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

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are attributable to hospitals and staff not having access to information or that information being incomplete or out-of-date, which in turn leads to the mis-allocation of scarce resources (staff, beds, materials).With no tools or mechanisms to predict admission level or the expected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