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Cited by 2Clinical Laboratory Practices and Quality ControlBacterial Identification and Susceptibility TestingArtificial Intelligence in Healthcare and Education

Improving turnaround times with artificial intelligence in microbiology

Open access
Davidson, Ross, Heinstein, Charles, Patriquin, Glenn, Goneau, Lee William, Brown, Leah Ashleigh, Hill, Brian James·2026·Journal of Clinical Microbiology
DOI 10.1128/jcm.00354-26PMC PMC13463692
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Abstract

This dual-center study evaluated the impact of artificial intelligence (AI) on urine culture turnaround times in Canadian diagnostic laboratories using microbiology laboratory automation. Data were collected before and after the implementation of PhenoMATRIX (PM), an AI-based software that provides continuous culture sorting and result interpretation support. In both a low-volume tertiary care hospital and a high-volume community laboratory, PM enabled earlier availability of interpretable results; however, reductions in time to result reporting (TTRR) were contingent on workflows for result release. At the tertiary care site, implementation of PM alone was associated with increased TTRR, reflecting delays between result availability and reporting. Integration of PM+ enabled automated release of negative results as they became available, resulting in a TTRR reduction of approximately 1.3 h. At the community laboratory, where PM+ was not implemented, a TTRR improvement of approximately 5.3 h was achieved by advancing manual screening workflows (08:00 vs 16:00), facilitating earlier review and release of PM-generated results. These findings indicate that AI-driven culture assessment reduces TTRR when coupled with processes that enable timely result release, either through automated reporting or optimized laboratory review workflows. IMPORTANCE: Advances in artificial intelligence (AI), coupled with the power of laboratory automation, is the next step in the evolution of the clinical microbiology laboratory. We demonstrated that bacterial culture assessment (urine cultures) by AI decreases the time required to release results to clinicians and reduces the hands-on time required by technologists to analyze and finalize laboratory analysis. This study was performed in two different laboratories that differed not only in geography but also in scope and scale. The same ultimate benefits were seen in both institutions demonstrating that this technology is widely applicable for most laboratories.

AI snapshot

Written from the paper’s full text.

TL;DR

This dual-center Canadian study evaluated whether AI-based PhenoMATRIX (PM) and automated release (PM+) reduce urine culture time to result reporting (TTRR) in automated microbiology laboratories. PM enabled earlier result availability, but TTRR reductions depended on result-release workflows: PM+ at QEII reduced TTRR by ~1.3 h, while workflow changes at Dynacare without PM+ reduced TTRR by ~5.3 h.

Key findings

  • At QEII, PM alone did not reduce TTRR; with PM+, mean TTRR decreased by approximately 1 h 19 min (≈1.3 h) from June 2023 to May 2025. By 17 h, final reports released increased to 24.9% with PM+ vs 8.3% PrePM and 7.3% PostPM; by 18 h, 49.6% vs 33.0% and 22.2%; pending reports beyond 24 h fell from 29.7% PrePM and 29.8% PostPM to 24.1% PM+.
  • At Dynacare, after PM plus workflow changes (urine screening expanded from 16:00–24:00 to 08:00–24:00), mean final report TTRR improved by approximately 5 h 18 min (≈5.3 h) from May 2022 to May 2023. Reports released by 17 h increased from 10.8% to 27.4%; by 18 h from 21.6% to 50.8%; by 24 h from 58.7% to 76.8%; pending beyond 24 h decreased from 41.3% to 23.2%.
  • Productivity changes included reallocation of one full-time equivalent (FTE) from the urine bench at Dynacare; at QEII, total laboratory automation and PostPM+ allowed reallocation of two FTE positions, one through reduced labor with automation and one from urine screening to another laboratory area.
  • Daily average urine culture volumes increased from 167 in 2023 to approximately 180 in 2024–2025 at QEII (~7%) and from 1,593 in 2022 to 1,778 in 2023 at Dynacare (~11.6%).

