Background: The integration of artificial intelligence (AI) into our digital healthcare system is seen as a significant strategy to contain Australia’s rising healthcare costs, support clinical decision making, manage chronic disease burden and support our ageing population. With the increasing roll-out of ‘digital hospitals’, electronic medical records, new data capture and analysis technologies, as well as a digitally enabled health consumer, the Australian healthcare workforce is required to become digitally literate to manage the significant changes in the healthcare landscape. To ensure that new innovations such as AI are inclusive of clinicians, an understanding of how the technology will impact the healthcare professions is imperative. Method: In order to explore the complex phenomenon of healthcare professionals’ understanding and experiences of AI use in the delivery of healthcare, an integrative review inclusive of quantitative and qualitative studies was undertaken in June 2018. Results: One study met all inclusion criteria. This study was an observational study which used a questionnaire to measure healthcare professional’s intrinsic motivation in adoption behaviour when using an artificially intelligent medical diagnosis support system (AIMDSS). Discussion: The study found that healthcare professionals were less likely to use AI in the delivery of healthcare if they did not trust the technology or understand how it was used to improve patient outcomes or the delivery of care which is specific to the healthcare setting. The perception that AI would replace them in the healthcare setting was not evident. This may be due to the fact that AI is not yet at the forefront of technology use in healthcare setting. More research is needed to examine the experiences and perceptions of healthcare professionals using AI in the delivery of healthcare.

Objective The aim of this study was to draw upon the collective knowledge of experts in the fields of health and technology to develop a questionnaire that measured healthcare professionals’ perceptions of Artificial Intelligence (AI). Methods The panel for this study were carefully selected participants who demonstrated an interest and/or involvement in AI from the fields of health or information technology. Recruitment was accomplished via email which invited the panel member to participate and included study and consent information. Data were collected from three rounds in the form of an online survey, an online group meeting and email communication. A 75% median threshold was used to define consensus. Results Between January and March 2019, five healthcare professionals and three IT experts participated in three rounds of study to reach consensus on the structure and content of the questionnaire. In Round 1 panel members identified issues about general understanding of AI and achieved consensus on nine draft questionnaire items. In Round 2 the panel achieved consensus on demographic questions and comprehensive group discussion resulted in the development of two further questionnaire items for inclusion. In a final e-Delphi round, a draft of the final questionnaire was distributed via email to the panel members for comment. No further amendments were put forward and 100% consensus was achieved. Conclusion A modified e-Delphi method was used to validate and develop a questionnaire to explore healthcare professionals’ perceptions of AI. The e-Delphi method was successful in achieving consensus from an interdisciplinary panel of experts from health and IT. Further research is recommended to test the reliability of this questionnaire.

Objective 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 ( n = 252) from a regional health district in Australia. Methods and Results 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 ‘ Professional impact of AI’ (α = .832) and Factor two represented ‘ Preparedness for AI’ (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. Conclusions 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.

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