Search

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., (© 2021 John Wiley & Sons Ltd.)

Blood centers in large hospitals in China are facing serious problems, including complex patient queues and inflexible nursing schedules. This study is aimed at developing a flexible scheduling method for blood center nurses. By systematically analyzing the constraints that affect scheduling, a flexible scheduling model is established based on queuing theory and mixed integer programming. This combined model can reasonably determine the number of nurses required during a given working period and flexibly arrange nursing schedules while ensuring sufficient rest periods for individual nurses. Results of numerical studies conducted using data from a large hospital in China show a significant improvement in patient waiting time performance metrics over the hospital's current practice. In addition, the nurses' workloads and rest periods are well balanced, indicating that the proposed method can effectively and flexibly arrange nursing shifts in blood centers. [ABSTRACT FROM AUTHOR]