ArticleThe Canadian journal of hospital pharmacy2021
Article in The Canadian journal of hospital pharmacy, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Using machine learning or deep learning models in a hospital setting to detect inappropriate prescriptions: a systematic review.European journal of hospital pharmacy : science and practice · 2024Pooled it
- Evaluating Large Language Model-Supported Instructions for Medication Use: First Steps Toward a Comprehensive Model.Mayo Clinic proceedings. Digital health · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundArtificial intelligence (AI) can be described as an advanced technology in which machines display a certain form of intelligence.
objectivesThe primary objective was to perform a narrative review of studies evaluating the feasibility and impact of AI in pharmacy. The secondary objective was to create a mind map of AI in health care. DATA SOURCES: Four databases were consulted: PubMed, Medline, Embase, and CINAHL. STUDY SELECTION AND DATA EXTRACTION: Four search strategies were developed. Initial selection of articles was based on their titles and abstracts; the full texts were then evaluated by a research assistant, with review by a pharmacist. Articles were included if they described or evaluated the feasibility or impact of AI in pharmacy. DATA SYNTHESIS: A total of 362 articles were identified by the literature review, of which 18 met the inclusion criteria. The studies were mainly conducted in the United States (72%, 13/18). The article topics were, in decreasing order, prediction of response to treatments and adverse effects (33%, 6/18), patient prioritization (28%, 5/18), treatment adherence (22%, 4/18), validation of prescriptions and electronic prescription (17%, 3/18), and other themes (e.g., diagnosis, costs, insurance, and verification of syringe volume).
conclusionsThis narrative review highlighted 18 studies evaluating the feasibility and impact of AI in pharmacy. The studies used various methodologies in different settings, both retail pharmacies and hospital pharmacies. It is still too soon to predict the implications of AI for pharmacy, but these studies emphasize the importance of attention in this area.
Indexed as
Identifiers
33896953PMC8042195What OpenQuestion holds
Registered trials
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.