Shinners Lucy, Grace Sandra, Smith Stuart, Stephens Alexandre, Aggar Christina
(Faculty of Health), Southern Cross University, Australia.
(Northern NSW Local Health District) NSW Health, Australia.
Digit Health. 2022 Feb 7;8:20552076221078110. doi: 10.1177/20552076221078110. eCollection 2022 Jan-Dec.
There is an urgent need to prepare the healthcare workforce for the implementation of artificial intelligence (AI) into the healthcare setting. Insights into workforce perception of AI could identify potential challenges that an organisation may face when implementing this new technology. The aim of this study was to psychometrically evaluate and pilot the Shinners Artificial Intelligence Perception (SHAIP) questionnaire that is designed to explore healthcare professionals' perceptions of AI. Instrument validation was achieved through a cross-sectional study of healthcare professionals ( = 252) from a regional health district in Australia.
Exploratory factor analysis was conducted and analysis yielded a two-factor solution consisting of 10 items and explained 51.7% of the total variance. Factor one represented perceptions of '' (α = .832) and Factor two represented '' (α = .632). An analysis of variance indicated that 'use of AI' had a significant effect on healthcare professionals' perceptions of both factors. 'Discipline' had a significant effect on Allied Health professionals' perception of Factor one and low mean scale score across all disciplines suggests that all disciplines perceive that they are not prepared for AI.
The results of this study provide preliminary support for the SHAIP tool and a two-factor solution that measures healthcare professionals' perceptions of AI. Further testing is needed to establish the reliability or re-modelling of Factor 2 and the overall performance of the SHAIP tool as a global instrument.
迫切需要让医疗保健人员为在医疗环境中实施人工智能(AI)做好准备。深入了解工作人员对人工智能的看法,可以识别组织在实施这项新技术时可能面临的潜在挑战。本研究的目的是对旨在探索医疗保健专业人员对人工智能看法的辛纳斯人工智能认知(SHAIP)问卷进行心理测量评估和试点。通过对澳大利亚一个地区卫生区的252名医疗保健专业人员进行横断面研究,实现了工具验证。
进行探索性因素分析,分析得出一个由10个项目组成的双因素解决方案,解释了总方差的51.7%。因素一代表对“……”的看法(α = 0.832),因素二代表“……”(α = 0.632)。方差分析表明,“人工智能的使用”对医疗保健专业人员对两个因素的看法有显著影响。“学科”对专职医疗专业人员对因素一的看法有显著影响,所有学科的平均量表得分较低表明所有学科都认为自己没有为人工智能做好准备。
本研究结果为SHAIP工具和一个测量医疗保健专业人员对人工智能看法的双因素解决方案提供了初步支持。需要进一步测试以确定因素2的可靠性或重新建模以及SHAIP工具作为一种通用工具的整体性能。