Benefits Derived from Full Laboratory Automation in Microbiology: a Tale of Four Laboratories
Open accessAbstract
Automation in clinical microbiology is starting to become more commonplace and reportedly offers several advantages over the manual laboratory. Most studies have reported on the rapid turnaround times for culture results, including times for identification of pathogens and their respective antimicrobial susceptibilities, but few have studied the benefits from a laboratory efficiency point of view.
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Algorithm-assisted culture interpretation accelerates reporting and reduces the proportion of cultures requiring extended review.
“These observations are consistent with prior studies demonstrating that algorithm-assisted culture interpretation (by Culbreath et al.) accelerates reporting and reduces the proportion of cultures requiring extended review (1).”
From Improving turnaround times with artificial intelligence in microbiology (2026) · cites this paper as [1]
The Results (TriCore Reference Laboratories) report faster reporting after algorithm-assisted automation: median turnaround time fell to 21 hours, and fewer cultures took over 48 hours to reach a final report. But the same section says the proportion requiring additional laboratory workup remained about 40% across stages (39% with algorithm-assisted automation). The paper supports faster reporting, not a reduced proportion requiring additional workup. (LLM, conf=0.88)
Experimental — accuracy not yet measuredAutomated technologies improve efficiency compared to manual methods across laboratories of varying size and patient acuity.
“Automated technologies improve efficiency compared to manual methods across laboratories of varying size and patient acuity (1).”
From Improving turnaround times with artificial intelligence in microbiology (2026) · cites this paper as [1]
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Mutters, N. T., Hodiamont, C. J., Jong, M. D. D., Overmeijer, H. P. J., Boogaard, M. V. D., & E.Visser, C. (2014) · Annals of Laboratory Medicine · doi:10.3343/alm.2014.34.2.111 · Cited by 82 · see citing works
Faron, M. L., Buchan, B. W., Vismara, C., Lacchini, C., Bielli, A., Gesu, G., Liebregts, T., van Bree, A., Jansz, A., Soucy, G., et al. (2016) · Journal of Clinical Microbiology · doi:10.1128/jcm.02778-15 · Cited by 69 · see citing works
Self-citationFaron, M. L., Buchan, B. W., Coon, C., Liebregts, T., van Bree, A., Jansz, A. R., Soucy, G., Korver, J., & Ledeboer, N. A. (2016) · Journal of Clinical Microbiology · doi:10.1128/jcm.01040-16 · Cited by 63 · see citing works
Self-citationVan, T. T., Mata, K., & Dien Bard, J. (2019) · Journal of Clinical Microbiology · doi:10.1128/jcm.00811-19 · Cited by 60 · see citing works
Theparee, T., Das, S. B., & Thomson, R. B. (2017) · Journal of Clinical Microbiology · doi:10.1128/jcm.01242-17 · Cited by 54 · see citing works
Croxatto, A., Marcelpoil, R., Orny, C., Morel, D. R., Prod’hom, G., & Greub, G. (2017) · Biomedical Journal · doi:10.1016/j.bj.2017.09.001 · Cited by 47 · see citing works
Faron, M. L., Buchan, B. W., Relich, R. F., Clark, J., & Ledeboer, N. A. (2020) · Journal of Clinical Microbiology · doi:10.1128/jcm.01683-19 · Cited by 41 · see citing works
Graham, M., Tilson, L., Streitberg, R., Hamblin, J., & Korman, T. M. (2016) · Diagnostic Microbiology and Infectious Disease · doi:10.1016/j.diagmicrobio.2016.06.020 · Cited by 40 · see citing works
Yarbrough, M. L., Lainhart, W., McMullen, A. R., Anderson, N. W., & Burnham, C. D. (2018) · European Journal of Clinical Microbiology & Infectious Diseases · doi:10.1007/s10096-018-3391-7 · Cited by 39 · see citing works
Faron, M. L., Buchan, B. W., Samra, H., & Ledeboer, N. A. (2019) · Journal of Clinical Microbiology · doi:10.1128/jcm.00540-19 · Cited by 31 · see citing works
Burckhardt, I., Horner, S., Burckhardt, F., & Zimmermann, S. (2018) · European Journal of Clinical Microbiology & Infectious Diseases · doi:10.1007/s10096-018-3308-5 · Cited by 21 · see citing works
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Research works printed but not linked: 1
- [9]Keyak C. 2019. Utilizing digital imaging to determine optimal incubation times for routine urine cultures. Med Lab Observ 51(10): 40– 44. https://www.mlo-online.com/diagnostics/microbiology/article/21106580/utilizing-digital-imaging-to-determine-optimal-incubation-times-for-routine-urine-cultures.
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Mencacci, A., Socio, G. V. D., Pirelli, E., Bondi, P., & Cenci, E. (2023) · Frontiers in Cellular and Infection Microbiology · doi:10.3389/fcimb.2023.1188684 · Cited by 37 · see citing works
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Zhang, W., Wu, S., Deng, J. G., Liao, Q., Liu, Y., Li, X., Shu, L., Yu, Y., Xiao, Y., Ma, Y., et al. (2021) · Frontiers in Cellular and Infection Microbiology · doi:10.3389/fcimb.2021.765504 · Cited by 13 · see citing works
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Farhana, S., & Raisa, J. (2026) · Diagnostic Microbiology and Infectious Disease · doi:10.1016/j.diagmicrobio.2026.117676
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- Authors
- Culbreath, K., Piwonka, H., Korver, J., & Noorbakhsh, M.
- Affiliations
- Department of Infectious Disease, TriCore Laboratories, Albuquerque, New Mexico, USA
- Willis-Knighton Cancer Center (US)
- Hamilton Regional Laboratory Medicine Program (CA)
- Sutter Health (US)
- Corresponding author
- Culbreath, Karissa
- ORCID
- Culbreath, Karissa
- Venue
- Journal of Clinical Microbiology
- Publisher
- American Society for Microbiology
- Published
- 2021-02-18
- Volume
- 59
- Issue
- 3
- ISSN
- 0095-1137 (ISSN-L)
- 1098-660X
- PMC
- PMC8106725
- OpenAlex
- W3109871469
- Cited by
- 44
- References
- 11
- Citations by year
- 2026: 11 · 2025: 3 · 2024: 8 · 2023: 9 · 2022: 7 · 2021: 5 · 2020: 1
- FWCI
- 3.31
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Journal of Clinical Microbiology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- UNM’s Digital Repository (University of New Mexico) (submittedVersion)
- Topics
- Health Sciences › Medicine › Physiology › Clinical Laboratory Practices and Quality Control
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Clinical Biochemistry › Bacterial Identification and Susceptibility Testing
- Health Sciences › Health Professions › Emergency Medical Services › Healthcare Operations and Scheduling Optimization
- Keywords
- laboratory automation, clinical microbiology laboratory, laboratory efficiency, full-time equivalents, staff reallocation, laboratory productivity, cost avoidance, antimicrobial susceptibility testing, pathogen identification
- MeSH terms
- Automation, Humans, Laboratories, North America, Automation, Laboratory
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