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

使用电子健康记录 (EHR) 数据进行血液供应链管理的数据驱动方法

• 批准号: RGPIN-2022-02999
• 负责人: Li, Na
• 金额: $ 1.97万
• 依托单位: University of Calgary
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=749606
• 关键词: Data; Driven; Methods; Blood; Supply

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.

操作研究（OR）和机器学习（ML）通常被视为数据驱动决策的两种独特而替代的方法：或包含基于分布假设的大量决策方法，而ML使用真实和复杂的数据提供解决方案，但是通常缺乏可解释性。生物统计学是健康数据科学的基础，涵盖了研究设计，数据收集和结果解释。整合ML或生物统计学可以弥合医疗保健中的健康信息学，决策和知识翻译。血液供应链管理（BSCM）是一个重要且具有挑战性的研究领域：重要的是，由于其对人类生命的价值；由于其复杂性包括各种产品/患者特征以及医疗保健系统，行业和政府的许多不确定因素，因此具有挑战性。效率低下的BSCM不仅会导致较高的医疗费用，而且还会对献血者和患者造成伤害。经常在医院的血库做出频繁的血液决定，导致负面的雪球效应，影响一系列的决定，因为人们向下移动。 ML或生物统计学的仔细集成可以为BSCM提供更多可解释和可实施的数据驱动解决方案吗？我最近关于红细胞（RBC）库存管理的工作显示了这种方法发展在此问题领域的潜在影响。我的长期研究计划着重于使用大型电子健康记录（EHR）数据开发以数据驱动的方法进行血液库存管理的方法。在接下来的五年中，我的研究目标是：1。使用EHR数据开发用于生产计划和库存管理的多产品数据驱动的决策模型。 o考虑使用大型EHR数据的层次时间序列，为多产品多物质预测开发新的需求预测模型，以提高预测准确性。 o制定综合数据驱动的决策模型，这些决策模型巩固了多种血液产品的生产和分布，同时考虑了交叉产物相互作用，血液年龄，输送频率和医疗保健策略。 o在当地医院实施现实世界实施的联合学习策略。 2。为涉及时间序列预测和优化的数据驱动决策模型开发新的绩效评估方法。 o使用现有的绩效指标评估数据驱动的决策模型，通过交叉验证和样本外方法的不同变体。 o为需要定期重新验证和验证的数据驱动决策模型开发新的自适应绩效评估方法。这项研究很重要，因为它将导致具有现实世界实施潜力的高质量综合方法。加拿大人将受益于由此产生的数据驱动的决策模型和评估方法，以提高BSCM效率。