Methods

  • Dual-center before-after study at QEII, a tertiary care hospital in Halifax processing over 65,000 urine specimens annually, and Dynacare Laboratories in Brampton, Ontario, a high-volume community laboratory processing approximately 650,000 urine specimens annually. Study period was May 2022 to May 2025, with discrete timepoints: QEII June 2023 (PrePM), September 2024 (PostPM), May 2025 (PostPM+); Dynacare May 2022 (PrePM) and May 2023 (PostPM).
  • Midstream urine specimens were plated and incubated using WASPLab automation and imaged at approximately 16 h. Organism identification used Vitek MS, and antimicrobial susceptibility testing used MALDI-TOF Vitek 2 MS per CLSI guidelines; E. coli with characteristic colonies was reported without MS confirmation at both sites.
  • Main analysis was descriptive comparison of TTRR and proportions of final reports released by time thresholds before and after PM/PM+ implementation. TTRR definitions differed by site: Dynacare measured from specimen placement on WASPLab to final approval/release in the LIS; QEII measured from specimen receipt in the laboratory to result release in the LIS, including ID and/or AST when performed. No formal statistical testing is described.

Limitations

  • The paper does not include an explicit limitations section.
  • Evident: At Dynacare, PM+ was not implemented, and the TTRR improvement coincided with workflow changes (expanded screening hours and one FTE reallocation), so the independent effect of AI cannot be isolated from workflow changes.
  • Evident: TTRR definitions differed between sites, and the design uses discrete before/after timepoints without a concurrent control; at QEII PM alone did not reduce TTRR (the abstract describes increased TTRR), underscoring dependence on result-release workflows.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1128/jcm.00354-26

Cites

9 of 9 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

7 of 12 cited sources checked · 5 supported · 0 flagged · accuracy not yet measured

  • Premisecitation

    Prior studies have shown the accuracy of these algorithms across multiple specimen types and media.

    “Prior studies have shown the accuracy of these algorithms across multiple specimen types and media (2–6).”
    Quote in the paperNot a substantial claim3 of 5 sources checked
    • cites [2] Faron ML, Buchan BW, Coon C, Liebregts T, van Bree A, Jansz AR, Soucy G, Korver J, Ledeboer NA. 2016. Automatic digital analysis of chromogenic media for vancomycin-resistant-Enterococcus screens using copan WASPLab. J Clin Microbiol 54: 2464– 2469. doi: 10.1128/JCM.01040-16 27413193 PMC5035414 source
    • cites [3] Faron ML, Buchan BW, Vismara C, Lacchini C, Bielli A, Gesu G, Liebregts T, van Bree A, Jansz A, Soucy G, Korver J, Ledeboer NA. 2016. Automated scoring of chromogenic media for detection of methicillin-resistant Staphylococcus aureus by use of WASPLab image analysis software. J Clin Microbiol 54: 620– 624. doi: 10.1128/JCM.02778-15 26719443 PMC4767952 source
    • cites [4] Faron M.L, Buchan BW, Relich RF, Clark J, Ledeboer NA. 2020. Evaluation of the WASPLab segregation software to automatically analyze urine cultures using routine blood and macconkey agars. J Clin Microbiol 58: e01683-19. doi: 10.1128/JCM.01683-19 31941690 PMC7098764 source
    • cites [5] Van TT, Mata K, Dien Bard J. 2019. Automated detection of Streptococcus pyogenes pharyngitis by use of colorex strep A CHROMagar and WASPLab artificial intelligence chromogenic detection module software. J Clin Microbiol 57: e00811-19. doi: 10.1128/JCM.00811-19 31434725 PMC6812993 source
    • cites [6] Baker J, Timm K, Faron M, Ledeboer N, Culbreath K. 2020. Digital image analysis for the detection of group B Streptococcus from ChromID StreptoB media using a phenomatrix artificial intelligence software algorithm. J Clin Microbiol 59: e01902-19. doi: 10.1128/JCM.01902-19 33087433 PMC7771474 source

