Relationship between clinical blood demand and its influencing factors
Journal Title: Chinese Journal of Blood Transfusion - Year 2023, Vol 36, Issue 3
Abstract
Objective To study the relationship between the key influencing factors and the short, medium and long term blood demand, so as to provide basis for building a blood demand prediction model with less prediction error and practical guidance. Methods Through literature research, the influencing factors of blood demand were preliminarily determined. Questionnaires were designed and distributed to relevant experts, and factor analysis was carried out on the survey results to obtain key influencing factors through Delphi method. Results Through literature research, 19 influencing factors of clinical blood demand were obtained, including policy factors, medical service demand, medical technology level, regional population, population characteristics, population structure, medical resource, number of beds, culture, natural environment, operation, patients outside the region, blood use in different departments, blood infusion, time trend, emergencies and disasters, the condition of disasters, hospitals in disaster area, limited diagnosis and treatment ability. Through Delphi method and data analysis, six key factors affecting blood demand were obtained, namely sudden disaster, medical resource, environmental factor, population, bed number and blood infusion. Conclusion The influence of key factors on clinical blood demand was divided into multiple hierarchies. Blood infusion and sudden disaster were short-term influencing factors. Medical resource, population and number of beds were medium influencing factors. Environmental factor was long-term influencing factor. Short, medium and long-term influencing factors were interrelated, and have different impacts on clinical blood demand. Based on the interaction relationship, a three-dimensional mathematical model of influencing factors of clinical blood demand was established, which provided a preliminary research basis for building a blood demand prediction model with less prediction error and practical guidance.
Authors and Affiliations
Shuhong XIE, Sijing ZHANG, Qi XIAO, Weibin YAN
Keywords
Related Articles
- EP ID EP738513
- DOI 10.13303/j.cjbt.issn.1004-549x.2023.03.014
- Views 55
- Downloads 0
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