Evidence map›Paper›PMID 39870860›Full record

ArticleNPJ digital medicine2025

AI driven decision support reduces antibiotic mismatches and inappropriate use in outpatient urinary tract infections.

Shirley Shapiro Ben David, Roni Romano, Daniella Rahamim-Cohen, Joseph Azuri, Shira Greenfeld, Ben Gedassi, Uri Lerner

Abstract read
In one paragraph

Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

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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

7 authors.

Shirley Shapiro Ben DavidMaccabi Healthcare Services, Tel Aviv, 6812509, Israel. shirleys2@tauex.tau.ac.il.ORCID http://orcid.org/0000-0002-6115-5722
Roni RomanoMaccabi Healthcare Services, Tel Aviv, 6812509, Israel.
Daniella Rahamim-CohenMaccabi Healthcare Services, Tel Aviv, 6812509, Israel.
Joseph AzuriMaccabi Healthcare Services, Tel Aviv, 6812509, Israel.ORCID http://orcid.org/0000-0003-1049-9848
Shira GreenfeldMaccabi Healthcare Services, Tel Aviv, 6812509, Israel.
Ben GedassiMaccabi Healthcare Services, Tel Aviv, 6812509, Israel.
Uri LernerMaccabi Healthcare Services, Tel Aviv, 6812509, Israel.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Urinary tract infections (UTIs) often prompt empiric outpatient antibiotic prescriptions, risking mismatches. This study evaluates the impact of "UTI Smart-Set" (UTIS), an AI-driven decision-support tool, on prescribing patterns and mismatches in a large outpatient organization. UTIS integrates machine learning forecasts of antibiotic resistance, patient data, and guidelines into a user-friendly order set for UTI management. From 6/1/2021-8/31/2022, 171,010 UTI diagnoses were recorded, with UTIS used in 75,630 cases involving antibiotic prescriptions. Overall acceptance rate of UTIS recommendations was 66.0%. Among 19,287 cases with urine cultures, antibiotic mismatch rate was significantly lower when UTIS recommendations were followed (8.9% vs. 14.2%, p < 0.0001). Among women over 18, mismatch rate was 47.5% lower, and among women over 50, 55.6% lower (p < 0.001). Additionally, an overall reduction of 80.5% in ciprofloxacin usage (6.4% vs 32.9%, p < 0.0001) was observed. UTIS improved prescribing accuracy, reduced mismatches, and minimized quinolone use, highlighting AI's potential for personalized infection management.

Identifiers

PMID39870860
PMCPMC11772748

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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.