Patient Perspectives on Artificial Intelligence in Healthcare Decision Making: A Multi-Center Comparative Study

Matthew W Parry; Jonathan S Markowitz; Cara M Nordberg; Aalpen Patel; Wesley H Bronson; Edward M DelSole

doi:10.1007/s43465-023-00845-2

Patient Perspectives on Artificial Intelligence in Healthcare Decision Making: A Multi-Center Comparative Study

Indian J Orthop. 2023 Mar 2;57(5):653-665. doi: 10.1007/s43465-023-00845-2. eCollection 2023 May.

Authors

Matthew W Parry^{1

2}, Jonathan S Markowitz³, Cara M Nordberg⁴, Aalpen Patel⁵, Wesley H Bronson³, Edward M DelSole^{1

2}

Affiliations

¹ Geisinger Orthopaedics and Sports Medicine, Scranton, PA USA.
² Geisinger Commonwealth School of Medicine, Scranton, PA USA.
³ Orthopaedic Surgery at the Mount Sinai Hospital, New York, NY USA.
⁴ Department of Population Health Sciences, Geisinger Medical Center, Danville, PA USA.
⁵ Department of Imaging Science and Innovation, Geisinger Medical Center, Danville, PA USA.

Abstract

Objective: Investigate the patient opinion on the use of Artificial Intelligence (AI) in Orthopaedics.

Methods: 397 orthopaedic patients from a large urban academic center and a rural health system completed a 37-component survey querying patient demographics and perspectives on clinical scenarios involving AI. An average comfort score was calculated from thirteen Likert-scale questions (1, not comfortable; 10, very comfortable). Secondary outcomes requested a binary opinion on whether it is acceptable for patient healthcare data to be used to create AI (yes/no) and the impact of AI on: orthopaedic care (positive/negative); healthcare cost (increase/decrease); and their decision to refuse healthcare if cost increased (yes/no). Bivariate and multivariable analyses were employed to identify characteristics that impacted patient perspectives.

Results: The average comfort score across the population was 6.4, with significant bivariate differences between age (p = 0.0086), gender (p = 0.0001), education (p = 0.0029), experience with AI/ML (p < 0.0001), survey format (p < 0.0001), and four binary outcomes (p < 0.05). When controlling for age and education, multivariable regression identified significant relationships between comfort score and experience with AI/ML (p = 0.0018) and each of the four binary outcomes (p < 0.05). In the final multivariable model gender, survey format, perceived impact of AI on orthopaedic care, and the decision to refuse care if it were to increase cost remained significantly associated with the average AI comfort score (p < 0.05). Additionally, patients were not comfortable undergoing surgery entirely by a robot with distant physician supervision compared to close supervision.

Conclusion: The orthopaedic patient appears comfortable with AI joining the care team.

Keywords: Artificial intelligence; Multi-center; Patient comfort; Patient perspectives.

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