Bryan A. Sisk, Alison L. Antes, Sunny C. Lin, Paige Nong, James M. DuBois
{"title":"验证用于评估患者对在医疗保健中使用人工智能的开放性和担忧程度的新型测量方法","authors":"Bryan A. Sisk, Alison L. Antes, Sunny C. Lin, Paige Nong, James M. DuBois","doi":"10.1002/lrh2.10429","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Objectives</h3>\n \n <p>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.</p>\n </section>\n \n <section>\n \n <h3> Study Design</h3>\n \n <p>Cross-sectional survey.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>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.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>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.</p>\n </section>\n \n <section>\n \n <h3> Conclusions</h3>\n \n <p>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.</p>\n </section>\n </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"9 1","pages":""},"PeriodicalIF":2.6000,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10429","citationCount":"0","resultStr":"{\"title\":\"Validating a novel measure for assessing patient openness and concerns about using artificial intelligence in healthcare\",\"authors\":\"Bryan A. Sisk, Alison L. Antes, Sunny C. Lin, Paige Nong, James M. DuBois\",\"doi\":\"10.1002/lrh2.10429\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Objectives</h3>\\n \\n <p>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.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Study Design</h3>\\n \\n <p>Cross-sectional survey.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Methods</h3>\\n \\n <p>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.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Results</h3>\\n \\n <p>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.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Conclusions</h3>\\n \\n <p>The AAIH-A is a brief measure that can be used to assess adults' perspectives about AI-driven technologies in healthcare and LHSs. 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Validating a novel measure for assessing patient openness and concerns about using artificial intelligence in healthcare
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.
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