Health systems experts worked with a Scottish health board and a software company to better predict the local critical care demands caused by the COVID-19 pandemic.
NHS Lanarkshire had been advised by central government to prepare for a ‘five-fold’ increase in demand for intensive care beds within its hospitals as the coronavirus peaked early in April.
But health systems experts at the University of Strathclyde Business school worked in partnership with the board and software modellers Simul8 to devise a model which showed they had ;already made sufficient updates to its capacity’ to cope with the surge.
This meant avoiding the costly adaptations to resourcing needs that would have otherwise been wasteful, as well as providing front line staff and capacity planners with piece of mind.
Dr Nicola Irvine, consultant physician, doctoral researcher and one of the team leads, said: “Once the executive team at Lanarkshire had set their key question – which was what will be your critical care need? And do we currently have the resource and the capability to meet that? – the fact that we were able to give them the answer within two weeks, and roughly seven to ten days before peak started, was vital in helping them manage this pandemic.”
The model has now been optimised to support the development of an Early Warning System for the next stage in the COVID-19 pandemic.
Chandrava Sinha from the Department of Management Science at the University of Strathclyde, who worked with Nicola Irvine and Gillian Anderson in building the simulation model, said: “A digital model is an approximate representation of any real-life system. They are basically mathematical or statistical models created using a computer which tries to best mimic and present a real life scenario or a proposed scenario, and to then answer various ‘what if’ questions to help decision-makers make a very well informed decision.”
A crucial element of the modelling process for NHS Lanarkshire was the use of data that the team were able to build into the simulation. To cut through any conflicting evidence and to make the model as accurate to local needs as possible the team drew on a range of data sets. This included very localised community data, such as population profiling, as well as national trends that were being received from central government. It also included wider international data from countries such as Italy and Spain where the pandemic wave was a few weeks ahead. This approach allowed Simul8 to create a model that was as accurate as possible to local needs.
Chandrava added: “This data all fed into the model and then gave us the maximum utilisation of beds across all different categories on a week-by-week basis for the whole first wave of the pandemic.”
Dr Irvine emphasised the need for a “triumvirate of executive expertise, clinical expertise and modelling expertise” in building and implementing a successful model such as this one.
The clinician understands the behaviours of the organisation at floor level; the modeller is able to interpret that nuanced dynamic environment and to simplify and abstract data into a model that can be usefully predictive; and an executive team will have the overview needed to set the most pertinent question, and then the authority to act on the predictions of the model.
“Validation is also a key part of any modelling process”, she said. “You want to make sure that you’ve captured the process that you are modelling, the environment, the disease, the activity etc. Crucial to this was the daily information that we were receiving from the hospital’s management team. We were able to constantly update our simulation using data from the local hospitals and authorities, as well as from wider resources such as the intensive care audit and information from the European Centre for Disease Control.”
In modelling for COVID-related planning, the research team realised that it was not just critical care that would be affected by the pandemic, but other areas of healthcare services would see knock on effects too.
“We were aware that other patients with emergency medical problems were presenting in smaller volumes”, said Dr Irvine, “but the turnaround time for testing the number of people who were presenting with suspected COVID – two days – was causing bottlenecks in the emergency department. This had potential to disrupt emergency care and other areas of urgent care, such in acute medical units.”
Further insights were also generated via the model in predicting that even while cases in the community were reducing, there were also some potential issues about infection being transmitted within the hospital that would need mitigating as well.
Dr Irvine added: “Simul8 modelling meant that we could say ‘here is the likely impact from COVID-19, but your other inpatient resources are predicted to be impacted too and you need to have a plan in place for this’.”