Data-Driven Methods for Blood Supply Chain Management Using Electronic Health Record (EHR) Data

点击翻译按钮获取中文摘要

Operations research (OR) and machine learning (ML) are often viewed as two distinct and alternative approaches for data-driven decision making: OR contains a wealth of methods for decision optimization based on distributional assumptions, whereas ML provides solutions using real and complex data, however, often lacks interpretability. Biostatistics are the foundation for health data science, encompassing study design, data collection, and result interpretation. Integrating ML, OR, and biostatistics can bridge health informatics, decision making, and knowledge translation in healthcare. Blood supply chain management (BSCM) is an important and challenging research area: important because of its value for human life; yet challenging due to its complexity including various product/patient characteristics and many uncertain factors from healthcare systems, industries, and governments. Inefficient BSCM not only leads to high healthcare costs, but also results in harm for blood donors and patients. Frequent over-ordering decisions for blood are often made at hospital blood banks, leading to a negative snowball effect impacting a series of decisions as one moves down the chain. Can careful integrations of ML, OR, and biostatistics offer more interpretable and implementable data-driven solutions to BSCM? My recent work on red blood cell (RBC) inventory management shows the potential impacts of such methodology developments in this problem domain. My long-term research program focuses on developing data-driven methods for blood inventory management using large electronic health record (EHR) data. Over the next five years, my research objectives are to: 1. Develop multi-product multi-facility data-driven decision models for production planning and inventory management using EHR data. o Develop new demand forecasting models for multi-product multi-facility predictions considering hierarchical time series using large scale EHR data to improve forecast accuracy. o Formulate integrated data-driven decision models that consolidate the production and distribution of multiple blood products while accounting for cross-product interactions, blood age, delivery frequency, and healthcare polices. o Initiate federated learning strategies for real-world implementation at local hospitals. 2. Develop new performance evaluation methods for data-driven decision models involving time series forecasting and optimization. o Evaluate data-driven decision models using existing performance metrics through different variants of cross-validation and out-of-sample approaches. o Develop new adaptive performance evaluation methods for data-driven decision models that require periodical retraining and validation. This research is important because it will lead to high quality integrated methodologies with great potential for real-world implementation. Canadians will benefit from the resulting data-driven decision models and evaluation methods to improve BSCM efficiency.