Developing a Service Quality Standard for Collaborative Integrated Healthcare Institutions in Guangdong, China: A Delphi-Analytic Hierarchy Process Study

基于德尔菲法和层次分析法的研究:构建中国广东省协同整合医疗机构服务质量标准

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Abstract

BACKGROUND: To develop a service quality standard and provide a reference for standardizing the quality of integrated healthcare institutions cooperating between tertiary hospitals and pension institutions in China. The establishment of the scientific and quantifiable service quality standard for collaborative integrated healthcare institutions can provide clear guidance on service direction, content and processes for care institutions, ensuring the effective implementation and coordinated growth of healthcare services. METHODS: A preliminary service quality standard was developed through literature review and semi-structured interviews. Two rounds of Delphi experts' consultation were conducted, and the relative weights of indicators at each level were calculated by the Analytic Hierarchy Process. RESULTS: The service quality standard consists of 3 first-level, 14 second-level, and 63 third-level indicators. The coordination of experts' opinions, expressed by Kendall's W of Delphi methods, ranged from 0.209 to 0.252. And the highest weights by the Analytic Hierarchy Process were observed for: "Service process" (0.493) among first-level indicators; " Operational management of integrated healthcare services" (0.181) among second-level indicators; and "Establish a professional service team for integrated healthcare in tertiary hospitals including doctors, nurses, therapists, pharmacists" (0.066) among third-level indicators. CONCLUSION: The service quality standard developed in this study is systematic and scientific. It incorporates multi-dimensional service quality indicators through rigorous design and weight allocation, providing both a theoretical foundation and practical guidance for evaluating service quality under the "integrated healthcare contract" model. There are also geographic limitations to this study, and the samples need to be expanded in the future to verify generalizability.

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