» Articles » PMID: 39822920

Validating a Novel Measure for Assessing Patient Openness and Concerns About Using Artificial Intelligence in Healthcare

Overview
Date 2025 Jan 17
PMID 39822920
Authors
Affiliations
Soon will be listed here.
Abstract

Objectives: Patient engagement is critical for the effective development and use of artificial intelligence (AI)-enabled tools in learning health systems (LHSs). We adapted a previously validated measure from pediatrics to assess adults' openness and concerns about the use of AI in their healthcare.

Study Design: Cross-sectional survey.

Methods: We adapted the 33-item "Attitudes toward Artificial Intelligence in Healthcare for Parents" measure for administration to adults in the general US population (AAIH-A), recruiting participants through Amazon's Mechanical Turk (MTurk) crowdsourcing platform. AAIH-A assesses openness to AI-driven technologies and includes 7 subscales assessing participants' openness and concerns about these technologies. The openness scale includes examples of AI-driven tools for diagnosis, prediction, treatment selection, and medical guidance. Concern subscales assessed privacy, social justice, quality, human element of care, cost, shared decision-making, and convenience. We co-administered previously validated measures hypothesized to correlate with openness. We conducted a confirmatory factor analysis and assessed reliability and construct validity. We performed exploratory multivariable regression models to identify predictors of openness.

Results: A total of 379 participants completed the survey. Confirmatory factor analysis confirmed the seven dimensions of the concerns, and the scales had internal consistency reliability, and correlated as hypothesized with existing measures of trust and faith in technology. Multivariable models indicated that trust in technology and concerns about quality and convenience were significantly associated with openness.

Conclusions: The AAIH-A is a brief measure that can be used to assess adults' perspectives about AI-driven technologies in healthcare and LHSs. The use of AAIH-A can inform future development and implementation of AI-enabled tools for patient care in the LHS context that engage patients as key stakeholders.

Citing Articles

Validating a novel measure for assessing patient openness and concerns about using artificial intelligence in healthcare.

Sisk B, Antes A, Lin S, Nong P, DuBois J Learn Health Syst. 2025; 9(1):e10429.

PMID: 39822920 PMC: 11733432. DOI: 10.1002/lrh2.10429.

References
1.
Hartz S, Quan T, Ibiebele A, Fisher S, Olfson E, Salyer P . The significant impact of education, poverty, and race on Internet-based research participant engagement. Genet Med. 2016; 19(2):240-243. PMC: 5274598. DOI: 10.1038/gim.2016.91. View

2.
Aggarwal R, Farag S, Martin G, Ashrafian H, Darzi A . Patient Perceptions on Data Sharing and Applying Artificial Intelligence to Health Care Data: Cross-sectional Survey. J Med Internet Res. 2021; 23(8):e26162. PMC: 8430862. DOI: 10.2196/26162. View

3.
Embi P, Richesson R, Tenenbaum J, Kannry J, Friedman C, Sarkar I . Reimagining the research-practice relationship: policy recommendations for informatics-enabled evidence-generation across the US health system. JAMIA Open. 2020; 2(1):2-9. PMC: 6951885. DOI: 10.1093/jamiaopen/ooy056. View

4.
Rubin J, Silverstein J, Friedman C, Kush R, Anderson W, Lichter A . Transforming the future of health together: The Learning Health Systems Consensus Action Plan. Learn Health Syst. 2019; 2(3):e10055. PMC: 6508804. DOI: 10.1002/lrh2.10055. View

5.
Friedman C, Rubin J, Brown J, Buntin M, Corn M, Etheredge L . Toward a science of learning systems: a research agenda for the high-functioning Learning Health System. J Am Med Inform Assoc. 2014; 22(1):43-50. PMC: 4433378. DOI: 10.1136/amiajnl-2014-002977. View