TY - JOUR
T1 - Machine Learning Model for Forecasting Daily Blood Product Demand in Tertiary Hospitals Using Surgical Schedules, Trauma Admissions, Transfusion History, Oncology Treatment Plans, and Inventory Depletion Patterns
AU - Andreas Müller
AU - Stefan Weber
AU - Julia Hoffmann
AU - Lukas Schneider
AU - Tobias Klein
JF - Journal of Health Informatics and Digital Systems
JO - J. Health Inform. Digit. Syst.
SN - 3149-8973
Y1 - 2024
VL - 4
IS - 1
DO - 10.68159/d585822177
SP - 86
N2 - Blood products are critical hospital resources with demand shaped by elective surgery, trauma, oncology treatment, and ongoing transfusion dependence. Volatility in daily use can create simultaneous risks of shortage and expiry-related wastage. Current inventory management often relies on par-level reordering and manual review of limited indicators. Such approaches may not anticipate daily demand shifts arising from multiple clinical drivers at the same time. This article develops a conceptual predictive model for forecasting daily blood product demand in tertiary hospitals. The model integrates surgical schedules, trauma admission patterns, transfusion history, oncology treatment plans, and inventory depletion data. The proposed approach uses time-series regression or gradient-boosted tree modelling trained on historical transfusion and hospital operations data. The model would output expected demand by blood product type for the next 24 hours. Conceptually, the model would provide a daily product-specific demand forecast with uncertainty bounds. It could flag days of expected high use driven by complex surgical lists, trauma activity, or planned oncology transfusion support. The model could support proactive, data-driven blood inventory management. It may help reduce emergency ordering, improve preparedness, and limit avoidable wastage.
UR - https://cirpublications.com/d585822177
ER -