Abstract:
Objective Based on the big data of the blood collection and supply business information system in the region, combined with the best storage time of red blood cells, we discuss the best red blood cell inventory in this region and the method of establishing and implementing a blood inventory early warning system.
Methods Through a retrospective analysis method, the annual red blood cell consumption, growth rate and average annual growth rate, the consumption and proportion of each blood type, the monthly average consumption and standard deviation of red blood cells, the clinical blood consumption plan and the blood best storage time limit, we calculated the reference value of the best monthly inventory of red blood cells of each blood type by the normal approximation method.
Results From 2016 to 2019, the amount of red blood cells in this region showed an upward trend year by year, with an average annual growth rate of 4.32%. The monthly average optimal inventory of type A, type B, type O and type AB red blood cells was (13 344.8nd type )U and(14 879.1nd type )U, (13 674.0nd type )U, (5 071.30nd ty)U, respectively. Combined with the actual work for each blood type, 14 times of the average daily blood consumption were the best stock of red blood cells, 5 times of the average daily blood consumption were the minimum stock (low stock early warning level), and 25 times of the average daily blood consumption were the highest stock (High inventory warning level 1).
Conclusion The optimal inventory is the basis for maintaining dynamic inventory monitoring and ensuring blood safety and effectiveness. This study provides a basis for establishing and improving the blood inventory early warning system, and playing a role in "shaving peaks and filling valleys" for blood inventory in blood collection and supply practices.
Key words:
Clinical blood demand,
Best stock of red blood cells,
Big data
Haizhong Fu, Xuemei Li, Minghui Li, Yuanbing Li, Yunsheng Yu, Yongzan Chen. Optimal inventory strategy of red blood cells based on big data of hospital blood volume[J]. Chinese Journal of Hygiene Rescue(Electronic Edition), 2022, 08(05): 280-284.