Predictors of Health Care Practitioners’ Intention to Use AI-Enabled Clinical Decision Support Systems: Meta-Analysis Based on the Unified Theory of Acceptance and Use of Technology Article Swipe
Julius Dingel
,
Anne‐Kathrin Kleine
,
Julia Cecil
,
Anna Sigl
,
Eva Lermer
,
Susanne Gaube
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.2196/57224
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.2196/57224
Background Artificial intelligence–enabled clinical decision support systems (AI-CDSSs) offer potential for improving health care outcomes, but their adoption among health care practitioners remains limited. Objective This meta-analysis identified predictors influencing health care practitioners’ intention to use AI-CDSSs based on the Unified Theory of Acceptance and Use of Technology (UTAUT). Additional predictors were examined based on existing empirical evidence. Methods The literature search using electronic databases, forward searches, conference programs, and personal correspondence yielded 7731 results, of which 17 (0.22%) studies met the inclusion criteria. Random-effects meta-analysis, relative weight analyses, and meta-analytic moderation and mediation analyses were used to examine the relationships between relevant predictor variables and the intention to use AI-CDSSs. Results The meta-analysis results supported the application of the UTAUT to the context of the intention to use AI-CDSSs. The results showed that performance expectancy (r=0.66), effort expectancy (r=0.55), social influence (r=0.66), and facilitating conditions (r=0.66) were positively associated with the intention to use AI-CDSSs, in line with the predictions of the UTAUT. The meta-analysis further identified positive attitude (r=0.63), trust (r=0.73), anxiety (r=–0.41), perceived risk (r=–0.21), and innovativeness (r=0.54) as additional relevant predictors. Trust emerged as the most influential predictor overall. The results of the moderation analyses show that the relationship between social influence and use intention becomes weaker with increasing age. In addition, the relationship between effort expectancy and use intention was stronger for diagnostic AI-CDSSs than for devices that combined diagnostic and treatment recommendations. Finally, the relationship between facilitating conditions and use intention was mediated through performance and effort expectancy. Conclusions This meta-analysis contributes to the understanding of the predictors of intention to use AI-CDSSs based on an extended UTAUT model. More research is needed to substantiate the identified relationships and explain the observed variations in effect sizes by identifying relevant moderating factors. The research findings bear important implications for the design and implementation of training programs for health care practitioners to ease the adoption of AI-CDSSs into their practice.
Related Topics
Concepts
Preprint
Unified theory of acceptance and use of technology
eHealth
Clinical decision support system
Health care
Psychology
Meta-analysis
mHealth
Decision support system
Knowledge management
Computer science
Psychological intervention
Medicine
Artificial intelligence
Nursing
Social psychology
Expectancy theory
World Wide Web
Economic growth
Economics
Internal medicine
Metadata
- Type
- review
- Language
- en
- Landing Page
- https://doi.org/10.2196/57224
- https://s3.ca-central-1.amazonaws.com/assets.jmir.org/assets/preprints/preprint-57224-accepted.pdf
- OA Status
- gold
- Cited By
- 31
- References
- 135
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4396857249
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4396857249Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.2196/57224Digital Object Identifier
- Title
-
Predictors of Health Care Practitioners’ Intention to Use AI-Enabled Clinical Decision Support Systems: Meta-Analysis Based on the Unified Theory of Acceptance and Use of TechnologyWork title
- Type
-
reviewOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-05-13Full publication date if available
- Authors
-
Julius Dingel, Anne‐Kathrin Kleine, Julia Cecil, Anna Sigl, Eva Lermer, Susanne GaubeList of authors in order
- Landing page
-
https://doi.org/10.2196/57224Publisher landing page
- PDF URL
-
https://s3.ca-central-1.amazonaws.com/assets.jmir.org/assets/preprints/preprint-57224-accepted.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://s3.ca-central-1.amazonaws.com/assets.jmir.org/assets/preprints/preprint-57224-accepted.pdfDirect OA link when available
- Concepts
