Evidence map›Paper›PMID 40858969›Full record

ArticleScientific reports2025

Artificial intelligence-assisted quality control circles led by clinical pharmacists to improve the rational use of parenteral proton pump inhibitors among hospitalised patients.

Ming Zhang, Wei-Cheng Ma, Wen-Yue He

Abstract read
In one paragraph

Article in Scientific reports, 2025. 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. Article
  2. 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

3 authors.

Ming ZhangDepartment of Pharmacy, Ningbo Yinzhou No.2 Hospital, Ningbo, 315100, Zhejiang, China.
Wei-Cheng MaDepartment of Pharmacy, Ningbo Yinzhou No.2 Hospital, Ningbo, 315100, Zhejiang, China.
Wen-Yue HeDepartment of Pharmacy, Ningbo Yinzhou No.2 Hospital, Ningbo, 315100, Zhejiang, China. wyh710331@163.com.

Funding

Natural Science Foundation of Ningbo Municipality 2024J363
6 · The paper itself

Abstract

Proton pump inhibitors (PPIs) are a class of drugs that inhibit gastric acid secretion and are commonly overused in clinical practice. We developed a quality control circle (QCC) assisted by artificial intelligence (AI) and led by clinical pharmacists to improve rational PPI use among patients in our hospital. A QCC team was established to follow QCC procedures. The root causes of inappropriate use were identified via AI-assisted brainstorming, and corresponding strategies were developed. The percentages of rational injectable PPI use before and after QCC implementation were calculated and compared. Radar charts were used to evaluate the capabilities of QCC members. Following QCC implementation, the percentage of rational injectable PPI use among hospitalised patients in our hospital increased from 66.51 to 93.43%, with a goal achievement percentage of 126.92% and a progression percentage of 40.47%. The increase in the percentage of rational PPI use was statistically significant (P < 0.001). Team cohesion, responsibility and proficiency in QCC techniques improved following QCC implementation. The standardised process for using injectable PPIs became more regulated following this process. AI-assisted, clinical pharmacist-led QCCs represent an efficient management tool for improving rational injectable PPI use among hospitalised patients, thereby promoting rational drug use.

Indexed as

Artificial IntelligencePharmacistsProton Pump InhibitorsFemaleHospitalizationHumansMalePharmacy Service, HospitalQuality ControlProton Pump InhibitorsArtificial intelligence (AI)Proton pump inhibitors (PPIs)Quality control circles (QCCs)Rational drug use

Identifiers

PMID40858969
PMCPMC12381210

What OpenQuestion holds

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Registered trials

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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.