Application of unsupervised machine learning to identify areas of blood product wastage in transfusion medicine. Issue 9 (26th February 2021)

• Record Type: Journal Article
• Authors: Xiang, Richard F.; Quinn, Jason G.; Watson, Stephanie; Kumar‐Misir, Andrew; Cheng, Calvino
• Is Part Of: Vox sanguinis. Volume 116:Issue 9(2021)
• Page Start: 955
• Page End: 964
• Publication Date: 2021-02-26
• Subjects: machine learning -- informatics -- transfusion medicine -- association rule mining -- inventory management -- product wastage; Blood -- Periodicals; Blood -- Transfusion -- Periodicals; Immunohematology -- Periodicals; Immunopathology -- Periodicals; 615.39
• DOI: 10.1111/vox.13089
• Languages: English
• ISSNs: 0042-9007

Abstract

Abstract: Background: Wastage of blood products can be a significant cost to blood banks. However, the cause of wastage is often complex and makes it difficult to determine wastage‐associated factors. Machine learning techniques may be useful tools to investigate these complex associations. We investigated whether unsupervised machine learning can identify patterns associated with wastage in our blood bank. Materials and methods: Data on red blood cells, platelets and frozen products were obtained from the laboratory information system of the Central Zone Blood Transfusion Services at Nova Scotia Health Authority. A total of 879 532 transactions were analysed by association rule mining, a type of machine learning algorithm. Associations with lift scores greater than 25 and with clinical relevance were flagged for further examination. Results: Association rule mining returned a total of 3355 associations related to wastage. Several notable associations were identified. For example, certain wards were associated with wastage due to thawing unused frozen products. Other examples included association between smaller blood banks and evening work shifts with product wastage due to excess time outside the laboratory or returning products with high temperatures. Conclusion: This paper demonstrates the effective use of unsupervised machine learning for the purpose of investigating wastage in a large blood bank. The use of association rule mining was able to identify wastage factors, which can help guide quality improvement initiatives. This technique can be automated to provide rapid analysis of complex associations contributing to wastage and could be utilized in modern blood banks.