Healthcare quality evaluation of tertiary public hospitals in ethnic border regions under China's performance assessment system-based on the entropy weight TOPSIS method and RSR fuzzy set.

Objective: Performance evaluation is critical for improving healthcare quality and service delivery. This study analyzes the healthcare quality of tertiary hospitals across various cities in Guangxi under China's public hospital performance assessment policy to identify influencing factors and propose targeted improvement strategies for the national evaluation system.

Methods: The healthcare quality of general hospitals in Guangxi from 2019 to 2022 was evaluated using a fuzzy set entropy-weighted TOPSIS and RSR method, followed by a comprehensive city-level ranking.

Results: Entropy-weighted TOPSIS revealed the top three weighted indicators: (1) number of referred-out patients, (2) low-risk group mortality rate, and (3) proportion of reviewed prescriptions. The quality of H7 and H11 improved significantly, while H9 consistently ranked in the top 4. The RSR stratification classified H1, H2, H8, and H9 as high-performing, H6 and H12 as low-performing, and H4, H5, H6, H12, and H13 as persistently below average for four consecutive years. Using the fuzzy set method, H1, H9, H11, and H1 were ranked as the highest-performing cities from 2019 to 2022, respectively.

Conclusion: There are minor discrepancies among the three methodologies, but the trends remain largely consistent. The fuzzy-combined approach provides more robust evaluations, which offers actionable insights for healthcare quality enhancement and management standardization. Consequently, hospitals should improve the quality of services and sustain the core competitiveness of public hospitals by implementing tiered healthcare systems and standardized prescription review protocols.