A bed forecasting app for the NHS

Woohoo (and thanks to the NIHR and NHS-X!). Super chuffed to have won support from NHS-X in the AI in Health and Care Award scheme.

Here’s the plain English summary from the grant:

More than 1000 patients have elective surgery cancelled every week in the NHS, causing anxiety for patients and families.(D 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.(C 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 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.