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Allied health professionals’ perceptions of artificial intelligence in the clinical setting: a cross sectional survey (Preprint)
Journal article   Open access   Peer reviewed

Allied health professionals’ perceptions of artificial intelligence in the clinical setting: a cross sectional survey (Preprint)

Jane Hoffman, Laetitia Hattingh, Lucy Shinners, Rebecca L. Angus, Brent Richards, Ian Hughes and Rachel Wenke
JMIR formative research, Vol.8, e57204
30/12/2024
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Abstract

Background: Artificial Intelligence (AI) has the potential to address growing logistical and economic pressures on the healthcare system by reducing risk, increasing productivity and improving patient safety; however digital transformation is disruptive. Workforce perception is a powerful indicator of technology use and acceptance, however, there is little research available on the perceptions of allied health professionals (AHPs) toward AI in healthcare. Objective: This study aimed to explore AHP perceptions of AI and the opportunities and challenges for its use healthcare delivery. Methods: A cross sectional survey was conducted at a health service in, Queensland, Australia utilising the Shinners Artificial Intelligence Perception tool. Results: 231 participants from eleven allied health professions responded to the survey. Participants were mostly (67.9%) under 40 years old, female (81.8%), working in a clinical role (84.8%) with a median of 10 years’ experience in their profession. Most participants had not used AI (80.1%), had little to no knowledge about AI (87.0%), and reported workforce knowledge and skill as the greatest challenges to incorporating AI in healthcare (77.1%). Age, profession, and AI knowledge were strong predictors of perceived professional impact of AI. AHPs generally felt unprepared for the implementation of AI in healthcare, with concerns about a lack of workforce knowledge on AI and losing valued tasks to AI. Prior use of AI and years of experience as a healthcare professional were significant predictors of perceived preparedness for AI. Most participants had not received education on AI (82.3%) and desired training (73.6%), believing AI will improve healthcare. Ideas and opportunities suggested for use of AI within the allied health setting were predominantly non-clinical, administrative and to support patient assessment tasks, with a view to improving efficiencies and increase clinical time for direct patient care. Conclusions: Education and experience with AI are needed in healthcare to support its implementation across allied health, the second largest workforce in health. Industry and academic partnerships with clinicians should not be limited to AHPs with high AI literacy as clinicians across all knowledge levels can identify many opportunities for AI in healthcare.

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