“Improving Laboratory Operations and Patient Care through Total Laboratory Automation”
Authors
Ruby Hall Clinic (India)
Ruby Hall Clinic (India)
Article Information
DOI: 10.51584/IJRIAS.2026.11010071
Subject Category: Pathology
Volume/Issue: 11/1 | Page No: 841-854
Publication Timeline
Submitted: 2026-01-20
Accepted: 2026-01-26
Published: 2026-02-06
Abstract
Background
Clinical laboratories are experiencing increasing test volumes, expanding test menus, and sustained pressure to deliver rapid and reliable turnaround times (TAT) amid workforce constraints. Total laboratory automation (TLA) has emerged as a systems-level approach to address these challenges; however, real-world evaluations focusing on workflow transformation and variability reduction remain limited.
Objectives
To evaluate the impact of comprehensive TLA on workflow efficiency, TAT performance, and process stability across core laboratory disciplines.
Methods
A retrospective operational evaluation was performed comparing laboratory performance before and after TLA implementation using LIS data. TAT performance was assessed using the proportion of samples meeting predefined targets across chemistry, immunology, and hematology disciplines. Process stability and variability were evaluated using statistical process control methods. Reductions in manual workflow steps and sample handling touchpoints were quantified. TAT performance for time-critical assays was assessed on the automated track.
Results
Implementation of TLA resulted in 56% reduction in manual workflow steps and a 75% reduction in sample handling touchpoints. Post-automation, the proportion of samples meeting TAT targets improved across all disciplines, accompanied by significant narrowing of performance variability. On the TLA line, 81–86% of chemistry tests were reported within 30 minutes, and up to 89% of high-sensitivity troponin I results were available within 40 minutes with improved process stability.
Conclusions
Comprehensive TLA significantly improved workflow efficiency, TAT performance, and process stability in a high-volume tertiary care laboratory. Beyond reductions in absolute TAT, automation enhanced predictability and operational control, supporting clinical decision-making, quality governance, and readiness for data-driven laboratory practice.
Keywords
Total Laboratory Automation, Laboratory Operations, Turnaround Time, Workflow Efficiency, Process Stability
Downloads
References
1. Hawker CD: Laboratory automation: total and subtotal. Clin Lab Med. 2007, 27:749–70, vi. 10.1016/j.cll.2007.07.010 [Google Scholar] [Crossref]
2. Genzen JR, Burnham C-AD, Felder RA, Hawker CD, Lippi G, Peck Palmer OM: Challenges and Opportunities in Implementing Total Laboratory Automation. Clin Chem. 2018, 64:259–64. 10.1373/clinchem.2017.274068 [Google Scholar] [Crossref]
3. Seaberg RS, Stallone RO, Statland BE: The role of total laboratory automation in a consolidated laboratory network. Clin Chem. 2000, 46:751–6. [Google Scholar] [Crossref]
4. Thomson RB, McElvania E: Total Laboratory Automation. Clinics in Laboratory Medicine. 2019, 39:371–89. 10.1016/j.cll.2019.05.002 [Google Scholar] [Crossref]
5. Lippi G, Da Rin G: Advantages and limitations of total laboratory automation: a personal overview. Clin Chem Lab Med. 2019, 57:802–11. 10.1515/cclm-2018-1323 [Google Scholar] [Crossref]
6. Nam Y, Park HD: Revolutionizing Laboratory Practices: Pioneering Trends in Total Laboratory Automation. Ann Lab Med. 2025, 45:472–83. [Google Scholar] [Crossref]
7. Dolci A, Giavarina D, Pasqualetti S, Szőke D, Panteghini M: Total laboratory automation: Do stat tests still matter? Clinical Biochemistry. 2017, 50:605–11. 10.1016/j.clinbiochem.2017.04.002 [Google Scholar] [Crossref]
8. Ialongo C, Porzio O, Giambini I, Bernardini S: Total Automation for the Core Laboratory: Improving the Turnaround Time Helps to Reduce the Volume of Ordered STAT Tests. J Lab Autom. 2016, 21:451–8. 10.1177/2211068215581488 [Google Scholar] [Crossref]
9. Mayne ALH, Mayne ES, Louw S: Proposed application of six sigma metrics using a gamma distribution to monitor turnaround time for a high-volume coagulation test before and after introduction of a total laboratory automation platform in an academic laboratory in South Africa. Int J Lab Hematol. 2020, 42:e124–7. 10.1111/ijlh.13163 [Google Scholar] [Crossref]
10. Sarkozi L, Simson E, Ramanathan L: The effects of total laboratory automation on the management of a clinical chemistry laboratory. Retrospective analysis of 36 years. Clin Chim Acta. 2003, 329:89–94. 10.1016/s0009-8981(03)00020-2 [Google Scholar] [Crossref]
11. Tseng C-W, Li Y-C, Lee H-S, Tseng Y-M: Laboratory testing consolidation and total laboratory automation improves service efficiency and effectiveness: a study of a medical center in Taiwan. Lab Med. 2024, 55:677–85. 10.1093/labmed/lmae044 [Google Scholar] [Crossref]
12. Pasqualetti S, Birindelli S, Aloisio E, Dolci A, Panteghini M: Clinical Governance Remains a Priority in Total Laboratory Automation Era. The Journal of Applied Laboratory Medicine. 2019, 4:130–2. 10.1373/jalm.2018.028035 [Google Scholar] [Crossref]
