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 thei...
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| Format: | Article |
| Language: | English |
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Wiley
2025-01-01
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| Series: | Learning Health Systems |
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| Online Access: | https://doi.org/10.1002/lrh2.10429 |
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| author | Bryan A. Sisk Alison L. Antes Sunny C. Lin Paige Nong James M. DuBois |
| author_facet | Bryan A. Sisk Alison L. Antes Sunny C. Lin Paige Nong James M. DuBois |
| author_sort | Bryan A. Sisk |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-eb802af3138846228b6aebe60536a961 |
| institution | Kabale University |
| issn | 2379-6146 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Learning Health Systems |
| spelling | doaj-art-eb802af3138846228b6aebe60536a9612025-01-15T08:51:32ZengWileyLearning Health Systems2379-61462025-01-0191n/an/a10.1002/lrh2.10429Validating a novel measure for assessing patient openness and concerns about using artificial intelligence in healthcareBryan A. Sisk0Alison L. Antes1Sunny C. Lin2Paige Nong3James M. DuBois4Division of Pediatric Hematology and Oncology, Department of Pediatrics Washington University School of Medicine St. Louis Missouri USABioethics Research Center, Division of General Medical Sciences, Department of Medicine Washington University School of Medicine St. Louis Missouri USADivision of General Medical Sciences, Department of Medicine Washington University School of Medicine St. Louis Missouri USADepartment of Health Policy and Management University of Minnesota School of Public Health Minneapolis Minnesota USABioethics Research Center, Division of General Medical Sciences, Department of Medicine Washington University School of Medicine St. Louis Missouri USAAbstract 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.https://doi.org/10.1002/lrh2.10429artificial intelligencebioethicshealthcareopennesssurveytechnology |
| spellingShingle | Bryan A. Sisk Alison L. Antes Sunny C. Lin Paige Nong James M. DuBois Validating a novel measure for assessing patient openness and concerns about using artificial intelligence in healthcare Learning Health Systems artificial intelligence bioethics healthcare openness survey technology |
| title | Validating a novel measure for assessing patient openness and concerns about using artificial intelligence in healthcare |
| title_full | Validating a novel measure for assessing patient openness and concerns about using artificial intelligence in healthcare |
| title_fullStr | Validating a novel measure for assessing patient openness and concerns about using artificial intelligence in healthcare |
| title_full_unstemmed | Validating a novel measure for assessing patient openness and concerns about using artificial intelligence in healthcare |
| title_short | Validating a novel measure for assessing patient openness and concerns about using artificial intelligence in healthcare |
| title_sort | validating a novel measure for assessing patient openness and concerns about using artificial intelligence in healthcare |
| topic | artificial intelligence bioethics healthcare openness survey technology |
| url | https://doi.org/10.1002/lrh2.10429 |
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