Monitoring healthcare processes, such as surgical outcomes, with a keen focus on detecting changes and unnatural conditions at an early stage is crucial for healthcare professionals and administrators. In line with this goal, control charts, which are the most popular tool in the field of Statistical Process Monitoring, are widely employed to monitor therapeutic processes. Healthcare processes are often characterized by a multistage structure in which several components, states or stages form the final products or outcomes. In such complex scenarios, Multistage Process Monitoring (MPM) techniques become invaluable for monitoring distinct states of the process over time. However, the healthcare sector has seen limited studies employing MPM. This study aims to fill this gap by developing an MPM control chart tailored for healthcare data to promote early detection, confirmation, and patient safety. As it is important to detect unnatural conditions in healthcare processes at an early stage, the statistical control charts are combined with machine learning techniques (i.e., we deal with Intelligent Control Charting, ICC) to enhance detection ability. Through Monte Carlo simulations, our method demonstrates better performance compared to its statistical counterparts. To underline the practical application of the proposed ICC framework, real data from a two-stage thyroid cancer surgery is utilized. This real-world case serves as a compelling illustration of the effectiveness of the developed MPM control chart in a healthcare setting.

中文翻译：

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使用状态空间模型和基于机器学习的框架监控多阶段医疗保健流程

监控医疗流程（例如手术结果），重点关注早期阶段的变化和不自然状况，对于医疗保健专业人员和管理人员至关重要。为了实现这一目标，控制图作为统计过程监测领域最流行的工具，被广泛用于监测治疗过程。医疗保健流程通常具有多阶段结构的特点，其中多个组件、状态或阶段形成最终产品或结果。在这种复杂的场景中，多阶段过程监控 (MPM) 技术对于监控过程随时间的不同状态变得非常有价值。然而，医疗保健行业采用 MPM 的研究有限。本研究旨在通过开发针对医疗保健数据量身定制的 MPM 控制图来填补这一空白，以促进早期检测、确认和患者安全。由于在早期阶段检测医疗保健过程中的不自然状况非常重要，因此将统计控制图与机器学习技术（即我们处理智能控制图，ICC）相结合以增强检测能力。通过蒙特卡罗模拟，我们的方法比统计方法表现出更好的性能。为了强调所提出的 ICC 框架的实际应用，利用了两阶段甲状腺癌手术的真实数据。这个真实案例有力地说明了所开发的 MPM 控制图在医疗保健环境中的有效性。