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Preprint, Unified theory of acceptance and use of technology, eHealth, Clinical decision support system, Health care, Psychology, Meta-analysis, mHealth, Decision support system, Knowledge management, Computer science, Psychological intervention, Medicine, Artificial intelligence, Nursing, Social psychology, Expectancy theory, World Wide Web, Economic growth, Economics, Internal medicineTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
31Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 28, 2024: 3Per-year citation counts (last 5 years)
- References (count)
-
135Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.health | 12, 19, 30, 312 |
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| abstract_inverted_index.showed | 132 |
| abstract_inverted_index.social | 140, 204 |
| abstract_inverted_index.weaker | 210 |
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| abstract_inverted_index.(0.22%) | 78 |
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| abstract_inverted_index.results | 114, 131, 194 |
| abstract_inverted_index.studies | 79 |
| abstract_inverted_index.support | 5 |
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| abstract_inverted_index.yielded | 72 |
| abstract_inverted_index.(UTAUT). | 48 |
| abstract_inverted_index.(r=0.54) | 180 |
| abstract_inverted_index.(r=0.66) | 146 |
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| abstract_inverted_index.adoption | 17, 318 |
| abstract_inverted_index.analyses | 94, 198 |
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| abstract_inverted_index.research | 276, 298 |
| abstract_inverted_index.results, | 74 |
| abstract_inverted_index.stronger | 225 |
| abstract_inverted_index.training | 309 |
| abstract_inverted_index.(r=0.55), | 139 |
| abstract_inverted_index.(r=0.63), | 170 |
| abstract_inverted_index.(r=0.66), | 136, 142 |
| abstract_inverted_index.(r=0.73), | 172 |
| abstract_inverted_index.AI-CDSSs, | 155 |
| abstract_inverted_index.AI-CDSSs. | 110, 129 |
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| abstract_inverted_index.inclusion | 82 |
| abstract_inverted_index.influence | 141, 205 |
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| abstract_inverted_index.perceived | 175 |
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| abstract_inverted_index.treatment | 236 |
| abstract_inverted_index.variables | 104 |
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| abstract_inverted_index.Background | 0 |
| abstract_inverted_index.Technology | 47 |
| abstract_inverted_index.additional | 182 |
| abstract_inverted_index.associated | 149 |
| abstract_inverted_index.conditions | 145, 243 |
| abstract_inverted_index.conference | 67 |
| abstract_inverted_index.databases, | 64 |
| abstract_inverted_index.diagnostic | 227, 234 |
| abstract_inverted_index.electronic | 63 |
| abstract_inverted_index.expectancy | 135, 138, 220 |
| abstract_inverted_index.identified | 27, 167, 282 |
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| abstract_inverted_index.literature | 60 |
| abstract_inverted_index.moderating | 295 |
| abstract_inverted_index.moderation | 91, 197 |
| abstract_inverted_index.positively | 148 |
| abstract_inverted_index.predictors | 28, 50, 263 |
| abstract_inverted_index.variations | 288 |
| abstract_inverted_index.Conclusions | 254 |
| abstract_inverted_index.application | 117 |
| abstract_inverted_index.contributes | 257 |
| abstract_inverted_index.expectancy. | 253 |
| abstract_inverted_index.identifying | 293 |
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| abstract_inverted_index.influential | 190 |
| abstract_inverted_index.performance | 134, 250 |
| abstract_inverted_index.predictions | 160 |
| abstract_inverted_index.predictors. | 184 |
| abstract_inverted_index.(r=–0.21), | 177 |
| abstract_inverted_index.(r=–0.41), | 174 |
| abstract_inverted_index.facilitating | 144, 242 |
| abstract_inverted_index.implications | 302 |
| abstract_inverted_index.relationship | 202, 217, 240 |
| abstract_inverted_index.substantiate | 280 |
| abstract_inverted_index.meta-analysis | 26, 113, 165, 256 |
| abstract_inverted_index.meta-analytic | 90 |
| abstract_inverted_index.practitioners | 21, 314 |
| abstract_inverted_index.relationships | 100, 283 |
| abstract_inverted_index.understanding | 260 |
| abstract_inverted_index.Random-effects | 84 |
| abstract_inverted_index.correspondence | 71 |
| abstract_inverted_index.implementation | 307 |
| abstract_inverted_index.innovativeness | 179 |
| abstract_inverted_index.meta-analysis, | 85 |
| abstract_inverted_index.practitioners’ | 32 |
| abstract_inverted_index.recommendations. | 237 |
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| countries_distinct_count | 3 |
| institutions_distinct_count | 6 |
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