Incentive and constraint factors and optimization strategies for artificial intelligence application in pharmacy based on TAM-TOE-DOI integrated framework
- VernacularTitle:基于TAM-TOE-DOI整合框架的人工智能在药学领域应用的激励和约束因素及优化策略研究
- Author:
Jian YANG
1
;
Zhichu LI
2
;
Weili ZHAO
3
;
Xiaoyi YU
1
;
Ming XU
1
Author Information
1. Dept. of Global Health，School of Public Health，Peking University，Beijing 100191，China
2. Dept. of Global Health，School of Public Health，Peking University，Beijing 100191，China;Institute of Global Health Development，Peking University，Beijing 100871，China
3. Dept. of Global Health，School of Public Health，Peking University，Beijing 100191，China;Division of Foreign Relations，Chinese Medical Association，Beijing 100710，China
- Publication Type:Journal Article
- Keywords:
artificial intelligence;
pharmacy;
incentive factors;
constraint factors;
technology acceptance model;
technology-organization-environment framework;
diffusion of innovation theory
- From:
China Pharmacy
2026;37(11):1478-1484
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVE Identify the incentive and constraint factors of artificial intelligence （AI） application in the pharmaceutical field， and promote the application of AI in the field of pharmacy. METHODS Based on the technology acceptance model （TAM）， technology-organization-environment （TOE） framework， and diffusion of innovation theory （DOI）， a TAM-TOE-DOI integrated framework was constructed through a four-stage research process of “theoretical review → dimension mapping → mechanism integration → proposition development”. Combining the analytical pathways of the above three theories in AI application in pharmacy with the integration mechanisms and core propositions of the TAM-TOE-DOI， literature review and deductive reasoning were employed to systematically identify the incentive and constraint factors of AI application in pharmacy from three levels：micro （TAM）， meso （TOE）， and macro （DOI）， and to propose optimization strategies. RESULTS & CONCLUSIONS At the micro level， the efficiency transformation and quality improvement brought by AI technology were the main incentive factors for perceived usefulness， while technological complexity and algorithmic opacity were the main constraint factors for perceived ease of use. At the meso level， the completeness of technological infrastructure， the strength of top management support and innovation climate， as well as external institutional pressure and competitive driving forces were the core incentive factors， whereas scarcity of organizational resources and talent shortage were the main constraint factors. At the macro level， relative advantage and observability were typical incentive factors， while technological complexity was a typical constraint factor. China’s health administration， medical insurance authorities， and other relevant departments should coordinate efforts at the macro， meso， and micro levels to advance AI application in pharmacy： optimizing human-computer interaction and implementing tiered training programs at the micro level； reinforcing organizational support systems and capacity building at the meso level； dismantling data barriers and building social trust at the macro level. Differentiated implementation pathways should be developed for medical institutions at different tiers.