      The Results report the imaging algorithm’s performance for GBS screening on ChromID GBS medium, and the Discussion describes earlier algorithm studies on other chromogenic media. The paper therefore supports accuracy findings across media, but its own evaluation uses vaginal-rectal swabs and does not establish accuracy across multiple specimen types. (LLM, conf=0.82)

      Experimental — accuracy not yet measured
  • Findingown data

    At Dynacare, the overall mean final report TTRR improved by approximately 5 h and 18 min from May 2022 to May 2023.

    “Overall, the mean final report TTRR improved by approximately 5 h and 18 min from May 2022 to May 2023.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: The TTRR data were collected and measured consistently across PrePM, PostPM, and PM+ phases at each site.
  • Findingown data

    At Dynacare, where PM+ was not implemented, a larger reduction in TTRR (~5.3 h) was observed following PM implementation.

    “At the high-volume community laboratory (Dynacare), where PM+ was not implemented, a larger reduction in TTRR (~5.3 h) was observed following PM implementation.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: The observed TTRR improvements can be attributed to the described AI and workflow interventions rather than to other temporal or site-specific factors.
  • Findingreasoning

    At Halifax, one FTE savings was achieved through a reduction in labor requirements with the introduction of the TLA.

    “One FTE savings (reallocation) was achieved through a reduction in labor requirements with the introduction of the TLA”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: The observed TTRR improvements can be attributed to the described AI and workflow interventions rather than to other temporal or site-specific factors.
  • Findingreasoning

    At the community laboratory, a TTRR improvement of approximately 5.3 h was achieved.

    “At the community laboratory, where PM+ was not implemented, a TTRR improvement of approximately 5.3 h was achieved by advancing manual screening workflows (08:00 vs 16:00), facilitating earlier review and release of PM-generated results.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: The observed changes in TTRR were attributable to PM/PM+ and workflow changes rather than other temporal or laboratory factors.
  • Classified as probably not a substantial claim. 5
  • May combine two claims. 15

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Automated 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).”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [1] Culbreath K, Piwonka H, Korver J, Noorbakhsh M. 2021. Benefits derived from full laboratory automation in microbiology: a tale of four laboratories. J Clin Microbiol 59: 01969– 20. doi: 10.1128/JCM.01969-20 PMC8106725 33239383 source

  • May combine two claims.

    Premisecitation

    Full laboratory automation improves culture processing and mitigates staffing challenges.

    “Full laboratory automation has already been shown to improve culture processing and mitigate staffing challenges (7–9).”
    Quote in the paperNot a substantial claim1 of 3 sources checked
    • cites [7] Graham M, Tilson L, Streitberg R, Hamblin J, Korman TM. 2016. Improved standardization and potential for shortened time to results with BD kiestra total laboratory automation of early urine cultures: a prospective comparison with manual processing. Diagn Microbiol Infect Dis 86: 1– 4. doi: 10.1016/j.diagmicrobio.2016.06.020 27422083 source
    • cites [8] Keyak C. 2019. Utilizing digital imaging to determine optimal incubation times for routine urine cultures. Med Lab Observ 51: 40– 44.

      Not checked in the free run.

    • cites [9] Mutters NT, Hodiamont CJ, de Jong MD, Overmeijer HPJ, van den Boogaard M, Visser CE. 2014. Performance of kiestra total laboratory automation combined with MS in clinical microbiology practice. Ann Lab Med 34: 111– 117. doi: 10.3343/alm.2014.34.2.111 24624346 PMC3948823 source

  • May combine two claims.

    Premisecitation

    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).”
    Quote in the paperSubstantial claim1 of 2 sources checked
    • cites Culbreath et al.

