Exploiting new and abundant healthcare data to develop novel operational research methodologies

利用新的、丰富的医疗数据来开发新颖的运筹学方法

• 批准号: RGPIN-2020-05825
• 负责人: Vanberkel, Peter
• 金额: $ 1.89万
• 依托单位: Dalhousie University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=717803
• 关键词: Exploiting; new; abundant; healthcare; data

The complexity and challenges associated with healthcare delivery has proved a fruitful area to practice Operational Research (OR) and Industrial Engineering for several decades. Research projects and outputs have advanced the science of these disciplines and improved healthcare systems. The importance of this partnership is poised to grow as healthcare providers invest more in data Analytics (infrastructure and professional human resources) and continue to look to universities to help with their most complex challenges. Healthcare organizations are creating data at a fascinating pace - the volume of healthcare data doubles approximately every 36 months. This data promises to transform how decisions are made and how healthcare is delivered. There currently exists a disconnect between the amount of data and the amount of improvement being delivered. How to use OR to capitalize on new and abundant sources of healthcare data for making better decisions is the overarching thrust of this research proposal. Specifically, in this proposal we are investigating: 1) how to use data to more accurately describe the system understudy (e.g. patient flow patterns) and 2) how to use data to more accurately describe stochastic variables. As such we can hone our OR models in on better understood system structures with less uncertainty. Through this process, we expect to inform better healthcare decisions, to advance OR methods, and to compare, contrast, and combine Analytics and OR methodologies. The overall approach to incorporating data, Analytics and OR will provide a framework for other researchers and healthcare organizations looking to capitalize on newly available data. This program of research is motivated by real problems faced by healthcare providers and as such, will continue to positively impact healthcare delivery in Nova Scotia and around the world. As healthcare problems require different OR methods, this research program will continue to advance the science of OR by identifying new problems and developing novel solution approaches.

几十年来，与医疗服务相关的复杂性和挑战已被证明是实践运筹学（OR）和工业工程的一个富有成效的领域。研究项目和成果推动了这些学科的科学发展，并改善了医疗保健系统。随着医疗保健提供商在数据分析（基础设施和专业人力资源）方面投入更多资金，并继续寻求大学帮助解决最复杂的挑战，这种合作关系的重要性将不断增长。 医疗保健组织正在以惊人的速度创建数据-医疗保健数据量大约每36个月翻一番。这些数据有望改变决策的制定方式和医疗保健的提供方式。目前，数据量和正在交付的改进量之间存在脱节。如何使用OR来利用新的和丰富的医疗保健数据源来做出更好的决策是本研究提案的首要目标。具体而言，在本提案中，我们正在研究：1）如何使用数据更准确地描述待研究系统（例如，患者流模式），以及2）如何使用数据更准确地描述随机变量。 因此，我们可以在更好地理解系统结构的基础上磨练我们的OR模型，减少不确定性。 通过这一过程，我们希望为更好的医疗保健决策提供信息，推进OR方法，并比较、对比和联合收割机分析和OR方法。 整合数据、分析和OR的整体方法将为其他希望利用新可用数据的研究人员和医疗保健组织提供一个框架。这项研究计划的动机是医疗保健提供者面临的真实的问题，因此，将继续积极影响新斯科舍省和世界各地的医疗保健服务。由于医疗保健问题需要不同的OR方法，该研究计划将通过识别新问题和开发新的解决方案来继续推进OR科学。