Using advanced AI and Natural Language Processing to accurately and automatically predict hospital length of stay, related patient-NHS resource requirements and improve discharge efficiency

使用先进的人工智能和自然语言处理准确自动预测住院时间、相关患者 NHS 资源需求并提高出院效率

• 批准号: 84911
• 金额: $ 50.42万
• 依托单位: ABTRACE LIMITED
• 依托单位国家: 英国
• 项目类别: Collaborative R&D
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=84911
• 关键词: Using; advanced; AI; Natural; Language

**Challenge to address**At every stage of the Patient inflow and outflow process within the NHS, there are delays which impact on patient care, staff, hospital resources and efficiency. The vast majority of delays are attributable to hospitals and staff not having access to information or that information being incomplete or out-of-date, which in turn leads to the mis-allocation of scarce resources (staff, beds, materials).With no tools or mechanisms to predict admission level or the expected length of stay - current leading platforms only provide time-delayed data - hospitals and staff need to make reactive, subjective decisions regarding beds, staffing and resources. Whilst clinician experience will always be important role, AI has the ability to harness, analyse and support decision-making and resource allocation in a quick, accurate and standardised way (analysing years of big data).**Solution**We are proposing to develop the first highly accurate AI real-time predictor of hospital admission and patient length of stay (AUC\>0.9). Projected benefits include:1. Predict resourcing requirements (bed, staff, equipment, etc) based upon large historical health datasets.2. Predict critical resource supply gaps such as PPE and oxygen (COVID-19 identified failure-point);3. Speed up the discharge of patients by providing accurate real-time information to clinicians.4. Help better manager whole-hospital bed occupancy status and resources in the short, medium and long term.**Innovation**The base information collected will be derived from local electronic health record datasets with the predictive core of the platform based on machine learning models that can accurately analyse both structured (numerical and categorical values) and unstructured (text-based information like triage and physicians' notes) large datasets. Such clinical algorithms will be trained on millions of data points. This process leads to accurate, actionable intelligence for clinicians and management teams to act on.For example, the engine will learn that for a given number of patients presenting with a high NEWS score or low oxygen saturations, a proportion will be admitted. With accumulation of clinical information on COVID-19 patients, increasingly accurate predictions for admission and length of stay will be generated. Such information will then be relayed to clinicians and hospital leads in real-time so that resources can be accurately ordered/allocated, discharge assessments planned and patient's aren't kept in any longer than needed.With delays to discharge purely from untimely information costing the NHS 625,942 bed-days/£92.6m per annum alone, the technology is timely and urgently needed.

**需要解决的挑战**在NHS内部患者流入和流出过程的每个阶段，都会出现延误，这会影响患者护理、工作人员、医院资源和效率。绝大多数延误是由于医院和工作人员无法获取信息，或者信息不完整或过时，进而导致稀缺资源(工作人员、床位、材料)的错误分配。由于没有工具或机制来预测入院水平或预期住院时间-当前的领先平台只提供延迟的数据-医院和工作人员需要就床位、人员和资源做出被动的主观决定。虽然临床医生的经验将永远是重要的角色，但人工智能有能力以快速、准确和标准化的方式(分析多年的大数据)来驾驭、分析和支持决策和资源分配。**解决方案**我们正在提议开发第一个高精度的人工智能入院和患者住院时间实时预测器(Auc\&gt；0.9)。预计的好处包括：1.根据大型历史健康数据集预测资源需求(床位、人员、设备等)。预测PPE和氧气等关键资源供应缺口(新冠肺炎确定的故障点)；3.为临床医生提供准确的实时信息，加快患者的出院速度。帮助更好地管理全院床位的短期、中期和长期占用状况和资源。**创新**收集的基础信息将来自本地电子健康记录数据集，平台的预测核心基于机器学习模型，可以准确分析结构化(数值和分类值)和非结构化(基于文本的信息，如分诊和医生笔记)大数据集。这样的临床算法将在数百万个数据点上进行训练。这一过程为临床医生和管理团队提供了准确、可操作的情报，以供他们采取行动。例如，引擎将了解到，对于给定数量的出现高新闻评分或低氧饱和度的患者，将接纳一定比例的患者。随着新冠肺炎患者临床信息的积累，对入院和住院时间的预测将越来越准确。然后，这些信息将被实时传递给临床医生和医院领导，以便准确地排序/分配资源，计划出院评估，患者的出院评估不会超过需要的时间。由于纯粹是由于不及时的信息需要延迟释放，仅NHS每年就要花费625,942个床日/GB 9260万，这项技术是及时和迫切需要的。