Silent Data Waste in Public Laboratories: A Conceptual Framework for Sustainable Data‑Driven Management
DOI:
https://doi.org/10.65405/r6tm5v65الكلمات المفتاحية:
Data utilization, public laboratories, Silent data waste, Specimen rejection, Sustainable management, Turnaround timeالملخص
Public medical laboratories generate extensive volumes of routine operational data, including test workloads, turnaround times, quality control results, reagent consumption records, and equipment performance indicators. Despite this abundance, a substantial proportion of these data remains underutilized in managerial and strategic decision‑making. This phenomenon is conceptualized in this paper as silent data waste, defined as the systematic loss of informational value arising not from data scarcity, but from weak governance, limited analytical capacity, and fragmented information systems.
This conceptual paper positions silent data waste as a managerial and organizational challenge rather than a purely technical limitation. Drawing on a focused and critical review of recent literature in laboratory management, health data governance, and sustainable healthcare systems, the study develops an integrated conceptual framework linking routine data utilization to quality performance and resource efficiency outcomes. Key operational indicators, such as median turnaround time, specimen rejection rates, and quality control compliance metrics, are used as quantifiable managerial performance measures to illustrate how improved data utilization can support evidence-based management, reduce material and environmental waste, and enhance service reliability. The proposed framework advances the discourse on sustainable laboratory management by reframing routine operational data as a strategic leadership asset. By strengthening data governance structures, analytical tools, and managerial data literacy, public laboratories can transition from reactive operations toward proactive, sustainable, and performance‑oriented management aligned with relevant sustainable development goals.
The review synthesizes evidence indicating that 30–50% of routine laboratory data remains underutilized, with measurable performance and sustainability implications.
التنزيلات
المراجع
1. Alhammad, L. A., Ainosah, T. K., Ahmad, A. M., Samarkandi, M. S., Jawi, N. H., Alharthi, M. A., Alsharif, A. M., Anazi, E. A. A., Aldugeshem, S. A., & Johali, F. Y. (2024). The impact of laboratory automation on efficiency and accuracy in healthcare settings. International Journal of Community Medicine and Public Health, 11(1), 459–463. https://doi.org/10.18203/2394-6040.ijcmph20233857
2. Arah, O. A., Klazinga, N. S., Delnoij, D. M., Asbroek, A. H., & Custers, T. (2020). Conceptual frameworks for health systems performance. International Journal for Quality in Health Care, 32(1), 5–13. https://doi.org/10.1093/intqhc/mzz083
3. Cai, X., Lin, Y., Zhan, L., Lu, Q., Wu, Z., & Lin, X. (2025). Optimizing clinical laboratory efficiency through digital shadow and Lean Six Sigma integration: A real-time monitoring approach to reduce intra-laboratory turnaround time. Digital Health, 11, 20552076251375939. https://doi.org/10.1177/20552076251375939
4. International Organization for Standardization. (2022). ISO 15189:2022 medical laboratories-Requirements for quality and competence. ISO
5. Kruse, C. S., Smith, B., Vanderlinden, H., & Nealand, A. (2020). Security techniques for electronic health records. Journal of Medical Systems, 44(2), 1–9. https://doi.org/10.1007/s10916-019-1535-0
6. Organisation for Economic Co-operation and Development. (2021). Health in the 21st century: Putting data to work for stronger health systems. OECD Publishing. https://doi.org/10.1787/e3b23f8e-en
7. Pozzan, C., Tiso, A., Pamich, C., & Verbano, C. (2025). Sustainable care quality improvement: A scoping literature review of performance measurement in lean healthcare implementations. BMC Health Services Research, 25, 1452. https://doi.org/10.1186/s12913-025-13598-5
8. Shaw, J., Rudzicz, F., Jamieson, T., & Goldfarb, A. (2022). Artificial intelligence and the implementation challenge in healthcare. NPJ Digital Medicine, 5, 65. https://doi.org/10.1038/s41746-022-00619-0
9. Tuladhar, S., Mwamelo, K., Manyama, C., Asefa, F., Soremekun, S., Worrall, S., & Aagaard-Hansen, J. (2023). Proceedings from the CIHLMU 2022 symposium: Availability of and access to quality data in health. BMC Proceedings, 17(Suppl 10), 21. https://doi.org/10.1186/s12919-023-00271-1
10. Uçar, K. T. (2023). Examining the influence of sample rejection rates on the carbon footprint of clinical laboratories: A retrospective analysis. Journal of Health Sciences and Medicine, 6(5), 993–997. https://doi.org/10.32322/jhsm.1342155
11. United Nations. (2015). Transforming our world: The 2030 agenda for sustainable development. United Nations. https://sdgs.un.org/2030agenda
12. WHO Regional Office for Europe. (2022). Strengthening health information systems for policy and practice. World Health Organization Regional Office for Europe. https://apps.who.int/iris/handle/10665/352500
13. Worku, A. (2025). Introduction to health care data analytics: An overview. In Healthcare data analytics (pp. 1–15). Springer. https://doi.org/10.1007/s44250-025-00291-x
14. World Bank. (2021). World development report 2021: Data for better lives. World Bank. https://doi.org/10.1596/978-1-4648-1600-0
15. World Health Organization. (2021). Global strategy on digital health 2020–2025. World Health Organization. https://www.who.int/publications/i/item/9789240020924
