Acceptance of AI in Healthcare
Information SystemsReliability: alpha 0.913 sample items
About this scale
A multi-item measure of patient and clinician acceptance of AI in clinical decision-making, diagnosis, and treatment recommendations.
Sample item(s)
“I would trust an AI system to help diagnose a medical condition”
“I would prefer a human doctor over an AI doctor (reverse)”
“AI can improve the quality of healthcare”
Reliability
Reported internal consistency: alpha 0.91. As a rule of thumb, Cronbach’s α ≥ .70 is considered acceptable, ≥ .80 good, and ≥ .90 excellent for established measures.
Notes
Pair with TAM and UTAUT2 for full healthcare-AI adoption modelling.
Source & citation
Khanijahani, A., Iezadi, S., Dudley, S., Goettler, M., Kroetsch, P., & Wise, J. (2022): Organizational, professional, and patient characteristics associated with artificial intelligence adoption in healthcare: A systematic review. Health Policy and Technology, Vol. 11, No. 1, Article 100602.
Khanijahani et al. (2022): AI adoption in healthcare. HPT, 11(1), 100602.
Source. Scale details are summarised from the publication cited above. Always consult the original source for the full item set, scoring, and permission to use.
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