Developing a model for evaluating and improving the quality of healthcare services.

With the improvement of people's living standards, the demand for medical services continues to grow, and the quality of medical services urgently needs to be systematically optimized. However, traditional optimization paths often only focus on a single perspective such as patient satisfaction or the performance of medical institutions, lacking multi-subject collaboration and process closed-loop analysis. Therefore, a service quality optimization model integrating the Analytic Hierarchy Process, the SERVQUAL model, and Six Sigma tools was constructed in this study. Based on the defect identification results in the service process, a questionnaire was designed and quantitatively evaluated using the Likert five-point scale. The research subjects were community hospitals in Southwest China, and the data collection period lasted from 2022 to 2023. The results show that the model performs exceptionally well in sampling sufficiency in variable groups 2 and 4, with KMO values of 0.99 and 0.98 respectively. There is a significant correlation among the variable groups, with p values ranging from 0.024 to 0.031, meeting the requirements of factor analysis. The problem resolution rate of the optimized model reached 96% within 80 days. Compared with traditional models such as SERVQUAL, KANO, and 5GAP, the model in this study performs better in terms of responsiveness and consistency. The results indicate that the model can effectively identify service issues in healthcare and significantly improve service quality in community hospitals after optimization. This study constructs a sound optimization path for healthcare services, providing new ideas for the development of healthcare services.