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [1] Culbreath K, Piwonka H, Korver J, Noorbakhsh M. 2021. Benefits derived from full laboratory automation in microbiology: a tale of four laboratories. J Clin Microbiol 59: 01969– 20. doi: 10.1128/JCM.01969-20 PMC8106725 33239383 source

      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 measured

  • May combine two claims.

    Premisecitation

    Smart incubation shortens incubation times while maintaining accuracy and enabling faster identification and antimicrobial susceptibility testing results.

    “Smart incubation has been shown to shorten incubation times while maintaining accuracy and enabling faster identification and antimicrobial susceptibility testing results (10).”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [10] Cintrón M, Clark B, Miranda E, Babady NE. 2025. Utility of digital images captured after 4 h of incubation on a microbiology laboratory automation system in guiding the work-up of subcultures from positive blood cultures. J Clin Microbiol 63: e0132024. doi: 10.1128/jcm.01320-24 39704521 PMC11837544 source

  • May combine two claims.

    Findingown data

    Implementation of PM at Dynacare was associated with measurable productivity gains, including the reallocation of one full-time equivalent (FTE) technologist from the urine bench.

    “In addition to improvements in TTRR, implementation of PM at Dynacare was associated with measurable productivity gains, including the reallocation of one full-time equivalent (FTE) technologist from the urine bench.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: The observed TTRR improvements can be attributed to the described AI and workflow interventions rather than to other temporal or site-specific factors.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    At QEII, approximately 77% of urine cultures are negative or reported as normal urogenital flora.

    “Approximately 77% of the QEII’s urine cultures are negative or reported as normal urogenital flora.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    At QEII, by 18 h, 49.6% of urine cultures had a final report available with PM+, compared with 33.0% PrePM and 22.2% PostPM.

    “By 18 h, 49.6% of urine cultures had a final report available, compared with 33.0% (PrePM) and 22.2% (PostPM).”
    Quote in the paperSubstantial claim
    • Rests on an assumption: The TTRR data were collected and measured consistently across PrePM, PostPM, and PM+ phases at each site.

  • May combine two claims.

    Findingown data

    At QEII, at 24 h, 75.9% of final urine culture results were released with PM+, compared with 70.3% PrePM and 70.2% PostPM.

    “At 24 h, 75.9% of final urine culture results were released with PM+, compared with 70.3% (PrePM) and 70.2% (PostPM).”
    Quote in the paperSubstantial claim
    • Rests on an assumption: The TTRR data were collected and measured consistently across PrePM, PostPM, and PM+ phases at each site.

  • May combine two claims.

    Findingown data

    At Dynacare, case review showed that early finalizations reflected either administrative outcomes or cultures imaged slightly earlier (~15.5 h) by WASPLab and finalized promptly when no ID or AST was required.

    “Case review showed these reflected either administrative outcomes (e.g., duplicate specimen cancelations or specimens not processed due to laboratory incidents) or cultures imaged slightly earlier (~15.5 h) by WASPLab and finalized promptly when no ID or AST was required (e.g., no growth).”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    At QEII, PrePM urine cultures were reported as negative, positive, and mixed growth in 56%, 23%, and 21% of cases, respectively.

    “The PrePM urine cultures were reported as negative, positive, and mixed growth in 56%, 23%, and 21% of cases, respectively”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    At Dynacare in May 2023 PostPM, urine cultures were reported as negative, positive, and mixed growth in 51%, 29%, and 20% of cases, respectively.

    “51%, 29%, and 20% in May 2023 (PostPM)”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    At QEII, PostPM urine cultures were reported as negative, positive, and mixed growth in 54%, 24%, and 22% of cases, respectively.

    “54%, 24%, and 22% for the PostPM phase”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    At Dynacare in May 2022 PrePM, urine cultures were reported as negative, positive, and mixed growth in 53%, 29%, and 18% of cases, respectively.

    “In May 2022 (PrePM), urine cultures were reported as negative, positive, and mixed growth in 53%, 29%, and 18% of cases, respectively”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationreasoning

    At QEII, implementation of PM alone did not reduce TTRR, reflecting delays between result availability and manual release.

