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

用于支持 GOSH 患者流程的操作机器学习和机械建模

• 批准号: 2245620
• 金额: --
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2245620
• 关键词: Operational; machine; learning; mechanistic; modelling

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.

病人流描述了病人在医院中的移动，因为他们沿着沿着他们的护理路径过渡。良好的患者流量是患者安全，医院效率和患者体验的核心，因此在NHS中非常重要。患者流建模可用于了解患者流，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，数字研究环境（DRE），以及一个相关的数字研究和信息单元，驱动器。DRE从医院收集电子病历数据进行研究，为GOSH的患者流建模提供机会，为信托基金内的未来流程规划和策略提供信息。该项目旨在使用患者级数据，使用网络分析和聚类来识别整个信托基金范围内的患者流路径，应用统计和机器学习方法来预测患者流的标记，如住院时间，使用机械建模和深度学习技术构建特定部门的患者流模型，并开发医院患者流的预测模型。