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๐Ÿง‘๐Ÿผโ€๐Ÿ’ป Research - January 18, 2025

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

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โšก Quick Summary

A recent study validated the AAIH-A measure, designed to assess adults’ openness and concerns regarding the use of artificial intelligence (AI) in healthcare. The findings indicate that trust in technology and concerns about quality and convenience significantly influence patient openness to AI-driven tools.

๐Ÿ” Key Details

  • ๐Ÿ“Š Participants: 379 adults from the general US population
  • ๐Ÿงฉ Measure Used: AAIH-A, adapted from a pediatric measure
  • โš™๏ธ Subscales: 7 subscales assessing openness and various concerns
  • ๐Ÿ” Analysis: Confirmatory factor analysis and multivariable regression models

๐Ÿ”‘ Key Takeaways

  • ๐Ÿ“Š AAIH-A is a brief and effective tool for assessing adult perspectives on AI in healthcare.
  • ๐Ÿ’ก Seven dimensions of concerns were confirmed through factor analysis.
  • ๐Ÿ‘ฉโ€๐Ÿ”ฌ Trust in technology is a significant predictor of openness to AI tools.
  • ๐Ÿ† Concerns about quality and convenience also play a crucial role in shaping attitudes.
  • ๐ŸŒ Engaging patients as stakeholders is essential for the successful implementation of AI in healthcare.
  • ๐Ÿ“ˆ Internal consistency of the AAIH-A scales was confirmed, indicating reliability.
  • ๐Ÿ”ฎ Future research can build on these findings to enhance AI tool development.

๐Ÿ“š Background

The integration of artificial intelligence in healthcare has the potential to transform patient care, but its success largely depends on patient engagement. Understanding patients’ attitudes towards AI is crucial for developing tools that meet their needs and concerns. This study aimed to adapt a validated measure to assess adult perspectives on AI in healthcare settings.

๐Ÿ—’๏ธ Study

Conducted through a cross-sectional survey, the study adapted the 33-item AAIH-A measure for adults, originally designed for parents. Participants were recruited via Amazon’s Mechanical Turk (MTurk) platform, allowing for a diverse sample from the general US population. The survey assessed openness to AI technologies and various concerns related to their use in healthcare.

๐Ÿ“ˆ Results

The analysis confirmed the seven dimensions of concern regarding AI in healthcare, demonstrating strong internal consistency across the scales. Notably, multivariable regression models revealed that trust in technology and concerns about quality and convenience were significantly associated with participants’ openness to AI-driven tools.

๐ŸŒ Impact and Implications

The findings from this study have important implications for the development and implementation of AI technologies in healthcare. By utilizing the AAIH-A measure, healthcare providers can better understand patient perspectives, leading to more effective engagement strategies. This approach ensures that AI tools are designed with patient concerns in mind, ultimately enhancing the quality of care.

๐Ÿ”ฎ Conclusion

The validation of the AAIH-A measure marks a significant step towards understanding patient attitudes towards AI in healthcare. By identifying key predictors of openness, this study paves the way for future research and development of AI tools that prioritize patient engagement and address their concerns. The future of AI in healthcare looks promising, with the potential for improved patient outcomes through thoughtful integration of technology.

๐Ÿ’ฌ Your comments

What are your thoughts on the role of AI in healthcare? How do you think patient concerns can be addressed in the development of these technologies? ๐Ÿ’ฌ Share your insights in the comments below or connect with us on social media:

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

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.

Author: [‘Sisk BA’, ‘Antes AL’, ‘Lin SC’, ‘Nong P’, ‘DuBois JM’]

Journal: Learn Health Syst

Citation: Sisk BA, et al. Validating a novel measure for assessing patient openness and concerns about using artificial intelligence in healthcare. Validating a novel measure for assessing patient openness and concerns about using artificial intelligence in healthcare. 2025; 9:e10429. doi: 10.1002/lrh2.10429

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