ArticleBMC psychology2025
Trust in artificial intelligence-based follow-up in hospital information systems: development and validation of a new scale.
Article in BMC psychology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Trust in AI: scale development and validation for online distance learners.BMC psychology · 2026Article
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8 authors.
Funding
Abstract
backgroundArtificial intelligence-based follow-up systems offer efficient solutions for postdischarge care but face limited patient acceptance due to trust concerns. Trust is a key determinant of acceptance, yet existing instruments are insufficient for assessing trust in artificial intelligence-driven follow-up contexts. This study focused on developing and psychometrically validating a scale to assess patient trust in artificial intelligence-based follow-up.
methodsThis methodological study was carried out in three sequential stages: item generation, scale development, and psychometric validation. During the initial phase, a preliminary item pool was established based on theoretical frameworks, existing literature, semistructured interviews, and focus group discussions. In the second phase, a preliminary scale was developed using the Delphi method, content validity assessment, and a pilot survey. In the third phase, a questionnaire was administered to patients who had experienced artificial intelligence-based follow-up. The structure was first determined by exploratory factor analysis (EFA), refined via item analysis, and then validated using confirmatory factor analysis (CFA). Reliability was assessed by computing internal consistency (Cronbach's α, McDonald's ω) and test-retest correlations.
resultsThe final scale comprised 27 items across three dimensions: patient dispositional trust, system interaction trust, and environmental trust. Content validity was high (I-CVI: 0.833-1.000; S-CVI/Ave: 0.955). EFA revealed three factors that explained 66.0% of the variance. CFA indicated a good model fit (χ²/df = 1.096, RMSEA = 0.022, CFI = 0.999, TLI = 0.99). The reliability indices were strong (Cronbach's α = 0.931; McDonald's ω = 0.960; test-retest = 0.901).
conclusionThe Artificial Intelligence-Based Follow-Up Trust Scale demonstrated strong reliability and validity. This valuable tool can assess trust in artificial intelligence-based follow-up systems, supporting future research and the development of strategies to increase trust and improve follow-up adherence.
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