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American Red Cross uses analytics-based methods to improve blood-collection operations

机译:美国红十字会使用基于分析的方法来改善血液采集操作

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This article describes how the American Red Cross uses data analytics and operations research to improve the cost effectiveness of its blood collection operations. The Red Cross supplies 40% of all the blood products to meet 3,000 hospitals around the country to manage more than 67,000 disasters per year worldwide. The Red Cross has more than a million volunteers, 30,000 employees, 650 chapters and 36 major regions. More than 36,000 blood donations are required per day to meet the above requirements. Because of short supply and its perishable nature, collection and timely processing are critical. In spite of intense media coverage less than 5% of eligible donors are donating blood. The increase in demand is double in comparison with the increase in collection. Blood collection is a complex process and is either through automated blood collection (apheresis) or through regular whole blood donation. In apheresis, the required blood components like plasma or platelets are collected from the donor, and during whole blood collection the donor gives whole blood. From the whole blood, cryoprecipitate (cryo) is produced and then frozen into fresh frozen plasma within eight hours of collection. For most other blood products the duration between collection and processing is at least 24 hours. Collecting blood for cryo is more problematic due to short time period compared to collection for other blood products. About 20% of collected blood is used for cryo production since it is used for transfusions, clotting, hemorrhaging, organ transplants and fibrinogen deficiencies. The Red Cross has 23 manufacturing units servicing 36 regions within the nation. Each manufacturing facility serves one or more regions. The cryo collection production occurs in twelve of these manufacturing facilities. The study was undertaken as a joint project of the Red Cross and Georgia Tech to improve cost effectiveness of whole blood collection at the Douglasville-Atlanta manufacturing facility which serves four states and more than 120 hospitals in the Red Cross Southern Region. The article focuses on implementation aspects of the decision support tool developed for the use by practitioners. (12 refs.)
机译:本文介绍了美国红十字会如何利用数据分析和运营研究来提高其采血业务的成本效益。红十字会提供全部血液产品的40%,用于满足全国3,000所医院的需要,每年可处理全球67,000多次灾难。红十字会拥有超过一百万名志愿者,30,000名员工,650个分会和36个主要地区。每天需要超过36,000次献血才能满足上述要求。由于供应短缺及其易腐性质,收集和及时处理至关重要。尽管媒体广泛报道,但仍有不到5%的合格献血者在献血。与收集的增加相比,需求的增加是原来的两倍。采血是一个复杂的过程,可以通过自动采血(自动采血)或通过定期全血捐赠。在单采血液分离术中,从供体中收集所需的血液成分(例如血浆或血小板),在全血收集过程中,供体会提供全血。从全血中产生冷沉淀(cryo),然后在收集后的八小时内冷冻成新鲜的冷冻血浆。对于大多数其他血液制品,采集和处理之间的持续时间至少为24小时。与其他血液产品的采集相比,由于时间短,因此为低温采集血液更成问题。因为大约有20%的血液用于输血,凝血,出血,器官移植和纤维蛋白原缺乏,所以这些血液被用于冷冻生产。红十字会拥有23个制造部门,为全国36个地区提供服务。每个制造厂都为一个或多个区域提供服务。在其中的十二个生产设施中进行冷冻收集生产。这项研究是红十字会和佐治亚理工学院的一项联合项目,旨在提高道格拉斯维尔-亚特兰大制造工厂的全血收集成本效益,该工厂为红十字会南部地区的四个州和120多家医院提供服务。本文重点介绍了为从业人员使用而开发的决策支持工具的实现方面。 (12个参考)

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