Publication Type

PhD Dissertation

Version

publishedVersion

Publication Date

5-2026

Abstract

With the deep integration of artificial intelligence (AI) technology in the healthcare sector, intelligent screening systems have achieved significant breakthroughs in predictive accuracy; however, they commonly face a management challenge in clinical implementation: accurate early warnings but limited patient recall. To address this ineffective technological empowerment issue in practice, this study proposes a theoretical framework featuringbidirectional interactions between "technological empowerment" and"relational empowerment" based on empowerment theory to systematicallyinvestigate the dynamic evolution mechanisms and boundary conditions of patient recall willingness under multi-stage, cross-departmental deployment of medical intelligent screening systems. Using a leading municipal hospital as the primary experimental setting, the study analyzed 28,409 authentic clinical behavior data records from January 2024 to June 2025, employing methods such as multi-purpose difference analysis, natural language processing, andmediation testing for rigorous causal inference. The empirical results reveal several important findings. First, algorithmic adjustments as core technological empowerment significantly enhanced overall patient recall willingness; however, as the system expanded from dominant disciplines intocomplex domains, the marginal benefits of technological empowerment exhibited a pronounced diminishing trend. Second, high-qualityphysician-patient communication, serving as relational empowerment, positively moderated the direct effects of technological empowerment, suggesting the synergy between precise identification and relational dynamics. Third, physicians' technological acceptance (task follow-up response rate) acted as the pivotal intermediary in translating algorithmic potential into actual clinical improvement. Fourth, patients' risk awareness and geographical proximity significantly constitute critical heterogeneity boundaries in empowerment efficacy. In summary, this study highlights the complementary role of technology-driven discovery and relationship-driventransformation in the multi-stage deployment of medical AI and provides strategic insights for healthcare institutions to reshape service workflows andoptimize dynamic, whole-course disease management.

Keywords

medical artificial intelligence, empowerment theory, patient recall willingness, physician-patient communication, information support, human-machine collaboration

Degree Awarded

Doctor of Business Administration (Accounting and Finance)

Discipline

Business Administration, Management, and Operations | Health Information Technology | Management Information Systems

Supervisor(s)

TANG, Qian

First Page

1

Last Page

121

Publisher

Singapore Management University

City or Country

Singapore

Copyright Owner and License

Author

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