Covid-19 deaths and infection rates continue to drop in most parts of the world, yet this is far from the end of the pandemic.
The World Health Organization has said the virus is not under control and infections are accelerating. We are now embarking on a new phase of planning and modelling that will determine how our health systems need to work in the future.
Amid fears that the UK is heading for a second peak and the need forpreparedness, NHS health systems are working hard - both to provide safe care to tackle ever-increasing waiting lists for postponed elective surgery and to ensure sufficient capacity to deal with a future rise in Covid-19 cases. The full impact of postponing elective surgery is yet to be discovered, but the British Medical Association has warned patients are being neglected and illnesses are worsening.
An unknown future means the need for a new way of ensuring capacity
Capacity has been a thorny issue for the NHS for decades. Many trusts have been running at near full capacity for years (92-95 per cent) and have become very good at managing this by looking in the ‘rear view mirror’ to benchmark themselves against peers.
This approach works well because it means you can look backwards to predict what is likely to happen in the future and is particularly helpful when you consider future predictions about health service demand. For example, all NHS Trusts are likely to have believed in January that they had the correct number of critical care beds for their needs.
However, running at such a high capacity means there is little flex in the system when a pandemic hits. Resources can be switched but there is no room for manoeuvre in terms of overall capacity.
The ability to expand and contract capacity is vital and going forwards we need to be in a better position to do this as it is inevitable that another pandemic or other disruption will be just around the corner.
Pandemic data can help to establish a nationwide baseline of activity
To enable better understanding of what happened within the health system, it is necessary to analyse the data captured by NHS organisations from the early months of the pandemic. The Health Episodes Statistics (HES) data for the pandemic period will soon be made available from the NHS and other healthcare organisations, and will enable a nationwide baseline across all areas of activity, giving a better understanding of the capacity needed to expand and contract.
The chart below is based on data from 29 CHKS clients across England, Wales and Northern Ireland to end April 2020. It shows average numbers of episodes of care split into broad types, per calendar day in a month – with normal seasonal variations in 2019, but a general consistent spilt and total volume.
The chart is based on data from 29 CHKS clients (across, England, Wales and Northern Ireland) to end of April 2020.
The chart is based on data from 29 CHKS clients (across, England, Wales and Northern Ireland) to end of April 2020.
Looking at March and April 2020, we see the impact of hospitals reconfiguring to free capacity to deal with the expected volume of pandemic related cases, with clear reductions in volumes for elective and day case activity and a smaller reduction in non-elective activity.
As a result, there are predictions of waiting lists of up to 10 million patients by the winter, so NHS trusts will need to increase capacity to deal with postponed activity while also ensuring that they’ll be able to deal with any rise in Covid-19 cases.
Predictive benchmarking can provide a system-wide picture of impact on services
The concept of benchmarking can potentially be extended to analyse the data in order to predict both any increase in activity and the potential free capacity across shared pools of resources.
It’s not an exact science, but if done well can help with making informed decisions based on how resilient (or fragile) current services are - and how a particular NHS trust compares to their peers to get a nationwide picture.
There is also the possibility that the use of such information could help to determine how far we are able to use new purpose-built facilities like the Nightingale hospitals and how many are kept on standby in case of further, widespread outbreaks.
Having a better understanding of the impact of the pandemic on the rest of the system, for instance how much activity has been created due to people not attending A&E, or as a result of postponed treatment, can help us understand how increasing capacity in certain areas can reduce future demand. You could call this a national benchmarking capacity modelling framework and it would need to be sophisticated enough to link data across primary care, acute care and community services.
One area that has already been brought to our attention is cancer treatment. By identifying the impact on trusts of postponing cancer treatment and diagnosis, it may be possible to focus on where capacity needs to be increased and how resources could be shared. Through expert analysis of local data we could start to see the sharing of capacity between sites, creating a localised system-wide bank of resources, both in terms of staff and physical capacity.
In such unprecedented times, this approach to capacity modelling is more important than ever. It’s not beyond us to start to use data in this way and the fact that we were operating a healthcare system within such tight capacity margins suggests that we would be able to understand exactly what is needed to flex the system the next time we need to do this.
Stuart Bailey
Product Director, Capita Healthcare Decisions
Stuart is a professional in IT product, solutions architecture, design and development with over twenty-five years’ experience, including work on major projects and pre-sales with both government and private bodies. He is a product director specialising in the healthcare Sector. His latest expertise has been developed over the past three years at Capita Healthcare Decisions where he was previously a solution director responsible for overall ownership and development of products for large scale or complex opportunities.
