State-variable modeling for healthcare planning and scheduling

用于医疗保健规划和调度的状态变量建模

• 批准号: RGPIN-2019-05517
• 负责人: HashemiDoulabi, Hossein
• 金额: $ 1.89万
• 依托单位: Concordia 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=715589
• 关键词: State; variable; modeling; healthcare; planning

In 2017, the total health budget in Canada was $242 billion accounting for 11.5% of Canada's gross domestic product (GDP). Effective decision making in healthcare systems is very crucial and can result in significant savings. Multiple sources of uncertainty in healthcare systems is one of the main challenges to make effective decisions. From this perspective, it is essential to be able to solve stochastic optimization problems efficiently. This research program develops novel stochastic optimization models for decision making under uncertainty with applications in healthcare planning and scheduling problems. In this context, we will work on operating room planning and scheduling, outpatient appointment scheduling, and scheduling of production and delivery of radio-pharmaceutical by considering various uncertainty factors. Operating room planning and scheduling (ORPS) is one of the most important decision making problems in hospitals that accounts for more than 40% of their total annual budget. Outpatient appointment scheduling (OAS) is also another important instance of healthcare operations scheduling that is very critical for the efficient use of human medical resources such as physicians and equipment for diagnostic tests (e.g., CT-scan, MRI-scan, ultrasound). Available models in the literature can only solve very simplified stochastic versions of the ORPS and OAS problems. This research program proposes significantly more effective stochastic optimization models that are capable of solving considerably larger instances with an exponential number of stochastic scenarios. The proposed models will simultaneously address many uncertainty factors in these problems such as uncertainty in surgical durations and recovery times of patients in wards for ORPS and uncertainty in visit times, patients' arrival times, and the possibility of no-shows in OAS. We also consider scheduling in radio-pharmaceutical imaging that is an advance technology in diagnosing bone issues. It has a market of $3.5 billion that is predicted to increase to $5.26 billion by 2023. From a managerial viewpoint, optimal timing for production and delivery of radio-pharmaceuticals is very critical as they are expensive materials with very short life deteriorating in exponential rates. The state-of-the-art approaches are mainly deterministic models ignoring different sources of uncertainty. In this research direction, we will propose novel stochastic optimization models addressing various uncertainties such as stochastic-time-dependent travel times, uncertainty in imaging durations due to the change in the health's situations of patients because of the injection, and no-shows.

2017年，加拿大卫生预算总额为2420亿加元，占加拿大国内生产总值（GDP）的11.5%。医疗保健系统中的有效决策非常重要，可以节省大量成本。医疗保健系统中的多种不确定性来源是做出有效决策的主要挑战之一。从这个角度来看，能够有效地解决随机优化问题至关重要。该研究项目开发了新颖的随机优化模型，用于不确定性下的决策，并应用于医疗保健规划和调度问题。在此背景下，我们将考虑各种不确定因素，对手术室规划和调度、门诊预约调度以及放射性药物的生产和交付调度进行研究。 手术室规划和调度 (ORPS) 是医院最重要的决策问题之一，占医院年度总预算的 40% 以上。门诊预约安排 (OAS) 也是医疗保健运营安排的另一个重要实例，对于有效利用医生和诊断测试设备（例如 CT 扫描、MRI 扫描、超声）等人力医疗资源非常关键。文献中的可用模型只能解决 ORPS 和 OAS 问题的非常简化的随机版本。该研究计划提出了更有效的随机优化模型，能够解决具有指数数量的随机场景的相当大的实例。所提出的模型将同时解决这些问题中的许多不确定性因素，例如 ORPS 病房患者的手术持续时间和恢复时间的不确定性，以及就诊时间、患者到达时间的不确定性以及 OAS 缺席的可能性。 我们还考虑放射性药物成像的调度，这是诊断骨骼问题的先进技术。它的市场规模为 35 亿美元，预计到 2023 年将增至 52.6 亿美元。从管理角度来看，放射性药物生产和交付的最佳时机非常关键，因为它们是昂贵的材料，寿命非常短，并且会以指数速度恶化。最先进的方法主要是确定性模型，忽略了不同的不确定性来源。在这个研究方向中，我们将提出新颖的随机优化模型，解决各种不确定性，例如随机时间相关的旅行时间、由于注射导致患者健康状况变化而导致的成像持续时间的不确定性以及缺席。