Evaluating the Effectiveness of AI-Enhanced 'One Body, Two Wings' Pharmacovigilance Models in China: A Nationwide Survey on Medication Safety and Risk Management

Jian Yang¹Yi Sun²Fan Li¹Quanzhi Wei¹Jincui Wei^3*Yingxiong Zhang^4*

• ¹Kunming Medical University, Kunming, China
• ²Yuxi Drug Evaluation Center, Yuxi, China
• ³Xinping Yi and Dai Autonomous County Drug Safety Monitoring and Evaluation Center, Yuxi, China
• ⁴Saling Pharmaceutical Technology Group Co. Ltd., Yuxi, China

Background: This study evaluates the effectiveness of AI-enhanced "One Body Two Wings" pharmacovigilance models in China, focusing on improving medication safety and risk management. As the pharmaceutical landscape grows more complex, integrating AI into pharmacovigilance offers the potential to enhance adverse drug reaction (ADR) detection and monitoring. Methods: A nationwide cross-sectional survey was conducted from June 25 to August 10, 2024, involving 1,000 participants from pharmacovigilance centers, hospitals, corporations, and the general public. Participants were recruited through stratified convenience sampling to ensure a broad geographical and professional representation. Data were collected through a validated questionnaire and analyzed using ANOVA, regression analysis, decision tree models, and random forest algorithms. To ensure the validity of the predictive models, resampling (SMOTE) and class weighting techniques were employed to address significant class imbalance in the outcome variable. Results: The survey revealed that 43% of participants were hospital staff and 46% had more than 10 years of experience, with these expert groups expressing strong support for AI's role. Path analysis indicated that AI's effectiveness in processing ADR reports was strongly related to enhanced monitoring capabilities (standardized path coefficient: 0.85). Furthermore, logistic regression identified the perceived effectiveness of information systems as a significant predictor of positive attitudes toward the model (odds ratio: 1.703). Crucially, a random forest model, adjusted for class imbalance, confirmed that information systems effectiveness was the most significant predictor of the model's success (mean importance: 0.53 ± 0.05), achieving robust performance with a weighted F1-score of 0.94 and an AUC-ROC of 0.89. Conclusion: The findings confirm AI's potential to enhance pharmacovigilance, especially in ADR monitoring. However, the study concludes that successful AI integration is predicated on a robust information systems infrastructure, which the data identified as the most critical foundational element. Therefore, optimizing pharmacovigilance in China requires prioritized investment in both this foundational IT and supportive organizational frameworks.