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Model Construction for Comprehensive Evaluation of Quality of Care Based on Multidimensional Indicators / 中国卫生统计
Chinese Journal of Health Statistics ; (6): 700-704, 2017.
Article in Chinese | WPRIM | ID: wpr-662359
ABSTRACT
Objective The study aimed to construct a composite score method based on multidimensional quality indi-cators and conduct simulation trials to validate the method. Besides,the quality of breast cancer care for both hospitals and sur-geons was evaluated by the method. Methods The two-parameter logistic latent variable model was constructed as measure-ment model;the latent variables in the measurement model were further incorporated into multilevel structural model as depend-ent variables and one pseudo level was designed for representing multiple latent variables. MCMC method was used to estimate model parameters. Three level and two-dimensional latent variable model was used to analyze the actual data. Results The sim-ulation study showed that the number of quality indicators and surgeons should not be less than 20 to obtain efficient estimate of quality of care;the multilevel and multidimensional latent variable model was applied to analyze the data;surgeons and hospitals who provided superior quality of breast cancer diagnosis and operative procedure were obtained. Conclusion The newly con-structed multilevel and multidimensional latent variable model could effectively address the hieratical structure in quality of care data as well as the multidimensional nature of quality of care,thus,the model can be used to comprehensively and rationally as-sess the quality of care;comprehensive evaluation of quality of care provided ground for linking the ranking of hospitals and per-formance appraisal of surgeons to the quality of care.

Full text: Available Index: WPRIM (Western Pacific) Type of study: Prognostic study Language: Chinese Journal: Chinese Journal of Health Statistics Year: 2017 Type: Article

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Full text: Available Index: WPRIM (Western Pacific) Type of study: Prognostic study Language: Chinese Journal: Chinese Journal of Health Statistics Year: 2017 Type: Article