Process mining is an underutilized clinical research tool in transfusion medicine

BACKGROUND To understand inventory performance, transfusion services commonly use key performance indicators (KPIs) as summary descriptors of inventory efficiency that are graphed, trended, and used to benchmark institutions. STUDY DESIGN AND METHODS Here, we summarize current limitations in KPI‐bas...

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Bibliographic Details
Published in:Transfusion (Philadelphia, Pa.) Pa.), 2017-03, Vol.57 (3), p.501-503
Main Authors: Quinn, Jason G., Conrad, David M., Cheng, Calvino K.
Format: Article
Language:English
Subjects:
Blood Banks
Blood Transfusion
Data Mining
Humans
Information systems
Inventories, Hospital
Inventory
Inventory management
Laboratories
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Summary:BACKGROUND To understand inventory performance, transfusion services commonly use key performance indicators (KPIs) as summary descriptors of inventory efficiency that are graphed, trended, and used to benchmark institutions. STUDY DESIGN AND METHODS Here, we summarize current limitations in KPI‐based evaluation of blood bank inventory efficiency and propose process mining as an ideal methodology for application to inventory management research to improve inventory flows and performance. RESULTS The transit of a blood product from inventory receipt to final disposition is complex and relates to many internal and external influences, and KPIs may be inadequate to fully understand the complexity of the blood supply chain and how units interact with its processes. Process mining lends itself well to analysis of blood bank inventories, and modern laboratory information systems can track nearly all of the complex processes that occur in the blood bank. CONCLUSION Process mining is an analytical tool already used in other industries and can be applied to blood bank inventory management and research through laboratory information systems data using commercial applications. Although the current understanding of real blood bank inventories is value‐centric through KPIs, it potentially can be understood from a process‐centric lens using process mining.
ISSN:0041-1132
1537-2995
DOI:10.1111/trf.13995