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.

In China, emergency room residents (EMRs) generally face high working intensity. It is particularly important to arrange the working shifts of EMRs in a scientific way to balance their work and rest time. However, in existing studies, most of the scheduling models are based on the individual doctor or nurse as a unit, less considering the actuality of operation and management of emergency department (ED) in large public hospitals in China. Besides, the depiction of the hard and soft constraints of EMR scheduling in China is insufficient. So in order to obtain the scientific and reasonable scheduling shifts, this paper considers various management rules in a hospital, physicians’ personal preferences, and the time requirements of their personal learning and living and takes the minimum deviation variables from the soft constraints as the objective function to construct a mixed integer programming model with the doctor group as the scheduling unit. The analytic hierarchy process (AHP) is used to determine the weights of deviation variables. Then, IBM ILOG CPLEX 12.8 is used to solve the model. The feasibility and effectiveness of the scheduling method are verified by the actual case from West China Hospital of Sichuan University. The scheduling results can meet the EMRs’ flexible work plans and the preferences of the doctor teams for the shifts and rest days. Compared with the current manual scheduling, the proposed method can greatly improve the efficiency and rationality of shift scheduling. In addition, the proposed scheduling method also provides a reference for EMR scheduling in other China’s high-grade large public hospitals.

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.

IntroductionHeadaches, dizziness and memory loss of unspecific causes are the most common non-acute ischemia symptoms in the ageing population, which are often associated with cerebral small vessel disease (CSVD) imaging markers; however, there is insufficient evidence concerning their association with the development of cognitive decline. This study aims to investigate risk factors, clinical course, cerebral and retinal imaging changes, proteomics features of non-symptomatic ischaemia symptomatic patients with cognitive decline.Methods and analysisThe Non-Acute Symptomatic Cerebral Ischemia Registration study is a multicentre, registry-based, prospective observational study, is designed to investigate the cognitive decline in non-acute ischaemia symptomatic patients. We will recruit 500 non-acute ischaemia symptomatic patients from four tertiary hospitals in China. For this study, non-acute ischaemia symptoms will be defined as headaches, dizziness and memory loss. Patients with headaches, dizziness or memory loss over 50 years of age will be included. Clinical features, cognitive assessment, cerebral and retinal imaging data, and a blood sample will be collected after recruitment. Patients will be followed up by structured telephone interviews at 1, 2, 3, 4, 5 years after recruitment. This study will improve our knowledge of the development of cognitive decline in non-acute ischaemia symptomatic patients and factors affecting the cognitive outcomes, which will eventually elucidate underlying pathways and mechanisms of cognitive decline in these patients and facilitate the optimisation of individualised interventions for its prevention and treatment.Ethics and disseminationEthics approval is obtained from The Biomedical Research Ethics Committee of West China Hospital, Sichuan University (Reference No. 2016 (335)). We will present our findings at national and international conferences and peer-reviewed journals in stroke and neurology.Trial registration numberChiCTR-COC-17013056.