Evidence map›Paper›PMID 41234132›Full record

ArticleInfection control and hospital epidemiology2026

Artificial intelligence enhances genomic surveillance in healthcare outbreak investigations.

Alexander Sundermann, Jieshi Chen, Melissa Saul, Kathleen Shutt, Marissa Griffith, Graham Snyder, Lora Lee Pless, Artur Dubrawski, Lee Harrison

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Infection control and hospital epidemiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–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

2 citing papers in PubMed.

  1. Review
  2. 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

9 authors.

Alexander SundermannMicrobial Genomic Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburghhttps://ror.org/01an3r305, Pittsburgh, PA, USA.ORCID https://orcid.org/0000-0002-2439-6287
Jieshi ChenAuton Lab, Carnegie Mellon University, Pittsburgh, PA, USA.ORCID https://orcid.org/0000-0003-2660-4913
Melissa SaulDepartment of Medicine, University of Pittsburgh, Pittsburgh, PA, USA.ORCID https://orcid.org/0000-0002-6712-5102
Kathleen ShuttMicrobial Genomic Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburghhttps://ror.org/01an3r305, Pittsburgh, PA, USA.ORCID https://orcid.org/0000-0003-3376-6152
Marissa GriffithMicrobial Genomic Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburghhttps://ror.org/01an3r305, Pittsburgh, PA, USA.
Graham SnyderDivision of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.ORCID https://orcid.org/0000-0001-5562-8880
Lora Lee PlessMicrobial Genomic Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburghhttps://ror.org/01an3r305, Pittsburgh, PA, USA.
Artur DubrawskiAuton Lab, Carnegie Mellon University, Pittsburgh, PA, USA.
Lee HarrisonMicrobial Genomic Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburghhttps://ror.org/01an3r305, Pittsburgh, PA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundOutbreak investigation and control are critical for preventing the spread of infectious diseases in healthcare settings. Traditional methods rely on manual processes, which are time-consuming and limited in scope. Whole genome sequencing (WGS) surveillance improves outbreak detection but still requires extensive manual chart reviews to identify transmission routes. Integrating artificial intelligence (AI) may enhance the efficiency and accuracy of these investigations.

methodsWe evaluated an AI tool developed to streamline healthcare outbreak investigations detected by the Enhanced Detection System for Healthcare-associated Transmission (EDS-HAT). For outbreaks detected between November 2021 and November 2023, multiple data elements were extracted from electronic health records (EHR) for all patients. The AI algorithm was applied to identify transmission routes, and its performance was assessed against expert manual reviews. Key measures included additional transmission routes identified and sensitivity.

resultsData from 172 outbreaks involving 476 case patients were analyzed. The AI tool identified 37 transmission routes that were missed by manual review, including procedures and provider routes. The algorithm achieved a sensitivity of 76.0% (95% confidence interval [CI] 71.1%-81.1%) compared to manual review, increasing to 91.7% (95% CI 87.7%-94.7%) after accounting for transmission at other facilities and downstream exposures.

conclusionThe EDS-HAT AI tool significantly improved outbreak investigations by automating the identification of transmission routes, both with concordant findings of manual review as well as finding additional routes of transmission missed by traditional chart review. AI with genomic surveillance has the potential to optimize outbreak detection and investigation to streamline interventions in healthcare settings.

Indexed as

Artificial IntelligenceCross InfectionDisease OutbreaksAlgorithmsElectronic Health RecordsGenomicsHumansWhole Genome Sequencing

Identifiers

PMID41234132
PMCPMC12926331

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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.