Exploring perceptions of healthcare technologies enabled by artificial intelligence: an online, scenario-based survey
Abstract Background Healthcare is expected to increasingly integrate technologies enabled by artificial intelligence (AI) into patient care. Understanding perceptions of these tools is essential to successful development and adoption. This exploratory study gauged participants’ level of openness, co...
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| Format: | Article |
| Language: | English |
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BMC
2021-07-01
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| Series: | BMC Medical Informatics and Decision Making |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12911-021-01586-8 |
| _version_ | 1818619734222962688 |
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| author | Alison L. Antes Sara Burrous Bryan A. Sisk Matthew J. Schuelke Jason D. Keune James M. DuBois |
| author_facet | Alison L. Antes Sara Burrous Bryan A. Sisk Matthew J. Schuelke Jason D. Keune James M. DuBois |
| author_sort | Alison L. Antes |
| collection | DOAJ |
| description | Abstract Background Healthcare is expected to increasingly integrate technologies enabled by artificial intelligence (AI) into patient care. Understanding perceptions of these tools is essential to successful development and adoption. This exploratory study gauged participants’ level of openness, concern, and perceived benefit associated with AI-driven healthcare technologies. We also explored socio-demographic, health-related, and psychosocial correlates of these perceptions. Methods We developed a measure depicting six AI-driven technologies that either diagnose, predict, or suggest treatment. We administered the measure via an online survey to adults (N = 936) in the United States using MTurk, a crowdsourcing platform. Participants indicated their level of openness to using the AI technology in the healthcare scenario. Items reflecting potential concerns and benefits associated with each technology accompanied the scenarios. Participants rated the extent that the statements of concerns and benefits influenced their perception of favorability toward the technology. Participants completed measures of socio-demographics, health variables, and psychosocial variables such as trust in the healthcare system and trust in technology. Exploratory and confirmatory factor analyses of the concern and benefit items identified two factors representing overall level of concern and perceived benefit. Descriptive analyses examined levels of openness, concern, and perceived benefit. Correlational analyses explored associations of socio-demographic, health, and psychosocial variables with openness, concern, and benefit scores while multivariable regression models examined these relationships concurrently. Results Participants were moderately open to AI-driven healthcare technologies (M = 3.1/5.0 ± 0.9), but there was variation depending on the type of application, and the statements of concerns and benefits swayed views. Trust in the healthcare system and trust in technology were the strongest, most consistent correlates of openness, concern, and perceived benefit. Most other socio-demographic, health-related, and psychosocial variables were less strongly, or not, associated, but multivariable models indicated some personality characteristics (e.g., conscientiousness and agreeableness) and socio-demographics (e.g., full-time employment, age, sex, and race) were modestly related to perceptions. Conclusions Participants’ openness appears tenuous, suggesting early promotion strategies and experiences with novel AI technologies may strongly influence views, especially if implementation of AI technologies increases or undermines trust. The exploratory nature of these findings warrants additional research. |
| first_indexed | 2024-12-16T17:42:11Z |
| format | Article |
| id | doaj.art-a4c4ed953e5b42dd944191e6dac23eeb |
| institution | Directory Open Access Journal |
| issn | 1472-6947 |
| language | English |
| last_indexed | 2024-12-16T17:42:11Z |
| publishDate | 2021-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Informatics and Decision Making |
| spelling | doaj.art-a4c4ed953e5b42dd944191e6dac23eeb2022-12-21T22:22:33ZengBMCBMC Medical Informatics and Decision Making1472-69472021-07-0121111510.1186/s12911-021-01586-8Exploring perceptions of healthcare technologies enabled by artificial intelligence: an online, scenario-based surveyAlison L. Antes0Sara Burrous1Bryan A. Sisk2Matthew J. Schuelke3Jason D. Keune4James M. DuBois5Bioethics Research Center, Washington University School of Medicine in St. LouisBioethics Research Center, Washington University School of Medicine in St. LouisDepartment of Pediatrics, Division of Hematology and Oncology, Washington University School of Medicine in St. LouisDivision of Biostatistics, Washington University School of Medicine in St. LouisDepartments of Surgery and Health Care Ethics, Bander Center for Medical Business Ethics, Saint Louis UniversityBioethics Research Center, Washington University School of Medicine in St. LouisAbstract Background Healthcare is expected to increasingly integrate technologies enabled by artificial intelligence (AI) into patient care. Understanding perceptions of these tools is essential to successful development and adoption. This exploratory study gauged participants’ level of openness, concern, and perceived benefit associated with AI-driven healthcare technologies. We also explored socio-demographic, health-related, and psychosocial correlates of these perceptions. Methods We developed a measure depicting six AI-driven technologies that either diagnose, predict, or suggest treatment. We administered the measure via an online survey to adults (N = 936) in the United States using MTurk, a crowdsourcing platform. Participants indicated their level of openness to using the AI technology in the healthcare scenario. Items reflecting potential concerns and benefits associated with each technology accompanied the scenarios. Participants rated the extent that the statements of concerns and benefits influenced their perception of favorability toward the technology. Participants completed measures of socio-demographics, health variables, and psychosocial variables such as trust in the healthcare system and trust in technology. Exploratory and confirmatory factor analyses of the concern and benefit items identified two factors representing overall level of concern and perceived benefit. Descriptive analyses examined levels of openness, concern, and perceived benefit. Correlational analyses explored associations of socio-demographic, health, and psychosocial variables with openness, concern, and benefit scores while multivariable regression models examined these relationships concurrently. Results Participants were moderately open to AI-driven healthcare technologies (M = 3.1/5.0 ± 0.9), but there was variation depending on the type of application, and the statements of concerns and benefits swayed views. Trust in the healthcare system and trust in technology were the strongest, most consistent correlates of openness, concern, and perceived benefit. Most other socio-demographic, health-related, and psychosocial variables were less strongly, or not, associated, but multivariable models indicated some personality characteristics (e.g., conscientiousness and agreeableness) and socio-demographics (e.g., full-time employment, age, sex, and race) were modestly related to perceptions. Conclusions Participants’ openness appears tenuous, suggesting early promotion strategies and experiences with novel AI technologies may strongly influence views, especially if implementation of AI technologies increases or undermines trust. The exploratory nature of these findings warrants additional research.https://doi.org/10.1186/s12911-021-01586-8Artificial intelligenceMachine learningAcceptance of healthcareOpennessBenefitsConcerns |
| spellingShingle | Alison L. Antes Sara Burrous Bryan A. Sisk Matthew J. Schuelke Jason D. Keune James M. DuBois Exploring perceptions of healthcare technologies enabled by artificial intelligence: an online, scenario-based survey BMC Medical Informatics and Decision Making Artificial intelligence Machine learning Acceptance of healthcare Openness Benefits Concerns |
| title | Exploring perceptions of healthcare technologies enabled by artificial intelligence: an online, scenario-based survey |
| title_full | Exploring perceptions of healthcare technologies enabled by artificial intelligence: an online, scenario-based survey |
| title_fullStr | Exploring perceptions of healthcare technologies enabled by artificial intelligence: an online, scenario-based survey |
| title_full_unstemmed | Exploring perceptions of healthcare technologies enabled by artificial intelligence: an online, scenario-based survey |
| title_short | Exploring perceptions of healthcare technologies enabled by artificial intelligence: an online, scenario-based survey |
| title_sort | exploring perceptions of healthcare technologies enabled by artificial intelligence an online scenario based survey |
| topic | Artificial intelligence Machine learning Acceptance of healthcare Openness Benefits Concerns |
| url | https://doi.org/10.1186/s12911-021-01586-8 |
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