Suche in Google Scholar PubMed
23. Pennestrì, F, Banfi, G. Health literacy: a new challenge for laboratory medicine. Clin Chem Lab Med 2025;63:1051–9. https://doi.org/10.1515/cclm-2024-1158.Suche in Google Scholar PubMed
24. Pennestrì, F, Tomaiuolo, R, Banfi, G. Blood overtesting: ethical issues and mitigation. Clin Chem Lab Med 2024;62:1283–7.10.1515/cclm-2023-1227Suche in Google Scholar PubMed
25. NHS Supply Chain. Value-based procurement programme; 2023. https://www.supplychain.nhs.uk/programmes/value-based-procurement [Accessed 6 July 2025].Suche in Google Scholar
26. Janssen, C, Ganter, M, Erber, J, Heidinger, O, Bäumer, T, Gessner, C, et al.. Implementation and effects of coordinated ambulatory care in southern Germany: a mixed-methods study. Front Med 2022;9:940886.Suche in Google Scholar
27. Hennrich, P, Bölter, R, Wensing, M. Implementing coordinated ambulatory cardiology care in southern Germany: a mixed-methods study. BMC Health Serv Res 2019;19:976. https://doi.org/10.1186/s12913-019-4832-4.Suche in Google Scholar PubMed PubMed Central
28. Black, N. Patient-reported outcome measures could help transform healthcare. BMJ 2013;346:f167. https://doi.org/10.1136/bmj.f167.Suche in Google Scholar PubMed
29. International Consortium for Health Outcomes Measurement (ICHOM). What matters Most: standard sets overview; 2020. https://www.ichom.org [Accessed 6 July 2025].Suche in Google Scholar
30. Ferrari, A, Pennestrì, F, Bonciani, M, Banfi, G, Vainieri, M, Tomaiuolo, R. The role of patient-reported experiences in disclosing genetic prenatal testing: findings from a large-scale survey on pregnant women. Eur J Obstet Gynecol Reprod Biol X 2024;23:100327. https://doi.org/10.1016/j.eurox.2024.100327.Suche in Google Scholar PubMed PubMed Central
31. Plebani, M. Advancing value-based laboratory medicine. Clin Chem Lab Med 2024;29. https://doi.org/10.1515/cclm-2024-0823.Suche in Google Scholar PubMed
© 2025 Walter de Gruyter GmbH, Berlin/Boston
