The successful implementation of an automated institution-wide assessment of hemoglobin and ABO typing to dynamically estimate red blood cell inventory requirements.

BACKGROUND: Blood bank inventories must balance adequate supply with minimal outdate rates. The day-to-day practice of ordering red blood cell (RBC) inventory usually involves manually comparing current inventory levels with predetermined thresholds calculated from historical usage and ordering the difference. To date, there have been no published methods for ordering RBC inventory based on laboratory characteristics of admitted patients. STUDY DESIGN AND METHODS: We designed and implemented a blood ordering algorithm to provide a more accurate measure of predicted RBC utilization in our institution. Cerner Command Language (Cerner Millennium) was used to extract and combine historical RBC unit usage, current inventory levels, and system-wide hematology values and blood groups. This report contains a suggested order based on current inventory, historical inventory data, ABO group, and the current "anemia index" for the institution. RESULTS: The mean daily total RBC inventory was significantly reduced after implementation (401.7 units vs. 309.0 units, p < 0.05). There was a significant reduction in monthly RBC outdates in this period (19.1 vs. 8.1, p < 0.05). The age of RBCs at time of transfusion was reduced as well. CONCLUSION: We developed a novel algorithm that automatically generates a suggested RBC inventory order using real-time hospital-wide survey of patient ABO typing, hematology values, and historical data. After implementation of the algorithm we demonstrated a significant reduction in daily inventory levels and RBC outdate rates.

Process Mining Uncovers Actionable Patterns of Red Blood Cell Unit Wastage in a Health Care Network.

Packed red blood cell transfusions are integral to the care of the critically and chronically ill patient, but require careful storage and a large, coordinated network to ensure their integrity during distribution and administration. Auditing a Transfusion Medicine service can be challenging due to the complexity of this network. Process mining is an analytical technique that allows for the identification of high-efficiency pathways through a network, as well as areas of challenge for targeted innovation. Here, we detail a case study of an efficiency audit of the Transfusion Medicine service of the Nova Scotia Health Administration Central Zone using process mining, across a period encompassing years prior to, during, and after the acute COVID-19 pandemic. Service efficiency from a product wastage perspective was consistently demonstrated at benchmarks near globally published optima. Furthermore, we detail key areas of continued challenge in product wastage, and suggest potential strategies for further targeted optimization.