Evidence map›Paper›PMID 41216923›Full record

ArticleInfection control and hospital epidemiology2026

Using secure artificial intelligence agents integrated within the electronic medical record for the evaluation of blood culture appropriateness-Northern California, 2025.

Guillermo Rodriguez-Nava, Timothy Keyes, Nerissa Ambers, Eugenia Miranti, Erika Paola Viana-Cardenas, Wajeeha Tariq, Mindy Marie Sampson, Jorge Luis Salinas

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 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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.

Guillermo Rodriguez-NavaDivision of Infectious Diseases & Geographic Medicine, Stanford University School of Medicinehttps://ror.org/03mtd9a03, Stanford, CA, USA.ORCID https://orcid.org/0000-0001-9826-7050
Timothy KeyesDepartment of Medicine, Stanford University School of Medicine, Stanford, CA, USA.ORCID https://orcid.org/0000-0003-0423-9679
Nerissa AmbersDepartment of Nursing Information & Informatics, Stanford Health Care, Stanford, CA, USA.ORCID https://orcid.org/0009-0008-9878-1824
Eugenia MirantiDivision of Infectious Diseases & Geographic Medicine, Stanford University School of Medicinehttps://ror.org/03mtd9a03, Stanford, CA, USA.ORCID https://orcid.org/0000-0002-0654-8937
Erika Paola Viana-CardenasDivision of Infectious Diseases & Geographic Medicine, Stanford University School of Medicinehttps://ror.org/03mtd9a03, Stanford, CA, USA.ORCID https://orcid.org/0000-0002-6399-890X
Wajeeha TariqDivision of Infectious Diseases & Geographic Medicine, Stanford University School of Medicinehttps://ror.org/03mtd9a03, Stanford, CA, USA.ORCID https://orcid.org/0000-0001-8387-4309
Mindy Marie SampsonDivision of Infectious Diseases & Geographic Medicine, Stanford University School of Medicinehttps://ror.org/03mtd9a03, Stanford, CA, USA.ORCID https://orcid.org/0000-0001-5724-5875
Jorge Luis SalinasDivision of Infectious Diseases & Geographic Medicine, Stanford University School of Medicinehttps://ror.org/03mtd9a03, Stanford, CA, USA.ORCID https://orcid.org/0000-0002-9193-820X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We evaluated large language model (LLM)-based agents integrated with the electronic medical record to assess blood culture appropriateness. While sensitivity was high, specificity remained low. Performance was shaped by prompt phrasing, sycophantic behavior, and semantic triggers, reflecting both the potential and limitations of LLMs in real-world clinical decision support.

Indexed as

Artificial IntelligenceBlood CultureDecision Support Systems, ClinicalElectronic Health RecordsCaliforniaHumansSensitivity and Specificity

Identifiers

PMID41216923
PMCPMC12926330

What OpenQuestion holds

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LicenceCC BY
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.