Reconstructing pharmaceutical service competency framework: development of AI-informed competency indicators and localized practices in China

• DOI: 10.55670/fpll.futech.4.2.7
• Publication Date: 2025-05-08T02:40:42Z
• Authors: Yuqiu Wang; Hazrina Hamid

This study introduces an innovative method for reconstructing pharmaceutical service competency frameworks. The approach integrates artificial intelligence technologies with localization practices specific to the Chinese context. Employing a mixed-methods sequential exploratory design, we analyzed six major international frameworks using natural language processing and machine learning techniques extract 4,782 unique statements, which were subsequently classified 91.4% accuracy into relevant domains. resulting preliminary integrated framework—comprising 5 domains, 24 competencies, 103 behavioral indicators—underwent through modified Delphi process involving 32 stakeholders verification via national survey of 456 pharmacists across 18 provinces. Implementation diverse healthcare settings resulted in significant improvements quality metrics, including 23.7% reduction medication errors (p<0.01) 18.6% increase patient satisfaction. Cross-setting analysis revealed variable adaptability, implementation feasibility scores ranging from 4.7/5 tertiary hospitals 3.2/5 rural community pharmacies. Four critical success factors effective framework adoption identified: institutional leadership engagement, integration existing systems, phased implementation, dedicated training resources. framework's distinctive features include competencies addressing traditional medicine modern pharmacy practice modular structure enabling context-specific adaptation while maintaining core standards. research contributes bridging gap between global standards local realities development, demonstrating potential AI-informed approaches enhance relevance, efficiency, effectiveness contexts.