ArticleBMC health services research2026
Research on the implementation path of digital-intelligent healthcare based on the TAM model from the perspective of high-quality development.
Article in BMC health services research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Authors and funding
5 authors.
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Abstract
backgroundDigital-intelligent healthcare is essential for high-quality, accessible medical services, yet its adoption is hindered by varying public attitudes.
objectiveThis study investigates determinants of public acceptance and explores reasons for suboptimal implementation to inform sustainable, patient-centered pathways.
methodsWe employed a sequential mixed-methods design grounded in the Technology Acceptance Model. A survey collected 456 valid responses, with 340 valid for structural equation modeling after data screening, followed by semi-structured interviews with eight purposively selected individuals (clinicians, students, educators, elderly patients) to triangulate findings.
resultsPerceived usefulness (PU) and perceived ease of use (PEOU) significantly influenced behavioral intention (BI). Attitude played a complete mediating role between PU and BI (indirect effect = 0.784, p < 0.01). PU was strongly predicted by subjective norms (β = 0.836, p < 0.01) and social influence (β = 0.254, p < 0.01). Notably, PEOU exhibited a significant negative direct effect on BI (β = -0.245, p < 0.01), suggesting a “trust threshold” where excessive simplification may undermine confidence in high-stakes medical contexts. Qualitative data corroborated these findings, revealing concerns over data security, operational complexity, and inadequate age-friendly design.
conclusionPublic acceptance of digital-intelligent healthcare is a socio-technical process mediated by attitude and constrained by trust barriers. Effective implementation requires multi-level strategies: alleviating individual technology anxiety, fostering organizational digital leadership, and building a collaborative policy-technology-society network to achieve truly patient-centered, high-quality development.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.