Evidence map›Paper›PMID 39816258›Full record

ReviewFrontiers in antibiotics2024

Automated extraction of standardized antibiotic resistance and prescription data from laboratory information systems and electronic health records: a narrative review.

Alice Cappello, Ylenia Murgia, Daniele Roberto Giacobbe, Sara Mora, Roberta Gazzarata, Nicola Rosso, Mauro Giacomini, Matteo Bassetti

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Frontiers in antibiotics, 2024. 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. FromFrontiers in medicine · 2025
    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.

Alice Cappello *Clinica Malattie Infettive, IRCCS Ospedale Policlinico San Martino, Genoa, Italy.
Ylenia Murgia *Department of Informatics, Bioengineering, Robotics and System Engineering (DIBRIS), University of Genoa, Genoa, Italy.
Daniele Roberto Giacobbe *Clinica Malattie Infettive, IRCCS Ospedale Policlinico San Martino, Genoa, Italy.
Sara MoraUO Information and Communication Technologies (ICT), IRCCS Ospedale Policlinico San Martino, Genoa, Italy.
Roberta GazzarataHealthropy, Savona, Italy.
Nicola RossoUO Information and Communication Technologies (ICT), IRCCS Ospedale Policlinico San Martino, Genoa, Italy.
Mauro Giacomini *Department of Informatics, Bioengineering, Robotics and System Engineering (DIBRIS), University of Genoa, Genoa, Italy.
Matteo Bassetti *Clinica Malattie Infettive, IRCCS Ospedale Policlinico San Martino, Genoa, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Antimicrobial resistance in bacteria has been associated with significant morbidity and mortality in hospitalized patients. In the era of big data and of the consequent frequent need for large study populations, manual collection of data for research studies on antimicrobial resistance and antibiotic use has become extremely time-consuming and sometimes impossible to be accomplished by overwhelmed healthcare personnel. In this review, we discuss relevant concepts pertaining to the automated extraction of antibiotic resistance and antibiotic prescription data from laboratory information systems and electronic health records to be used in clinical studies, starting from the currently available literature on the topic. Leveraging automatic extraction and standardization of antimicrobial resistance and antibiotic prescription data is an tremendous opportunity to improve the care of future patients with severe infections caused by multidrug-resistant organisms, and should not be missed.

Indexed as

antibiotic resistanceantimicrobial stewardshipautomated extractionEHRLIS

Identifiers

PMID39816258
PMCPMC11731964

What OpenQuestion holds

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LicenceCC BY
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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.