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BMC Health Services Research
Published in: BMC Health Services Research 1/2024

Open Access 01-12-2024 | Artificial Intelligence | Research

Applying the UTAUT2 framework to patients’ attitudes toward healthcare task shifting with artificial intelligence

Authors: Weiting Huang, Wen Chong Ong, Mark Kei Fong Wong, Eddie Yin Kwee Ng, Tracy Koh, Chanchal Chandramouli, Choon Ta Ng, Yoran Hummel, Feiqiong Huang, Carolyn Su Ping Lam, Jasper Tromp

Published in: BMC Health Services Research | Issue 1/2024

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Abstract

Background

Increasing patient loads, healthcare inflation and ageing population have put pressure on the healthcare system. Artificial intelligence and machine learning innovations can aid in task shifting to help healthcare systems remain efficient and cost effective. To gain an understanding of patients’ acceptance toward such task shifting with the aid of AI, this study adapted the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), looking at performance and effort expectancy, facilitating conditions, social influence, hedonic motivation and behavioural intention.

Methods

This was a cross-sectional study which took place between September 2021 to June 2022 at the National Heart Centre, Singapore. One hundred patients, aged ≥ 21 years with at least one heart failure symptom (pedal oedema, New York Heart Association II-III effort limitation, orthopnoea, breathlessness), who presented to the cardiac imaging laboratory for physician-ordered clinical echocardiogram, underwent both echocardiogram by skilled sonographers and the experience of echocardiogram by a novice guided by AI technologies. They were then given a survey which looked at the above-mentioned constructs using the UTAUT2 framework.

Results

Significant, direct, and positive effects of all constructs on the behavioral intention of accepting the AI-novice combination were found. Facilitating conditions, hedonic motivation and performance expectancy were the top 3 constructs. The analysis of the moderating variables, age, gender and education levels, found no impact on behavioral intention.

Conclusions

These results are important for stakeholders and changemakers such as policymakers, governments, physicians, and insurance companies, as they design adoption strategies to ensure successful patient engagement by focusing on factors affecting the facilitating conditions, hedonic motivation and performance expectancy for AI technologies used in healthcare task shifting.
Appendix
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Metadata
Title
Applying the UTAUT2 framework to patients’ attitudes toward healthcare task shifting with artificial intelligence
Authors
Weiting Huang
Wen Chong Ong
Mark Kei Fong Wong
Eddie Yin Kwee Ng
Tracy Koh
Chanchal Chandramouli
Choon Ta Ng
Yoran Hummel
Feiqiong Huang
Carolyn Su Ping Lam
Jasper Tromp
Publication date
01-12-2024
Publisher
BioMed Central
Published in
BMC Health Services Research / Issue 1/2024
Electronic ISSN: 1472-6963
DOI
https://doi.org/10.1186/s12913-024-10861-z

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