Evidence map›Paper›PMID 32102900›Full record

ReviewClinical microbiology reviews2020

Consolidation of Clinical Microbiology Laboratories and Introduction of Transformative Technologies.

Olivier Vandenberg, Géraldine Durand, Marie Hallin, Andreas Diefenbach, Vanya Gant, Patrick Murray, Zisis Kozlakidis, Alex van Belkum

Abstract readReview
In one paragraph

Review in Clinical microbiology reviews, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

26 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Review
  6. Review
  7. Review
  8. Deep Learning-Based Rapid Identification ofRisk management and healthcare policy · 2026
    Article
  9. Article
  10. How AI can help us beat AMR.npj antimicrobials and resistance · 2025
    Review
  11. Review
  12. Article
  13. Review
  14. Review
  15. Article
  16. Article
  17. Article
  18. Article
  19. Review
  20. Considerations for diagnostic COVID-19 tests.Nature reviews. Microbiology · 2021
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Olivier VandenbergInnovation and Business Development Unit, LHUB-ULB, Groupement Hospitalier Universitaire de Bruxelles (GHUB), Université Libre de Bruxelles, Brussels, Belgium olivier.vandenberg@lhub-ulb.be.
Géraldine DurandbioMérieux, Microbiology Research and Development, La Balme Les Grottes, France.
Marie HallinDepartment of Microbiology, LHUB-ULB, Groupement Hospitalier Universitaire de Bruxelles (GHUB), Université Libre de Bruxelles, Brussels, Belgium.
Andreas DiefenbachDepartment of Microbiology, Infectious Diseases and Immunology, Charité-Universitätsmedizin Berlin, Berlin, Germany.
Vanya GantDepartment of Clinical Microbiology, University College London Hospitals NHS Foundation Trust, London, United Kingdom.
Patrick MurrayBD Life Sciences Integrated Diagnostic Solutions, Scientific Affairs, Sparks, Maryland, USA.
Zisis Kozlakidis *Laboratory Services and Biobank Group, International Agency for Research on Cancer, World Health Organization, Lyon, France.
Alex van Belkum *bioMérieux, Open Innovation and Partnerships, La Balme Les Grottes, France.

Funding

World Health Organization 001
6 · The paper itself

Abstract

Clinical microbiology is experiencing revolutionary advances in the deployment of molecular, genome sequencing-based, and mass spectrometry-driven detection, identification, and characterization assays. Laboratory automation and the linkage of information systems for big(ger) data management, including artificial intelligence (AI) approaches, also are being introduced. The initial optimism associated with these developments has now entered a more reality-driven phase of reflection on the significant challenges, complexities, and health care benefits posed by these innovations. With this in mind, the ongoing process of clinical laboratory consolidation, covering large geographical regions, represents an opportunity for the efficient and cost-effective introduction of new laboratory technologies and improvements in translational research and development. This will further define and generate the mandatory infrastructure used in validation and implementation of newer high-throughput diagnostic approaches. Effective, structured access to large numbers of well-documented biobanked biological materials from networked laboratories will release countless opportunities for clinical and scientific infectious disease research and will generate positive health care impacts. We describe why consolidation of clinical microbiology laboratories will generate quality benefits for many, if not most, aspects of the services separate institutions already provided individually. We also define the important role of innovative and large-scale diagnostic platforms. Such platforms lend themselves particularly well to computational (AI)-driven genomics and bioinformatics applications. These and other diagnostic innovations will allow for better infectious disease detection, surveillance, and prevention with novel translational research and optimized (diagnostic) product and service development opportunities as key results.

Indexed as

AnimalsArtificial IntelligenceAutomationClinical Laboratory ServicesClinical Laboratory TechniquesCommunicable DiseasesHumansartificial intelligenceautomationbiobankingclinical microbiologyconsolidationsurveillance

Identifiers

PMID32102900
PMCPMC7048017

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.