Title: Digitally adapted, hyper-local realtime bed forecasting to manage flow for NHS wards
Funder: Department of Health and Social Care

Abstract: For the proposed project, we request funding for 2 years covering one software engineer, and one post-doctoral research associate supported by the assembled project team including health care design and quality management support. We will take an existing functioning bed demand forecast, and make it fit for scaling across the NHS. Work is divided into the following packages: Governance, PPI and approvals and team onboarding Application foundation: building an application suitable for different levels of digital maturity; respecting interoperability standards; and providing a round trip for user interaction to improve forecasting Application integration: connecting the application to live data feeds and rapid prototyping and iteration cycles Upgrading the AI components of the model including adaptions to predict staff demand, consider COVID-19 adaptions, and explore transfer learning to allow deployment where training data is sparse Application user design: working closely with ward managers and clinicians through-out to ensure the application delivers actionable insights that lead operational efficiencies Model evaluation: both quantitative and qualitative evidence of safety, reliability and utility Quality management: ensuring the model and the application move toward CE marking A package of PPI interventions The application will deliver hyper-local demand forecasts for beds and eventually staff, and other resources. We forecast the counterfactual demand rather than the observed provision. The forecast is adapted and delivered to the hospital ward team since this is the tactical unit that can use the information to improve operational efficiency. Staff can be flexed, surgical timing adjusted, and case mix adjusted to make the most of the resource available. The assembled team brings together expertise in software engineering, mathematics, machine learning and health informatics. We are embedded in a busy teaching hospital. We will take every advantage to embed the learning we gather from our users in the application we build.

Lay abstract: More than 1000 patients have elective surgery cancelled every week in the NHS, causing anxiety for patients and families.(Wong 2017) Cancellations are upsetting, inefficient, may cause harm, delay treatment, and let slip therapeutic opportunities. Bed capacity is the primary reason for these cancellations. Yet bed capacity fluctuates. Operational efficiency depends on an accurate view of near-future demand, but this is challenging because a hospital is a complex system with many interdepartmental flows, and individual emergencies seem unpredictable. This challenge can be met by operational modelling combined with Artificial Intelligence. In 2012, we built a demand forecasting solution that supports operational teams to reschedule elective surgery and reallocate resources to avoid last minute cancellations and bed shortages. We deployed and customised this for a single ward: a cardio-thoracic critical care unit at Great Ormond Street Hospital.(Pagel 2017). However, that technology could not be scaled, nor readily adapted to work in different institutions. In 2018-19, in partnership, with INFORM (a translational data science team at University College Hospital), we generalised the underpinning mathematics of the solution and built a prototype that could scale. Our approach is distinct from most bed modelling approaches in that we deliver forecasts that are local (ward level), and near future (over the next week). Unlike most other bed forecasting models, we predict future *demand* not future bed *utilisation*. Bed utilisation is best thought of as *demand* already mitigated by *actual supply*. More simply, we are interested in bed demand *before* not *after* cancellations have occurred. This creates a window for local teams to better use their existing bed capacity by flexing staffing levels, or rescheduling surgical operating lists. More importantly, local teams are enabled to innovate: to find local solutions to predicted bottlenecks and to better use their existing bed capacity. We now seek support to 1. Upgrade the AI component of the model so that it can learn from a wider range of patient clinical characteristics (lab results, clinical history, vital signs etc.) 2. To extend the mathematics to include staffing and infection control constraints that must also influence bed availability. 3. To encapsulate the mathematical model in a software application that is resilient and 'connectable' to hospitals across the NHS ( interoperability ). Our application would enable both hospitals using predominantly paper notes (most nonetheless have an electronic patient booking system) and hospitals that are already fully paperless. 4. To test the application with clinical and operational teams so that it is reliable, easy and safe to use. High quality local bed forecasts have the potential to allow the NHS to run at higher capacity, safely and efficiently. This can reduce costs and waste, reduce short-notice cancellations, and ultimately reduce anxiety and suffering for our patients.

Research activity codes: 8.1 ORGANISATION AND DELIVERY OF SERVICES
Health categories: GENERIC HEALTH RELEVANCE