A crystal ball for healthcare?

Frances Sneddon, chief technology officer at Simul8, reveals how digital simulation could navigate a path out of COVID-19

Digital simulation is a computer system that tests various scenarios in a real-life model, helping to underpin operational decisions

Predicting the future is a fraught business – but it is critical for effective planning and resource allocation, especially in the healthcare sector.

And never was this more significant than in the early days of the COVID-19 pandemic.

In these extreme circumstances, digital simulation proved an invaluable tool in helping healthcare providers deploy available resources in the most-effective way possible

As focus turned immediately to managing the rising number of severe coronavirus inpatients as a priority, quick decisions were needed to aid capacity planning, ensuring there would be sufficient beds, ventilators and staff on shift to cover a variety of scenarios.

It was in these extreme circumstances that digital simulation proved an invaluable tool in helping healthcare providers deploy available resources in the most-effective way possible.

Through simulation software, ICU managers were able to quickly evaluate and prepare for surges in capacity using reliable evidence produced by the digital simulations, helping to evade the threat of health services being overrun.

And while the pandemic outlook may be less bleak than it was in early spring, albeit ongoing; a new challenge is emerging that will require equally agile planning.

The fallout of the pandemic has revealed an even-greater backlog of service requirements than was faced pre-coronavirus.

So how can the use of digital simulation, as was seen in ICU planning, be rolled out across wider healthcare services, and what kind of new contingencies will it need to incorporate?

Exacerbated waiting lists

Of course, capacity planning in healthcare has always been a challenge, but this is now only exacerbated with an increased backlog of patient waiting lists.

The resilience and strength of healthcare workers has really shone through during the pandemic, and this same level of commitment will now be focused on the rebalancing of service provision to combat the growing waiting list

In fact, NHS waiting lists could double by the end of the year, with 10 million people waiting for potentially-life-saving treatment.

This was the stark warning of the NHS Confederation, thanks to healthcare services operating at a reduced capacity because of measures designed to reduce the spread of COVID-19.

The resilience and strength of healthcare workers has really shone through during the pandemic, and this same level of commitment will now be focused on the rebalancing of service provision to combat the growing waiting list.

The backdrop of uncertainty, however, makes this particularly challenging, as so many variables and contingencies now enter the equation.

Contingencies begin to multiply

How have priorities changed over the last few months? Which waiting patients require more-immediate services? Which waiting patients are now more vulnerable? How will services need to be adapted to maintain social distancing and safe practices? What happens if we allocate a certain proportion of clinicians to a particular task over another, less-pressing task?

And what if more beds are allocated to one speciality over another? What if community beds are blocked, meaning patients have to stay in hospital for longer? What happens when winter brings a new surge in demand? Or what happens if a particular illness turns out to affect more patients this year than normal?

Will there be a second spike in coronavirus cases? If so, when will it happen and how great will it be?

Frances Sneddon, chief technology officer at Simul8

How can health services be prepared to, once again, switch to emergency pandemic provision?

And how will staffing levels be impacted, either through the need for redeployment or from staff needing to take their own sick leave or a period of isolation?

In addition, how have patient pathways been affected through the wider healthcare system? What is the impact on co-ordination with wider healthcare services, from hospitals to GP surgeries, social care and ambulance services?

What happens in the event of a vaccine being developed, and what if that does not happen?

The starting point for effective decision-making in the healthcare sector has to be data driven. This, after all, is the essence of evidence-based medicine – a core principle underpinning the NHS’s operation

While COVID-19 persists, how much deeper can the backlog of patient waiting lists grow, and how often do priorities need to be reviewed?

With all these questions raising a multitude of potential outcomes, capacity planning in this current climate becomes significantly more problematic.

It’s with this level of complexity, however, that digital modelling comes into its own.

Simulation as a decision-making tool

Clearly, the starting point for effective decision-making in the healthcare sector has to be data driven. This, after all, is the essence of evidence-based medicine – a core principle underpinning the NHS’s operations.

However, when dealing with data on this scale – on everything from staff volumes, qualifications and availability, to physical premises and bed numbers, and from supply chains to procurement – the analysis will quickly push a spreadsheet model to breaking point.

And, when the data is liable to constant changes and shifts in patterns, making decisions based on averages becomes equally problematic.

Instead, digital simulation can take away the headache by offering a means of testing multiple different possible outcomes quickly, cost-effectively and, crucially, without risk.

How does it work?

A simulation is a computer model that looks and acts just like real-life processes.

As organisations across the NHS work to ready themselves for multiple different possibilities – as well as their ordinary, and enormous, workload – digital simulation promises something of a crystal ball

By testing a change in a virtual environment, risk is removed and the results will provide operational evidence to underpin decisions.

Importantly, simulation builds in real-life randomness across multiple conditions (e.g. an unexpected surge in demand, a keyworker being taken sick, or individual patients whose conditions experience a rapid escalation) so that the model reflects the performance of the actual process.

Building this real-world variation into the simulation ensures the model behaves in the same way as it would in real life, which is extremely difficult to replicate without digital support.

When decisions are made and then implemented, such as redeployment of staff to assist in a new spike in COVID-19 cases; the simulation will continue to monitor performance and can provide ongoing insight into the impact on other parts of the system.

With this agility, changes can be continually reviewed and updated based on reliable and quickly-attained data.

The applications for such modelling in healthcare can include managing variations in the flow of patients through the system’ managing the deployment of resources, whether staff, beds or other equipment’ shifting to new priority areas’ or managing patient pathways through one part of the system and into the next to avoid disruptions from bottlenecks.

Reconfiguring services becomes a more-efficient and fluid process.

The COVID-19 pandemic will no doubt impact on health services for the foreseeable future.

But, as organisations across the NHS work to ready themselves for multiple different possibilities – as well as their ordinary, and enormous, workload – digital simulation promises something of a crystal ball.

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