Operational machine learning and mechanistic modelling for supporting patient flow at GOSH

病人流动描述了病人在医院里的移动，因为他们沿着他们的护理路径过渡。良好的病人流动对病人安全、医院效率和病人体验至关重要，因此在NHS中非常重要。患者流模型可用于了解患者流，并帮助提高需求和容量规划、预约安排和患者路径优化等领域的效率。大奥蒙德街医院（GOSH）最近开发了一个基于云的数据和分析平台，即数字研究环境（DRE），以及一个相关的数字研究和信息学单位DRIVE。DRE从医院收集电子病历数据进行研究，为GOSH的病人流程建模提供了机会，为医院未来的流程规划和策略提供信息。利用患者层面的数据，该项目旨在通过网络分析和聚类来确定全信任范围的患者流动路径，应用统计和机器学习方法来预测患者流动的标记，如住院时间，使用机制建模和深度学习技术建立特定科室的患者流动模型，并开发整个医院的患者流动预测模型。

Patient flow describes the movement of patients through a hospital as they transition along their care pathway. Good patient flow is central to patient safety, hospital efficiency and patient experience, and is therefore of great importance in the NHS. Patient flow modelling can be used to understand patient flow and help improve efficiency in areas such as demand and capacity planning, appointment scheduling and patient pathway optimisation. Great Ormond Street Hospital (GOSH) has recently developed a cloud-based data and analytics platform, the digital research environment (DRE), and an associated digital research and informatics unit, DRIVE. The DRE collects electronic patient records data from the hospital for research, presenting the opportunity to model patient flow at GOSH to inform future flow planning and strategy within the trust.Using patient-level data, this project aims to identify trust-wide patient flow pathways using network analysis and clustering, apply statistical and machine learning methods to predict markers of patient flow such as length of stay, build department-specific patient flow models using mechanistic modelling and deep learning techniques, and develop a predictive model of patient flow across the hospital.