Summary To address, among other issues, the regional and international challenges of the heavy health care burden caused by an aging population, integrated care organizations (ICOs) were proposed at the end of the 20th century for health care delivery. However, the implementation of ICOs has not progressed smoothly, and the current results have not eliminated the imbalance of medical service capabilities among hospitals of different levels. To make up for the deficiency in the current evaluation system at ICOs and offer suggestions for improved sustainable health planning and management, this study establishes a balanced scorecard based on a comprehensive measurement system valid for a Chinese ICO by surveying the staff at the West China Hospital ICO. This study collected valid responses from 216 professional staff members at the ICO via questionnaires. K‐means clustering and the coefficient of variation method were used to evaluate the weights of the first‐ and second‐level indicators. The results show the importance ranking of the core perspectives of the ICO balanced scorecard in the following order: patient, internal process, learning and growth, and financial. The weight‐based analysis identified the importance ranking of all indicators and pointed to the areas that require close attention in future ICO planning and management.

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Purpose: Provide new methods to predict the number of hospital blood collections. Methods:The registered outpatients and blood collection patients in a large hospital in China in the period from March 2018 to April 2019 were enrolled in the study. Firstly, we analyzed the time series characteristics of the daily blood collection patients and their correlation with the number of daily outpatients. Then, we used the time series ARIMA and linear regression methods to build the periodic trend model of the blood collections number prediction and the regression prediction model with the number of registered outpatients as an independent variable. Finally, we built a combined prediction model considering mixed time series to predict the number of blood collections in the hospital. Results:The combined prediction model has a higher accuracy and can better explore the characteristics of the number of blood collections compared with other models.It can also give some suggestions for a reasonable blood collection management. Conclusion:The combined prediction model of mixed time series can reflect the change in the blood collections number due to the influence of internal and external factors and can realize the blood collection prediction with a higher accuracy providing a new method for the prediction of the blood collections number.