Evidence map›Paper›PMID 40352326›Full record

ArticleFrontiers in digital health2025

Implementation of artificial intelligence-based decision support systems for antibiotic prescribing in hospitals: a Delphi study.

Pinar Tokgöz, Joanna Albrecht, Christoph Dockweiler

Abstract read
In one paragraph

Article in Frontiers in digital health, 2025. 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. 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

3 authors.

Pinar TokgözFaculty of Arts and Humanities, Professorship of Digital Public Health, University of Siegen, Siegen, Germany.
Joanna AlbrechtFaculty of Arts and Humanities, Professorship of Digital Public Health, University of Siegen, Siegen, Germany.
Christoph DockweilerFaculty of Arts and Humanities, Professorship of Digital Public Health, University of Siegen, Siegen, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Numerous initiatives against antimicrobial resistance have been initiated in recent years. Decision support systems (DSSs) based on artificial intelligence (AI) provide new opportunities for automating antibiotic therapy in hospitals. While AI-based DSSs may improve antimicrobial use and patient outcomes and reduce healthcare costs, the challenges associated with their implementation, optimization, and adoption cannot be ignored. Methods: A Delphi study was conducted to investigate factors influencing the implementation of AI-based DSSs in the hospital setting. Results: The study included 36 experts with perspectives on the hospital setting and DSS development. A consensus was reached on the importance of 34 factors and the ranking as well as assessment of current realization of implementation factors revealed important starting points for implementation strategies. Discussion: The study results indicate that whilst there are multiple factors of importance in DSS implementation, some factors, as e.g., promoting application- and user-orientated development of DSSs, establishing user-friendly organizational structures, and fulfilling demands of trust, transparency, and responsibility through sensitization and education on organizational but also legal level should gain more attention. In addition, two factors did not reach a consensus in terms of importance, indicating that it may not be practical to consider all factors of importance when implementing AI-based DSSs in the hospital setting.

Indexed as

antibiotic prescribingartificial intelligenceclinical practicedecision supportimplementation

Identifiers

PMID40352326
PMCPMC12062133

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LicenceCC BY
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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.