Purpose: GLP-1 receptor agonists have attracted increasing attention due to their dual efficacy in glycemic control and weight reduction. Although several GLP-1 receptor agonists have been included in China’s National Reimbursement Drug List, their actual adoption at the hospital level still faces multiple challenges. A well-structured evaluation framework is urgently needed to facilitate their integration into hospital formularies. This study aims to develop a clinically comprehensive evaluation system for GLP-1 receptor agonists in public hospital drug selection, based on multicriteria decision analysis (MCDA). The goal is to provide a systematic and quantifiable tool to support the rational selection and implementation of GLP-1RAs in public hospitals, thereby enhancing the scientific basis of hospital formulary decisions. Patients and Methods: The study integrates the Delphi method and multi-criteria decision analysis (MCDA), utilizing literature review, expert consultations, and questionnaire surveys to select and assign weights to evaluation indicators. Results: A three-tier evaluation system was established, comprising four primary indicators, eight secondary indicators, and seventeen tertiary indicators. Among them, “Clinical Value” had the highest weight (0.5373), followed by “Hospital Admission Demand” (0.1924) and “Hospital Management” (0.1379), while “Economic Value” had the lowest weight (0.1324). At the tertiary level, “Cost-Effectiveness Advantage”, “Glycosylated Hemoglobin”, “Cardiovascular, Hepatic, and Renal Benefits”, “Addressing Unmet Clinical Needs”, and “Hypoglycemia Incidence” contributed most to decision-making. Conclusion: This study fills the gap in clinical evaluation research on GLP-1RA drugs in China. The proposed evaluation system provides scientific support for hospital pharmaceutical management, medical insurance inclusion, and drug procurement, promoting value-based hospital drug selection strategies. Keywords: GLP-1RA, clinical comprehensive evaluation, health technology assessment, MCDA
Jiang et al. (Sun,) studied this question.