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Background: Managing Canada's immunoglobulin (Ig) product resource allocation is challenging due to increasing demand, high expenditure, and global shortages. Detection of groups with high utilization rates can help with resource planning for Ig products. This study aims to uncover utilization subgroups among the Ig recipients using electronic health records (EHRs). Methods: The study included all Ig recipients (intravenous or subcutaneous) in Calgary from 2014 to 2020, and their EHR data, including blood inventory, recipient demographics, and laboratory test results, were analyzed. Patient clusters were derived based on patient characteristics and laboratory test data using K‐means clustering. Clusters were interpreted using descriptive analyses and visualization techniques. Results: Among 4112 recipients, six clusters were identified. Clusters 1 and 2 comprised 408 (9.9%) and 1272 (30.9%) patients, respectively, contributing to 62.2% and 27.1% of total Ig utilization. Cluster 3 included 1253 (30.5%) patients, with 86.4% of infusions administered in an inpatient setting. Cluster 4, comprising 1034 (25.1%) patients, had a median age of 4 years, while clusters 2–6 were adults with median ages of 46–60. Cluster 5 had 62 (1.5%) patients, with 77.3% infusions occurring in emergency departments. Cluster 6 contained 83 (2.0%) patients receiving subcutaneous Ig treatments. Conclusion: The results identified data‐driven segmentations of patients with high Ig utilization rates and patients with high risk for short‐term inpatient use. Our report is the first on EHR data‐driven clustering of Ig utilization patterns. The findings hold the potential to inform demand forecasting and resource allocation decisions during shortages of Ig products. [ABSTRACT FROM AUTHOR]

Background: The demand and supply of blood are highly variable over time. Blood inventory management that relies heavily on experience‐based decisions may not be adaptive to real demand, leading to high operational costs, wastage, and shortages. Methods: We combined statistical modeling, machine learning, and optimization methods to develop a data‐driven demand forecasting and inventory management strategy for red blood cells (RBCs). We then used the strategy to inform daily blood orders. A secondary semi‐weekly (twice per week) ordering strategy was developed to handle the last‐mile split delivery problem for blood suppliers, characterized by multi‐deliveries to the same location multiple times during a short period of time. Both strategies were evaluated using the TRUST database including all patient data across four hospitals in Hamilton, Ontario. Results: We identified 227,944 RBC transfusions for 40,787 patients in Hamilton, Ontario from 2012 to 2018. The predicted daily demand from the hybrid demand forecasting model was not significantly different from the actual daily demand (paired t‐test p‐value = 0.163); however, the proposed daily ordering quantity from the model was significantly lower than the actual ordering quantity (p‐value <0.001). The proposed daily ordering strategy reduced inventory levels by 38.4% without risk of shortages, leading to an overall cost reduction of 43.0% (95% confidence interval [CI]: 42.3%, 43.7%) compared with the actual cost. The semi‐weekly ordering strategy reduced ordering frequency by 62.6% (95% CI: 61.5%, 63.7%). Conclusion: The proposed data‐driven ordering strategy combining demand forecasting and inventory optimization can achieve significant cost savings for healthcare systems and blood suppliers. [ABSTRACT FROM AUTHOR]