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

** 解决的挑战 ** 在NHS内患者流入和流出过程的每一个阶段，都会出现延误，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得信息，或者信息不完整或过时，这反过来又导致稀缺资源分配不当由于没有工具或机制来预测入院水平或预期住院时间-当前领先的平台仅提供延迟的数据-医院和工作人员需要做出反应，关于床位、人员配备和资源的主观决定。虽然临床医生的经验将始终是重要的角色，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）利用，分析和支持决策和资源分配。解决方案 ** 我们建议开发首个高度准确的AI实时预测入院和患者住院时间（AUC\>0.9）。预计效益包括：1.预测资源需求（床，工作人员，设备等）的基础上，大的历史卫生保健。预测关键资源供应缺口，如PPE和氧气（COVID-19确定的故障点）;3.通过为临床医生提供准确的实时信息，加快患者出院速度。帮助更好地管理整个医院短期、中期和长期的床位占用状况和资源。**创新 ** 收集的基本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）大型数据集。这些临床算法将在数百万个数据点上进行训练。这个过程为临床医生和管理团队提供了准确、可操作的情报。例如，引擎将了解到，对于给定数量的NEWS评分较高或血氧饱和度较低的患者，将有一定比例的患者入院。随着COVID-19患者临床信息的积累，将产生越来越准确的入院和住院时间预测。这些信息将被实时转发给临床医生和医院领导，以便能够准确地订购/分配资源，计划出院评估，患者的时间不会超过需要。由于纯粹由于不及时的信息而导致的出院延迟每年仅花费NHS 625，942个床位/9260万英镑，该技术是及时且迫切需要的。

**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delays are attributable to hospitals and staff not having access to information or that information being incomplete or out-of-date, which in turn leads to the mis-allocation of scarce resources (staff, beds, materials).With no tools or mechanisms to predict admission level or the expected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.