To explore how to effectively select key hospitals that need quality control management and quality control indicators, and try to establish a quality control management system based on big data analysis to accurately determine the direction and strategy of quality control management.

In this study, 25 quality control indicators of ultrasonic medicine department of 155 sentinel hospitals in Sichuan Province were analyzed by multivariate statistical analysis. First, Pearson correlation analysis was used to explore whether there was collinearity between the indicators. Then, multivariate quality control data were projected onto the main plane to explore the outliers, and the 95% confidence interval of statistical differences was given by using the minimum covariance determinant (MCD) algorithm. Finally, according to the score chart and load chart, the quality control index set which had the greatest contribution to the hospital abnormal clustering was detected.

Using the MCD algorithm, we can clearly identify hospitals with abnormal distribution of ultrasonic quality control indicators among the same level medical institutions. For example, it was found that in West China Second University Hospital & Sichuan University, the numbers of junior doctors and those with master's degree or above were higher than those of other hospitals based on the MCD algorithm combined with the load chart, indicating that talent reserve in this hospital was the most abundant. The analysis of the components of the main different indexes in the load chart showed that the main reasons for the quality control difference in the tertiary class A hospitals were the proportion of doctors aged 25-35 years, the proportion of residents, and the proportion of doctors with a bachelor's degree. The main reasons for the quality control difference in the tertiary class B hospitals were the proportion of outpatients, the proportion of inpatients, the proportion of doctors aged 36-45 years, and the rate of coincidence with pathological results. The main reasons for the quality control difference in the secondary class A hospitals were the proportion of doctors with a bachelor's degree, the proportion of doctors with an education level below bachelor's degree, the proportion of doctors aged >45 years, the proportion of doctors, and the staff/patient ratio in ultrasound department. The main reasons for the quality control difference in the secondary class B or lower hospitals were the proportion of doctors aged from 35 to 45 years and the proportion of outpatients.

Based on big data analysis of ultrasonic quality control data among different hospitals, we can accurately determine the hospitals that need quality control management and specific quality control indicators.