26rhk022 10.3205/26rhk022 urn:nbn:de:0183-26rhk0228 Meeting Abstract Alanwar Alanwar Amr A

Technical University of Munich, München, Deutschland TUM School of Computation, Information and Technology, Department of Computer Engineering, München, Deutschland

author Hafez Hafez Ahmed A

Technical University of Munich, München, Deutschland TUM School of Computation, Information and Technology, Department of Computer Engineering, Rheumapraxis Bonn, Bonn, Deutschland

author Abdellatif Abdellatif Hussein H

Rheumapraxis Bonn, Bonn, Deutschland

author Alfaar Alfaar Ahmed A

Roya Eye-center, Stendal, Berlin, Deutschland

author German Medical Science GMS Publishing House

Düsseldorf

610 20260909 engl This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 License. Dieser Artikel ist ein Open-Access-Artikel und steht unter den Lizenzbedingungen der Creative Commons Attribution 4.0 License (Namensnennung). M0656 022 Deutsche Gesellschaft für Rheumatologie Deutsche Gesellschaft für Orthopädische Rheumatologie Gesellschaft für Kinder- und Jugendrheumatologie 54. Kongress der Deutschen Gesellschaft für Rheumatologie und Klinische Immunologie (DGRh), 36. Jahrestagung der Gesellschaft für Kinder- und Jugendrheumatologie (GKJR), 40. Jahrestagung der Deutschen Gesellschaft für Orthopädische Rheumatologie (DGORh) Deutscher Rheumatologiekongress 2026 Digitale Rheumatologie Leipzig 20260909 20260912 DR.10 TextBackground: 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. 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 2025 Am J Clin Pathol 759-777 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. 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Case Studies on Laboratory Automation: Efficiency, Cost, and Staff Outcomes. Journal of Laboratory Automation. 2023;28(2):101-110. Kim K Lee SG Kim TH Lee SG Economic Evaluation of Total Laboratory Automation in the Clinical Laboratory of a Tertiary Care Hospital 2022 Ann Lab Med 89-95 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 https://doi.org/10.3343/alm.2022.42.1.89 Holland I Davies JA Automation in the Life Science Research Laboratory 2020 Front Bioeng Biotechnol 571777 Holland I, Davies JA. Automation in the Life Science Research Laboratory. Front Bioeng Biotechnol. 2020 Nov 13;8:571777. 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