Impact of an autonomous delivery robot on sample turnaround time in a clinical laboratory: an early evaluation of first implementation
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Abstract
Objectives
The aim of this study was to evaluate the impact of introducing an autonomous courier in a clinical laboratory, focusing on specimen turnaround time (TAT). We assessed whether high-frequency robotic transport from the central accessioning area to analytical sections could reduce delays and variability compared with manual delivery.
Methods
We retrospectively analyzed routine lithium–heparin glucose specimens processed during the initial days of robot deployment (13–23 May 2025, weekdays, 08:00–16:00) and compared them with the same period in 2024 under manual transport. TAT was defined as the interval from check-in at accessioning to technical validation in the Laboratory Information System (LIS). Data were assessed globally and stratified by time of day, examining changes in central tendency, dispersion, and extreme delays.
Results
In total, 6,299 samples in 2024 and 5,759 in 2025 were analyzed, showing a leftward shift of the distribution with fewer delay. In 2025, the mean TAT decreased from 122.64 to 106.72 min, the median from 112 to 101 min, and dispersion tightened. Stratification by time of day also demonstrated consistent improvements.
Conclusions
Even in its earliest days of operation, the delivery robot reduced TAT and variability, converting specimen transport from batch runs into near-continuous flow. These findings highlight ease of adoption and the potential of robotic transport to improve speed, predictability, and safety in intralaboratory logistics. Further validation across longer periods and additional laboratory sections is warranted.
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: The authors state no conflict of interest.
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Research funding: None declared.
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Data availability: Not applicable.
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Articles in the same Issue
- Frontmatter
- Editorial
- Reshaping laboratory medicine through technological advances
- Reviews
- Capillary blood in core laboratories: current and future challenges
- Artificial intelligence and machine learning in thrombosis and hemostasis: a scoping review of clinical and laboratory applications, challenges, and future directions
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- Implementation of an automated alert system of critical results in hospitalized and emergency patients
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