    “At the tertiary care site (QEII), implementation of PM alone did not reduce TTRR, reflecting delays between result availability and manual release.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: The observed TTRR improvements can be attributed to the described AI and workflow interventions rather than to other temporal or site-specific factors.

  • May combine two claims.

    Interpretationown data

    While PM facilitated earlier availability of interpretable culture results at both sites, reductions in TTRR were only realized when paired with processes that supported earlier review or automated reporting.

    “While PM facilitated earlier availability of interpretable culture results at both sites, reductions in TTRR were only realized when paired with processes that supported earlier review or automated reporting.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: The observed TTRR improvements can be attributed to the described AI and workflow interventions rather than to other temporal or site-specific factors.

  • May combine two claims.

    Interpretationown data

    These findings support the role of AI not only in accelerating reporting but also in optimizing labor utilization in high-throughput environments.

    “These findings support the role of AI not only in accelerating reporting but also in optimizing labor utilization in high-throughput environments.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: The observed TTRR improvements can be attributed to the described AI and workflow interventions rather than to other temporal or site-specific factors.

  • May combine two claims.

    Interpretationown data

    This dual-site study demonstrates that AI-assisted culture interpretation improves TTRR in automated microbiology laboratories, but the magnitude of improvement depends on workflows that enable timely result release.

    “This dual-site study demonstrates that artificial intelligence-assisted culture interpretation improves TTRR in automated microbiology laboratories; however, the magnitude of improvement depends on workflows that enable timely result release.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: The observed TTRR improvements can be attributed to the described AI and workflow interventions rather than to other temporal or site-specific factors.

  • May combine two claims.

    Interpretationown data

    At QEII, integration of PM+ enabled automated release of negative and non-significant cultures and resulted in a measurable reduction in TTRR (~1.3 h) and a shift toward earlier reporting timepoints.

    “In contrast, integration of PM+, which enabled automated release of negative and non-significant cultures, resulted in a measurable reduction in TTRR (~1.3 h) and a shift toward earlier reporting timepoints.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: The observed TTRR improvements can be attributed to the described AI and workflow interventions rather than to other temporal or site-specific factors.

  • May combine two claims.

    Interpretationreasoning

    The TTRR improvement at Dynacare was not solely attributable to AI interpretation, but rather to concurrent workflow modifications that enabled earlier result review, including expansion of urine culture screening from evening-only to combined day and evening shifts.

    “This improvement was not solely attributable to AI interpretation, but rather to concurrent workflow modifications that enabled earlier result review, including expansion of urine culture screening from evening-only to combined day and evening shifts.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: The observed TTRR improvements can be attributed to the described AI and workflow interventions rather than to other temporal or site-specific factors.

  • May combine two claims.

    Interpretationreasoning

    The workflow changes at Dynacare facilitated earlier action on PM-generated results and underscore the importance of aligning laboratory workflows with AI-enabled data availability.

    “These changes facilitated earlier action on PM-generated results and underscore the importance of aligning laboratory workflows with AI-enabled data availability.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: The observed TTRR improvements can be attributed to the described AI and workflow interventions rather than to other temporal or site-specific factors.
20 of 20

  • The paper relies on this without support.

    This is the first dual-site evaluation of PM AI-driven algorithms and their impact on TTRR in North America.

    “This represents the first dual-site evaluation of PM AI-driven algorithms and their impact on TTRR in North America.”

  • The paper relies on this without support.

    Automation in clinical microbiology is essential to address persistent staffing shortages and increasing specimen volumes.

    “Automation in clinical microbiology has become essential to address persistent staffing shortages and increasing specimen volumes.”

  • The paper relies on this without support.

    The two study laboratories are representative of most clinical microbiology laboratories.

