In March 2020, the University of Strathclyde Business School health systems experts used Simul8 simulation software, in collaboration with NHS Lanarkshire, to predict critical care needs at the start of the pandemic. In a paper recently published by the Royal College of Physician’s Future Healthcare Journal, it says this case study is unique in healthcare literature and serves as an example of successful methodology for similar crises. Let’s take a closer look.
Initial advice from central government had suggested NHS Lanarkshire prepare for a worst-case scenario of a five-fold increase in demand for critical care at the peak of the crisis. However, by using discrete pandemic event simulation (DES) modelling to accurately replicate the critical care unit, the trust was able to make a much more informed decision. In fact, the model highlighted that it had already made sufficient updates to its capacity to be able to manage the projected surge in critical care needs brought on by the pandemic.
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 peace of mind.
“Once the executive team had set their key questions – 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 the peak started, was vital in helping them manage this pandemic,” says Dr Nicola Irvine, consultant physician, doctoral researcher and one of the team leads in the collaboration.
Creating the model
Simul8’s sophisticated simulation software was used to create the model for this new pandemic planning approach. “As the name suggests,” said 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, “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 pandemic modelling process was the use of data that the team was 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 made sure that the model was as accurate as possible to local needs.
Chandrava added: “All of this data fed into the simulation 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.”
Validating the model
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,” continued Dr Irvine. “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 continually update our simulation using data from the local hospitals and authorities, including inpatient activity and weekly attendances to the emergency department and community hubs, as well as from wider resources such as the intensive care audit and information from the European Centre for Disease Control.”
Identifying the wider impact
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 that 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 as 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.”
Wider adoption of simulation
The University of Strathclyde research team is led by Professor Robert Van Der Meer and includes Dr Nicola Irvine, Gillian Anderson, Chandrava Sinha and Holly McCabe as healthcare modelling specialists. The success of the Simul8 simulation in assisting NHS Lanarkshire at the beginning of the pandemic meant that Holly and Gillian then used the model to support the development of an Early Warning System for the next stage in the COVID-19 pandemic.
Prof van der Meer said: “The Strathclyde model really demonstrates the value of simulation for critical decision making. The approach provides evidence for those factors that are unknown and does so by generating an extremely localised picture of the situation. It’s from here that you can make confident decisions where the risk has been mitigated significantly.
As for wider applications, Dr Irvine is now a strong advocate for the use of digital simulation not just in critical care but throughout health services. “To be honest, I struggle to think of any applications in healthcare where simulation modelling wouldn’t be useful,” she said.
“As a clinician, these models allow you to create a virtual, experimental laboratory where you can see the patient, staffing and efficiency outcomes when testing different systems. To deliver this as a real-life trial would be cumbersome and it would take a long time, which would receive a lot of opposition. To instead be able to deliver the trial in a virtual environment and get a very clear picture of the outcomes without the associated risk or costs makes it a lot easier to achieve buy-in, and this makes digital simulation truly invaluable.”
By Tom Stephenson, Head of Simulation Excellence – Healthcare