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A STUDY ON THE SWITCHING INTENTION TO AI-ASSISTED HEALTHCARE
SERVICES THROUGH THE PUSH-PULL-MOORING (PPM) MODEL IN HANOI

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Pham Van Tuan* , Ho Thanh Hang , Nguyen Danh Duc , Phan Ngoc Anh , Nguyen Ha Vy ,
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Nguyen Thuy Duong 6
1, 2, 3, 4, 5, 6 National Economics University, Hanoi, Vietnam.
(*E-mail: phamvantuan@neu.edu.vn)
ABSTRACT
Amid rapid digital transformation, artificial intelligence is increasingly integrated
into healthcare to improve diagnostic quality and efficiency. This study investigates
factors influencing Hanoi residents’ switching intention from traditional services to AI-
assisted diagnosis using the PPM framework. Survey data from 386 respondents were
analysed with PLS-SEM. Results show that pull factors are the strongest positive drivers of
switching intention, while mooring factors have significant negative effects and weaken
pull influences. push factors are not significant. The findings stress maximizing AI benefits
and minimizing switching barriers through trust, safety, and transparency.
Keywords: Artificial intelligence (AI), AI-assisted healthcare, PPM model, switching
intention

1. Introduction
Recent advances in AI have accelerated its integration into healthcare systems
worldwide, transforming diagnostic practices and healthcare delivery. Across many
countries, AI has been recognized as a strategic tool to enhance diagnostic accuracy,
operational efficiency and personalized care, thereby stimulating growing scholarly
interest in patients’ switching intention from traditional healthcare models to AI-assisted
diagnostic services (Jeong et al., 2025; Muthmainah & Cholil, 2019). Empirical evidence
demonstrates AI’s diagnostic potential. For example, IBM Watson proposed treatment
options consistent with oncologists in 99% of cases and identified additional treatment
alternatives in approximately 30% of cases (Lohr, 2016). However, switching behavior in
healthcare remains complex because medical decisions are closely associated with
perceived health risks, institutional and interpersonal trust and long-established doctor -
patient relationships (Choi & Yoon, 2025; Dao et al., 2025).
At the policy level, Vietnam has identified AI in healthcare as a national strategic
priority through Resolution No. 57/NQ-TW (Political Bureau, 2024) and Resolution No.
72/NQ-TW (Political Bureau, 2025), which promote digital transformation and advanced
technologies in healthcare modernization. Hanoi, as the political and medical hub of
Vietnam, has been positioned as a leading locality for piloting digital health initiatives.
Nevertheless, AI implementation remains limited and largely concentrated in pilot
projects and selected healthcare institutions. This situation contrasts with developed
countries such as the United States, where approximately 46% of healthcare
organizations have initiated generative AI deployment and 25% of hospitals have applied
predictive analytics to forecast workforce demand and patient readmission risks (Deloitte,
2025; Linh, 2024).
This study examines determinants influencing Hanoi residents’ intention to switch
from traditional healthcare services to AI-assisted diagnostic services. By adopting an

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