13. Markus C, Tan RZ, Loh TP: Evidence-based approach to setting delta check rules. Crit Rev Clin Lab Sci. 2021, 58:49–59. 10.1080/10408363.2020.1800585 [Google Scholar] [Crossref]
14. Brown AS, Badrick T: The next wave of innovation in laboratory automation: systems for auto-verification, quality control and specimen quality assurance. Clin Chem Lab Med. 2023, 61:37–43. 10.1515/cclm-2022-0409 [Google Scholar] [Crossref]
15. Plebani M: Total laboratory automation: fit for its intended purposes? Clin Chem Lab Med. 2026, 64:22–6. 10.1515/cclm-2025-0855 [Google Scholar] [Crossref]
16. El-Khoury JM: Breaking the Chain: Navigating the Pitfalls of Total Laboratory Automation. J Appl Lab Med. 2024, 9:1095–6. 10.1093/jalm/jfae061 [Google Scholar] [Crossref]
17. Bartosova K, Kubicek Z, Franekova J, Louzensky G, Lavrikova P, Jabor A: Analysis of Four Automated Urinalysis Systems Compared to Reference Methods. Clin Lab. 2016, 62:2115–23. 10.7754/Clin.Lab.2016.160316 [Google Scholar] [Crossref]
18. Burckhardt I: Laboratory Automation in Clinical Microbiology. Bioengineering. 2018, 5:102. 10.3390/bioengineering5040102 [Google Scholar] [Crossref]
19. Da Rin G, Seghezzi M, Padoan A, et al.: Multicentric evaluation of the variability of digital morphology performances also respect to the reference methods by optical microscopy. Int J Lab Hematol. 2022, 44:1040–9. 10.1111/ijlh.13943 [Google Scholar] [Crossref]
20. Salvagno GL, Danese E, Lippi G: Mass spectrometry and total laboratory automation: opportunities and drawbacks. Clin Chem Lab Med. 2020, 58:994–1001. 10.1515/cclm-2019-0723 [Google Scholar] [Crossref]
21. Çubukçu HC, Topcu Dİ, Yenice S: Machine learning-based clinical decision support using laboratory data. Clin Chem Lab Med. 2024, 62:793–823. 10.1515/cclm-2023-1037 [Google Scholar] [Crossref]
22. Cobbaert C, Albersen A, Zwiers I, Schippers P, Gillis J: Designing a diagnostic Total Testing Process as a base for supporting diagnostic stewardship. Clin Chem Lab Med. 2020, cclm-2020–1251. 10.1515/cclm-2020-1251 [Google Scholar] [Crossref]
23. Poland DCW, Cobbaert CM: Blood self-sampling devices: innovation, interpretation and implementation in total lab automation. Clinical Chemistry and Laboratory Medicine (CCLM). 2025, 63:3–13. 10.1515/cclm-2024-0508 [Google Scholar] [Crossref]
24. Ellison TL, Alharbi M, Alkaf M, et al.: Implementation of total laboratory automation at a tertiary care hospital in Saudi Arabia: effect on turnaround time and cost efficiency. Ann Saudi Med. 2018, 38:352–7. 10.5144/0256-4947.2018.352 [Google Scholar] [Crossref]
25. Kim K, Lee S-G, Kim TH, Lee SG: Economic Evaluation of Total Laboratory Automation in the Clinical Laboratory of a Tertiary Care Hospital. Ann Lab Med. 2022, 42:89–95. 10.3343/alm.2022.42.1.89 [Google Scholar] [Crossref]
26. Das B, Bhat L, Patil R, Tawde V, Kamath S, Pal P: Total lab Automation Validation/Verification Protocol: First Step of Process Excellence Journey in Smart Core Lab in Kokilaben Dhirubhai Ambani Hospital & Medical Research Institute, Mumbai, India. Clinical Chemistry. 2023, 69:hvad097.375. 10.1093/clinchem/hvad097.375 [Google Scholar] [Crossref]
27. Siemens Healthineers: Case Study: How data and automation revolutionized a reference laboratory network in India. 2025. [Google Scholar] [Crossref]
28. Abstracts of The 52nd Annual Conference of Research Society for the Study of Diabetes in India. Int J Diabetes Dev Ctries. 2024, 44:83–172. 10.1007/s13410-024-01421-6 [Google Scholar] [Crossref]
29. Lou AH, Elnenaei MO, Sadek I, Thompson S, Crocker BD, Nassar B: Evaluation of the impact of a total automation system in a large core laboratory on turnaround time. Clin Biochem. 2016, 49:1254–8. 10.1016/j.clinbiochem.2016.08.018 [Google Scholar] [Crossref]
30. Yu H-YE, Lanzoni H, Steffen T, Derr W, Cannon K, Contreras J, Olson JE: Improving Laboratory Processes with Total Laboratory Automation. Lab Med. 2019, 50:96–102. 10.1093/labmed/lmy031 [Google Scholar] [Crossref]
31. Zhang BY, Wang G, Wang X, Wu BS, Liu D, Zhang QQ, Zheng L, Li BR, Zhang XF, Wu W: Development and assessment of a novel multimedia-based educational software for teaching peripheral blood smear morphology. BMC Med Educ. 2025, 25:397. [Google Scholar] [Crossref]
32. Daldaban-Dinçer Ş, Aksaray S: The Effect of Total Laboratory Automation on Urine Culture Result Times in a Consolidated Laboratory. Clin Lab. 2023, 69:. 10.7754/Clin.Lab.2022.220736 [Google Scholar] [Crossref]
33. Gruson D, Zima T, Plebani M: Automation in value-based laboratory medicine: driving precision, capacity, and better outcomes. Clin Chem Lab Med. Published Online First: 15 January 2026. 10.1515/cclm-2026-0023 [Google Scholar] [Crossref]
34. Gervasoni J, Pedrazzini D, Cicchinelli M, et al.: Impact of an autonomous delivery robot on sample turnaround time in a clinical laboratory: an early evaluation of first implementation. Clin Chem Lab Med. Published Online First: 10 December 2025. 10.1515/cclm-2025-1336 [Google Scholar] [Crossref]