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. 2024 Dec 30:8:e57204.
doi: 10.2196/57204.

Allied Health Professionals' Perceptions of Artificial Intelligence in the Clinical Setting: Cross-Sectional Survey

Jane Hoffman  1   2 , Laetitia Hattingh  1   2   3 , Lucy Shinners  4 , Rebecca L Angus  1 , Brent Richards  1   5 , Ian Hughes  1   6 , Rachel Wenke  1
Affiliations

Allied Health Professionals' Perceptions of Artificial Intelligence in the Clinical Setting: Cross-Sectional Survey

Jane Hoffman et al. JMIR Form Res. .

Abstract

Background: Artificial intelligence (AI) has the potential to address growing logistical and economic pressures on the health care system by reducing risk, increasing productivity, and improving patient safety; however, implementing digital health technologies can be 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 health care.

Objective: This study aimed to explore AHP perceptions of AI and the opportunities and challenges for its use in health care delivery.

Methods: A cross-sectional survey was conducted at a health service in, Queensland, Australia, using the Shinners Artificial Intelligence Perception tool.

Results: A total of 231 (22.1%) participants from 11 AHPs responded to the survey. Participants were mostly younger than 40 years (157/231, 67.9%), female (189/231, 81.8%), working in a clinical role (196/231, 84.8%) with a median of 10 years' experience in their profession. Most participants had not used AI (185/231, 80.1%), had little to no knowledge about AI (201/231, 87%), and reported workforce knowledge and skill as the greatest challenges to incorporating AI in health care (178/231, 77.1%). Age (P=.01), profession (P=.009), and AI knowledge (P=.02) were strong predictors of the perceived professional impact of AI. AHPs generally felt unprepared for the implementation of AI in health care, with concerns about a lack of workforce knowledge on AI and losing valued tasks to AI. Prior use of AI (P=.02) and years of experience as a health care professional (P=.02) were significant predictors of perceived preparedness for AI. Most participants had not received education on AI (190/231, 82.3%) and desired training (170/231, 73.6%) and believed AI would improve health care. Ideas and opportunities suggested for the use of AI within the allied health setting were predominantly nonclinical, administrative, and to support patient assessment tasks, with a view to improving efficiencies and increasing clinical time for direct patient care.

Conclusions: Education and experience with AI are needed in health care 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 health care.

Keywords: AI; Australia; allied health; artificial intelligence; challenge; clinical setting; clinician; confirmatory factor analysis; cross sectional; delivery; digital health; health professional; healthcare; hospital; impact; linear regression; mHealth; medical professional; opportunity; perception; survey.

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Conflict of interest statement

Conflicts of Interest: None declared.

Figures

Figure 1
Figure 1
AHP responses to SHAIP questions about perceptions of AI in health care (N=231). AHP: allied health professional; AI: artificial intelligence; SHAIP: Shinners Artificial Intelligence Perception.
See this image and copyright information in PMC

References

    1. Allied health workforce data gap analysis, issues paper. Australian Government Department of Health and Aged Care. 2022. [2023-06-10]. https://www.health.gov.au/resources/publications/allied-health-workforce... .
    1. Topol EJ. The Topol Review: Preparing the Healthcare Workforce to Deliver the Digital Future. England: Health Education; 2019.
    1. Russell S. Artificial Intelligence: A Modern Approach. 3rd ed. New Jersey: Pearson; 2016.
    1. Powles J, Hodson H. Google DeepMind and healthcare in an age of algorithms. Health Technol (Berl) 2017;7(4):351–367. doi: 10.1007/s12553-017-0179-1. https://europepmc.org/abstract/MED/29308344 179 - DOI - PMC - PubMed
    1. Chomutare T, Tejedor M, Svenning TO, Marco-Ruiz L, Tayefi M, Lind K, Godtliebsen F, Moen A, Ismail L, Makhlysheva A, Ngo PD. Artificial intelligence implementation in healthcare: a theory-based scoping review of barriers and facilitators. Int J Environ Res Public Health. 2022;19(23):16359. doi: 10.3390/ijerph192316359. https://www.mdpi.com/resolver?pii=ijerph192316359 ijerph192316359 - DOI - PMC - PubMed

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