Evidence map›Paper›PMID 37555012›Full record

ArticleSaudi pharmaceutical journal : SPJ : the official publication of the Saudi Pharmaceutical Society2023

Artificial intelligence in pharmacy practice: Attitude and willingness of the community pharmacists and the barriers for its implementation.

Anan S Jarab, Walid Al-Qerem, Karem H Alzoubi, Haneen Obeidat, Shrouq Abu Heshmeh, Tareq L Mukattash, Yara A Naser, Ahmad Al-Azayzih

Abstract read
In one paragraph

Article in Saudi pharmaceutical journal : SPJ : the official publication of the Saudi Pharmaceutical Society, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed, 2 pooled it
–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

18 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
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  15. Machine Learning Operations in Health Care: A Scoping Review.Mayo Clinic proceedings. Digital health · 2024
    Review
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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

8 authors.

Anan S JarabDepartment of Clinical Pharmacy, Faculty of Pharmacy, Jordan University of Science and Technology. P.O. Box 3030. Irbid 22110, Jordan.
Walid Al-QeremDepartment of Pharmacy, Faculty of Pharmacy, Al-Zaytoonah University of Jordan. P.O. Box 130, Amman 11733, Jordan.
Karem H AlzoubiDepartment of Pharmacy Practice and Pharmacotherapeutics, College of Pharmacy, University of Sharjah, Sharjah, UAE.
Haneen ObeidatDepartment of Clinical Pharmacy, Faculty of Pharmacy, Jordan University of Science and Technology. P.O. Box 3030. Irbid 22110, Jordan.
Shrouq Abu HeshmehDepartment of Clinical Pharmacy, Faculty of Pharmacy, Jordan University of Science and Technology. P.O. Box 3030. Irbid 22110, Jordan.
Tareq L MukattashDepartment of Clinical Pharmacy, Faculty of Pharmacy, Jordan University of Science and Technology. P.O. Box 3030. Irbid 22110, Jordan.
Yara A NaserSchool of Pharmacy, Queen's University Belfast, Medical Biology Centre, 97 Lisburn Road, Belfast BT9 7BL, Northern Ireland, UK.
Ahmad Al-AzayzihDepartment of Clinical Pharmacy, Faculty of Pharmacy, Jordan University of Science and Technology. P.O. Box 3030. Irbid 22110, Jordan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Artificial intelligence (AI) is the capacity of machines to perform tasks that ordinarily require human intelligence. AI can be utilized in various pharmaceutical applications with less time and cost. Objectives: To evaluate community pharmacists' willingness and attitudes towards the adoption of AI technology at pharmacy settings, and the barriers that hinder AI implementation. Methods: This cross-sectional study was conducted among community pharmacists in Jordan using an online-based questionnaire. In addition to socio-demographics, the survey assessed pharmacists' willingness, attitudes, and barriers to AI adoption in pharmacy. Binary logistic regression was conducted to find the variables that are independently associated with willingness and attitude towards AI implementation. Results: The present study enrolled 401 pharmacist participants. The median age was 30 (29-33) years. Most of the pharmacists were females (66.6%), had bachelor's degree of pharmacy (56.1%), had low-income (54.6%), and had one to five years of experience (35.9%). The pharmacists showed good willingness and attitude towards AI implementation at pharmacy (n = 401). The most common barriers to AI were lack of AI-related software and hardware (79.2%), the need for human supervision (76.4%), and the high running cost of AI (74.6%). Longer weekly working hours (attitude: OR = 1.072, 95% C.I (1.040-1.104), P < 0.001, willingness: OR = 1.069, 95% Cl. 1.039-1.009, P-value = 0.011), and higher knowledge of AI applications (attitude: OR = 1.697, 95%Cl (1.327-2.170), willingness: OR = 1.790, 95%Cl. (1.396-2.297), P-value < 0.001 for both) were significantly associated with better willingness and attitude towards AI, whereas greater years of experience (OR = 20.859, 95% Cl (5.241-83.017), P-value < 0.001) were associated with higher willingness. In contrast, pharmacists with high income (OR = 0.382, 95% Cl. (0.183-0.795), P-value = 0.010), and those with<10 visitors (OR = 0.172, 95% Cl. (0.035-0.838), P-value = 0.029) or 31-50 visitors daily (OR = 0.392, 95% Cl. (0.162-0.944), P-value = 0.037) had less willingness to adopt AI. Conclusions: Despite the pharmacists' positive willingness and attitudes toward AI, several barriers were identified, highlighting the importance of providing educational and training programs to improve pharmacists' knowledge of AI, as well as ensuring adequate funding support to overcome the issue of AI high operating costs.

Indexed as

Artificial intelligenceAttitudeBarrierJordanPharmacistWillingness

Identifiers

PMID37555012
PMCPMC10404546

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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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.