Evidence map›Paper›PMID 31949893›Full record

ArticleHealth information science and systems2020

Predicting healthcare professionals' intention to use poison information system in a Malaysian public hospital.

Yulita Hanum P Iskandar, Gogilavani Subramaniam, Mohamed Isa Abd Majid, Adilah Mohamed Ariff, Gururajaprasad Kaggal Lakshmana Rao

Abstract read
In one paragraph

Article in Health information science and systems, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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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

7 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

5 authors.

Yulita Hanum P Iskandar1Graduate School of Business, Universiti Sains Malaysia, 11800 USM Penang, Malaysia.ORCID 0000-0002-8037-5800
Gogilavani Subramaniam1Graduate School of Business, Universiti Sains Malaysia, 11800 USM Penang, Malaysia.
Mohamed Isa Abd Majid2National Poison Centre, Universiti Sains Malaysia, 11800 USM Penang, Malaysia.
Adilah Mohamed Ariff2National Poison Centre, Universiti Sains Malaysia, 11800 USM Penang, Malaysia.
Gururajaprasad Kaggal Lakshmana Rao3Craniofacial and Biomaterial Science Cluster, Advanced Medical and Dental Institute, Universiti Sains Malaysia, 13200 Penang, Malaysia.ORCID 0000-0002-5697-2402

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThe main objective of this paper is to determine the predicting factors that influence the intention to use Poison Information System (PIS) among healthcare professionals.

methodsA quantitative approach was applied, using a five-point Likert scale questionnaire, adapted from previous studies. Data were collected from 167 healthcare professionals working for Malaysian Public Hospitals in Penang. Smart Partial Least Square (PLS) version 3.2.7 were used to analyse the proposed relationships.

resultsThe results indicated that attitude and computer anxiety had a significant positive relation to the intention to use PIS among healthcare professionals wherein computer knowledge was found to have had a significant relationship with attitude and computer habit. Apart from that, technical support and training had a positive relationship with perceived ease of use. Surprisingly, computer habit, perceived usefulness, perceived ease of use, compatibility and facilitating condition did not significantly influence intention to use PIS.

conclusionThe results of this study provided useful insights for healthcare agencies to understand the underlying elements that could improve the poison information management. The results proved that attitude and computer anxiety were critical factors among healthcare professionals managing poisoning cases in a highly stressful and unpredictable work environment. These factors must, therefore, be considered before implementing PIS in managing poisoning cases. The study also provided an understanding of how to improve system development by utilising the end user's expectation on the implementation of the system.

Indexed as

HealthcareIntention to usePoison information system (PIS)Technology acceptance model (TAM)

Identifiers

PMID31949893
PMCPMC6942115

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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.