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

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