As Healthcare Providers struggled with the Covid pandemic the UK’s NHS was looking for a solution to help them model the impacts of the virus on planned care. Their aim was to re-engineer patient flows to best cope with the coming crisis. There were several challenges to overcome, not least that Covid was a novel virus, meaning there were no meaningful data sets to help predict what the impact would be. This is a general challenge in data science. When so many approaches to predictive analytics are dependent on reliable and clean data, where do you turn when that isn’t the case, and how do you make predictions with partial or poor data? In their article, Modelling the impact of covid on waiting lists for planned care, the NHS Strategy Unit summed this up as follows;
Pre-covid models will be of limited use. Their founding assumptions have melted. Some will therefore abandon any attempt to model - relying instead on instinct. But crisis management can’t be sustained. It will prove inefficient and error prone.
To overcome these challenges, the NHS turned to a different approach that modelled the underlying dynamics that produce data rather than trying to infer structure from data. The technique that they used can be thought of as a virtual digital twin of the processes that were important to the questions that they were trying to answer and that was populated with assumptions on how things would change over time. This freed the NHS to move on from understanding historical trends and to instead start evaluating how structural change would impact the organisation.
NHS care is a complex web of different patient flows as individuals move from one department to the next, typically starting at a GP and eventually moving into a hospital ward via an array of diagnostic departments. The capacity of each department determines how quickly a patient moves through the system. Changing any of these individual capacities has a knock-on effect in different parts of the healthcare system. In modelling the whole system, the NHS were able to provide managers with the tools to run their own experiments in a safe environment, ensuring they could learn from those experiments before implementing expensive process transformation in the real-world. By using Silico, the NHS could evaluate lots of futures and start to understand with some certainty where the sensitivities and bottlenecks might be. In their own words:
The key here is not to crave certainty. It does not stop everyone, but nobody can say with confidence how the next few months will pan out. We need approaches that understand which, of the many new unknowns, have the greatest potential to influence outcomes. We need approaches that can identify, prioritise - and then remedy – limiting factors in our knowledge.
As the work was carried out some time ago, we can now ask whether this approach worked and whether it produced valuable insights? It did. The simulated model predicted time delays before waiting lists grew due to the impact of limited GP appointments, but that once the floodgates were opened waiting lists would not only rise to unprecedented levels but would take much longer to return to pre-Covid levels than might be expected. This was precisely what we observed in late 2020.
The NHS also revealed that without the Silico approach they would have been ‘planning in the dark,’ Their Silico model highlighted where the important data gaps were, where the key sensitivities lay, and ultimately where further research and focus needed to happen. In short, the initial model created insights that would have otherwise remained hidden and has become the foundation for further projects and analysis around the NHS.
When deciding what technology to use to model waiting lists, the NHS turned to Silico. We are the go-to platform for building dynamic simulation models and creating Decision Intelligence.
From process re-engineering to financial optimisation Silico is in use across a range of sectors beyond Healthcare, including Banking, Insurance, Telecommunications, and Retail. As organisations increasingly embrace the complexity of their processes, Silico is the first Enterprise-ready toolkit for modelling and re-engineering those processes. To find out more about how your organisation can benefit from the Silico approach visit us at www.silicoai.com