Presentation
DH7 - Factors Influencing Trust in Artificial Intelligence Interventions in Healthcare: A Rapid Review
SessionPoster Session 1
DescriptionArtificial Intelligence (AI) has the potential to shape the delivery of healthcare and address the quintuple aims, namely, enhancing provider and patient experiences, improving health, reducing costs (Committee on Implementing High-Quality Primary Care et al., 2021, p. 1) and advancing health equity (Nundy et al., 2022). Patients’ and providers’ trust in AI and the trustworthiness of these systems are important factors in the adoption and integration of AI in healthcare. This poster presents the results of a rapid review conducted to understand the factors influencing trust in AI applications and the trustworthiness of AI applications within a healthcare context.
A rapid review was conducted based on the methodology of Tricco et al. (2017). A search strategy was developed for Embase and PubMed with search terms for the main concepts of AI, trust, and healthcare. Records were imported into Covidence for screening. Studies were included that reported on trust or trustworthiness as outcomes in relation to the development, conceptualization or implementation of an AI tool, system or intervention within a healthcare context. The literature search, across two databases, identified 1,247 studies of which 105 studies were included in the final analysis. A thematic analysis was performed on the studies and highlighted several determinants of trust in AI for patients and providers as well as features of AI trustworthiness.
This poster presentation will highlight determinants of trust and trustworthiness including:
- Sociodemographic Characteristics: The association between sociodemographic characteristics and trust.
- Explainability and Transparency: The need for explainable and transparent AI.
- Inclusive Data Sources and Model inputs: AI systems that are built on trustworthy data, namely with data sources and model inputs that are inclusive of diverse populations and relevant to clinical settings.
- Racial Concordance: AI interventions were considered trustworthy when they were inclusive and concordant with patients’ racial identities.
- System Performance: The importance of accuracy and reliability of performance.
- Design considerations: Design considerations included requirements for language and tone as well as culturally responsive human-likeness of anthropomorphic aids.
- Health System Integration: This theme reflects the necessary conditions for successful integration of an AI tool within a clinical setting or system. The compatibility and integration of an AI tool with existing software, tools, and practices, institutional accountability and responsibility measures, and AI implementation as a complement or co-pilot to clinical decision-making.
The findings of this study can inform appropriate and effective design, development, implementation and integration of AI in healthcare.
References
Committee on Implementing High-Quality Primary Care, Board on Health Care Services, Health and Medicine Division, & National Academies of Sciences, Engineering, and Medicine. (2021). Implementing High-Quality Primary Care: Rebuilding the Foundation of Health Care (L. McCauley, R. L. Phillips, M. Meisnere, & S. K. Robinson, Eds.; p. 25983). National Academies Press. https://doi.org/10.17226/25983
Nundy, S., Cooper, L. A., & Mate, K. S. (2022). The Quintuple Aim for Health Care Improvement: A New Imperative to Advance Health Equity. JAMA, 327(6), 521. https://doi.org/10.1001/jama.2021.25181
Tricco, A. C., Langlois, Etienne. V., Straus, S. E., Alliance for Health Policy and Systems Research, & World Health Organization. (2017). Rapid reviews to strengthen health policy and systems: A practical guide. World Health Organization. https://iris.who.int/handle/10665/258698
A rapid review was conducted based on the methodology of Tricco et al. (2017). A search strategy was developed for Embase and PubMed with search terms for the main concepts of AI, trust, and healthcare. Records were imported into Covidence for screening. Studies were included that reported on trust or trustworthiness as outcomes in relation to the development, conceptualization or implementation of an AI tool, system or intervention within a healthcare context. The literature search, across two databases, identified 1,247 studies of which 105 studies were included in the final analysis. A thematic analysis was performed on the studies and highlighted several determinants of trust in AI for patients and providers as well as features of AI trustworthiness.
This poster presentation will highlight determinants of trust and trustworthiness including:
- Sociodemographic Characteristics: The association between sociodemographic characteristics and trust.
- Explainability and Transparency: The need for explainable and transparent AI.
- Inclusive Data Sources and Model inputs: AI systems that are built on trustworthy data, namely with data sources and model inputs that are inclusive of diverse populations and relevant to clinical settings.
- Racial Concordance: AI interventions were considered trustworthy when they were inclusive and concordant with patients’ racial identities.
- System Performance: The importance of accuracy and reliability of performance.
- Design considerations: Design considerations included requirements for language and tone as well as culturally responsive human-likeness of anthropomorphic aids.
- Health System Integration: This theme reflects the necessary conditions for successful integration of an AI tool within a clinical setting or system. The compatibility and integration of an AI tool with existing software, tools, and practices, institutional accountability and responsibility measures, and AI implementation as a complement or co-pilot to clinical decision-making.
The findings of this study can inform appropriate and effective design, development, implementation and integration of AI in healthcare.
References
Committee on Implementing High-Quality Primary Care, Board on Health Care Services, Health and Medicine Division, & National Academies of Sciences, Engineering, and Medicine. (2021). Implementing High-Quality Primary Care: Rebuilding the Foundation of Health Care (L. McCauley, R. L. Phillips, M. Meisnere, & S. K. Robinson, Eds.; p. 25983). National Academies Press. https://doi.org/10.17226/25983
Nundy, S., Cooper, L. A., & Mate, K. S. (2022). The Quintuple Aim for Health Care Improvement: A New Imperative to Advance Health Equity. JAMA, 327(6), 521. https://doi.org/10.1001/jama.2021.25181
Tricco, A. C., Langlois, Etienne. V., Straus, S. E., Alliance for Health Policy and Systems Research, & World Health Organization. (2017). Rapid reviews to strengthen health policy and systems: A practical guide. World Health Organization. https://iris.who.int/handle/10665/258698
Authors
Event Type
Poster Presentation
TimeMonday, March 314:45pm - 6:15pm EDT
LocationFrontenac Foyer
Digital Health (DH)
Simulation and Education (SE)
Hospital Environments (HE)
Medical and Drug Delivery Devices (MDD)
Patient Safety and Research Initiatives (PS)