“Improving Laboratory Operations and Patient Care through Total Laboratory Automation”
by Dr. Anjali J. Kelkar, Dr. Vaishali T. Patil
Published: February 6, 2026 • DOI: 10.51584/IJRIAS.2026.11010071
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.