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Background: Largescale clinical laboratories are seeking the adoption of robotic assistants to increase throughput and standardize pre-analytical handling, yet evidence linking operational gains with staff experience in routine practice on small- and medium- sized labs remains limited.

Methods: We conducted a prospective, single-center observational study over 35 working days (January–April 2025) in a health-center diagnostic laboratory in Germany. Days with robotic assistance (“robot days”) were compared with fully manual days using a prespecified non-parametric analysis plan. The primary outcome was daily sample throughput (robot-processed and human-processed items). Secondary outcomes were perceived time savings, stress, training adequacy, operator confidence, safety events and near-misses, reliability faults, satisfaction, and workflow preference. Comparisons used Mann–Whitney U tests with rank-biserial r; associations used Spearman’s ρ; categorical endpoints used Fisher’s exact tests (α = 0.05).

Results: Twenty robot days and 12 manual days werethree additional mixed-exposure days were included for descriptive analyses only. On robot days, the system processed 14.1 ± 13.2 samples/day. Human operators processed fewer items on robot days than manual days (12.3 ± 9.6 vs. 25.0 ± 0.0; p = 0.002; r = –0.72). Robot and human volumes were inversely correlated (ρ = –0.62, p < 0.001). Technical issues occurred on 8/20 robot days (40%): mechanical 62.5%, software 37.5%, navigation 25.0%, communication 25.0%; multiple issue types could co-occur. Staff reported 15–30 minutes saved on 45% of robot days. Stress was lower with the robot (U = 48.5, p = 0.023, r = 0.42). Training was rated adequate in 65% of instances during robot activation, yet confidence was variable (20% “very confident,” 20% “slight confidence”); confidence correlated with training adequacy (ρ = 0.58, p = 0.007). Safety incidents did not differ (p = 1.000), though near-misses were more frequently reported on robot days. Satisfaction was higher on manual days (U = 52.0, p = 0.038, r = 0.39). Among 22 responses, 63.6% preferred manual workflows, 22.7% preferred robots, 13.6% had no preference. Higher fault frequency correlated with lower satisfaction (ρ = –0.49, p = 0.028) and lower robot preference (ρ = –0.53, p = 0.016).

Conclusions: In routine practice, a laboratory assistant robot reallocated repetitive handling and reduced perceived stress while increasing throughput; however, reliability events were common and strongly shaped satisfaction and acceptance. Improving mechanical robustness, troubleshooting.

Objective: To evaluate the effect of a laboratory assistant robot on workflow efficiency, reliability, safety signals, and staff-reported outcomes during routine diagnostics.support, and competency-based training may be pivotal for sustainable adoption. Limitations include a single-center design, a small sample, and a short duration of the study during early adoption. Multi-site evaluations and more formal designs, such as randomized or stepped-wedge implementations, as used in some broader workforce and automation studies, may better capture variability across institutions and adoption stages.

References

[1] Garcia E, Diaz J, Kundu I, Kelly M, Soles R. The American Society for Clinical Pathology 2024 Vacancy Survey of medical laboratories in the United States. Am J Clin Pathol. 2025 Nov 19;164(5):759-777. DOI: 10.1093/ajcp/aqaf101
[2] Alsuwairi MMS, Alshammari DM, Alshehri AS, et al. The Impact of Automation on Clinical Laboratory Efficiency and Error Reduction. Saudi Journal of Medicine and Public Health. 2024;1(2):927-939.
[3] Buesing M, et al. Artificial Intelligence and Robotics in Laboratory Workflow Optimization. Clinical Chemistry and Laboratory Medicine. 2022;60(8):1234-1242.
[4] Schumacher J, et al. Case Studies on Laboratory Automation: Efficiency, Cost, and Staff Outcomes. Journal of Laboratory Automation. 2023;28(2):101-110.
[5] Kim K, Lee SG, Kim TH, Lee SG. Economic Evaluation of Total Laboratory Automation in the Clinical Laboratory of a Tertiary Care Hospital. Ann Lab Med. 2022 Jan 1;42(1):89-95. DOI: 10.3343/alm.2022.42.1.89
[6] Holland I, Davies JA. Automation in the Life Science Research Laboratory. Front Bioeng Biotechnol. 2020 Nov 13;8:571777. DOI: 10.3389/fbioe.2020.571777
[7] Savela N, Latikka R, Oksa R, Kortelainen S, Oksanen A. Affective Attitudes Toward Robots at Work: A Population-Wide Four-Wave Survey Study. Int J Soc Robot. 2022;14(6):1379-1395. DOI: 10.1007/s12369-022-00877-y
[8] Scamarcio V, Tan J, Stellacci F, Hughes J. Reliable and robust robotic handling of microplates via computer vision and touch feedback. Front Robot AI. 2025 Jan 7;11:1462717. DOI: 10.3389/frobt.2024.1462717
[9] Nazareno L, Schiff DS. The impact of automation and artificial intelligence on worker well-being. Technology in Society. 2021;67:101679.
[10] Johnson K. Hospital robots are helping combat a wave of nurse burnout. Wired Magazine. 2022.
[11] Eitrheim M, Kaarstad M, Sørensen L, Berg K. Workload of rehabilitation healthcare personnel when assisted by a robot. In: Proceedings of the 33rd European Safety and Reliability Conference. 2023. p. 2326-2333.
[12] Sayfeld L, Peretz Y, Someshwar R, Edan Y. Evaluation of Human-Robot Collaboration Models for Fluent Operations in Industrial Tasks [preprint]. arXiv. 2017. DOI: 10.48550/arXiv.1708.04790
[13] Zhou J, Luo M, Chen L, et al. A multi-robot–multi-task scheduling system for autonomous chemistry laboratories. Digital Discovery. 2025;4(3):636-652.