Blood Demand Forecasting and Supply Management: An Analytical Assessment of Key Studies Utilizing Novel Computational Techniques.

Use of data-driven methodologies in enhancing blood transfusion practices is rising, leveraging big data, machine learning, and optimization techniques to improve demand forecasting and supply chain management. This review used a narrative approach to identify, evaluate, and synthesize key studies that considered novel computational techniques for blood demand forecasting and inventory management through a search of PubMed and Web of Sciences databases for studies published from January 01, 2016, to March 30, 2023. The studies were analyzed for their utilization of various techniques, and their strengths, limitations, and areas for improvement. Seven key studies were identified. The studies focused on different blood components using various computational methods, such as regression, machine learning, hybrid models, and time series models, across different locations and time periods. Key variables used for demand forecasting were largely derived from electronic health record data, including clinical related predictors such as laboratory test results and hospital census by location. Each study offered unique strengths and valuable insights into the use of data-driven methods in blood bank management. Common limitations were unknown generalizability to other healthcare settings or blood components, need for field-specific performance measures, lack of ABO compatibility consideration, and ethical challenges in resource allocation. While data-driven research in blood demand forecasting and management has progressed, limitations persist and further exploration is needed. Understanding these innovative, interdisciplinary methods and their complexities can help refine inventory strategies and address healthcare challenges more effectively, leading to more robust, accurate models to enhance blood management across diverse healthcare scenarios.

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.