    “This study was performed in two different laboratories that differed not only in geography but also in scope and scale. The same ultimate benefits were seen in both institutions demonstrating that this technology is widely applicable for most laboratories.”

  • The paper relies on this without support.

    The observed changes in TTRR were attributable to PM/PM+ and workflow changes rather than other temporal or laboratory factors.

    “These findings indicate that AI-driven culture assessment reduces TTRR when coupled with processes that enable timely result release, either through automated reporting or optimized laboratory review workflows.”

  • The paper relies on this without support.

    The observed TTRR improvements can be attributed to the described AI and workflow interventions rather than to other temporal or site-specific factors.

    “This dual-site study demonstrates that artificial intelligence-assisted culture interpretation improves TTRR in automated microbiology laboratories; however, the magnitude of improvement depends on workflows that enable timely result release.”

  • The paper relies on this without support.

    The TTRR data were collected and measured consistently across PrePM, PostPM, and PM+ phases at each site.

    “The TTRR for PrePM, PostPM, and PostPM+ phases are shown in Fig. 1.”

  • The paper relies on this without support.

    The PM/PM+ culture interpretations are sufficiently accurate that final reports reflect valid culture results.

    “Urine cultures with a final result of no growth or normal urogenital flora of less than 10 colonies were automatically released and reported via PM+.”

  • The paper relies on this without support.

    There is an ongoing shortage of qualified medical laboratory technologists.

    “The ongoing shortage of qualified medical laboratory technologists and increasing specimen volumes underscore the importance of technologies that optimize efficiency without compromising quality.”

  • The paper relies on this without support.

    Specimen volumes are increasing.

    “The ongoing shortage of qualified medical laboratory technologists and increasing specimen volumes underscore the importance of technologies that optimize efficiency without compromising quality.”

  • The paper relies on this without support.

    The addition of AI software such as PhenoMATRIX enables continuous assessment of culture plates and prioritization of review.

    “The addition of AI software such as PhenoMATRIX enables continuous assessment of culture plates and prioritization of review.”

  • The paper relies on this without support.

    This study is the first dual-site evaluation in North America assessing the impact of PM AI-driven algorithms on urine culture TTRR.

    “This study represents, to our knowledge, the first dual-site evaluation in North America assessing the impact of PM AI-driven algorithms on urine culture TTRR.”
11 of 11

Cites

Flagged first, then by citations

Research works printed but not linked: 1

  1. Keyak C. 2019. Utilizing digital imaging to determine optimal incubation times for routine urine cultures. Med Lab Observ 51:40–44.

Cited by

Still being checked.

Authors
Davidson, R., Heinstein, C., Patriquin, G., Goneau, L. W., Brown, L. A., & Hill, B. J.
Affiliations
  • Dalhousie University (CA)
  • Queen Elizabeth II Health Sciences Centre (CA)
  • University of Toronto (CA)
  • Dynacare Laboratory
  • Copan Diagnostics, Inc.
ORCID
Venue
Journal of Clinical Microbiology
Publisher
American Society for Microbiology
Published
2026-08-12
Volume
64
Issue
8
Pages
e0035426-e0035426
ISSN
  • 0095-1137 (ISSN-L)
  • 1098-660X
DOI
10.1128/jcm.00354-26
PMC
PMC13463692
OpenAlex
W7167927033
Semantic Scholar
30a01217cd4731045fd3d1f0c576191af80aa5e3
Cited by
2
References
9
Citations by year
2026: 2
FWCI
7.63
Type
journal article
Language
en
Open access
Licence
Locations
  • Journal of Clinical Microbiology (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (submittedVersion, cc-by)
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 › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Keywords
artificial intelligence, urine culture, turnaround time, interpretation of results, laboratory automation, clinical microbiology laboratory, laboratory workflow optimization
MeSH terms
Artificial Intelligence, Canada, Humans, Microbiological Techniques, Time Factors, Workflow, Automation, Laboratory, Tertiary Care Centers

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