There’s no denying COVID-19’s contribution to the current backlog in elective care, creating one of the biggest challenges in the history of the NHS. But the backlog was already growing before the pandemic, indicative of deeper, systemic issues in need of solutions.
With a new government on the horizon, now is the time to examine the root causes of the elective care backlog, find out where support is needed and look to advances in technology to help us improve practice and process.
The elective care backlog has reached critical levels with a waiting list of 7.57 million cases – many waiting months and even years.
But this issue is not just a number; it’s a growing list of real people waiting for essential surgery, affecting their quality of life and, in some cases, their prognosis.
Cases have soared since the pandemic but the issue started long before, with the number of people waiting for consultant-led elective care estimated to have grown by 95 per cent between January 2009, when waiting lists were at their lowest, and January 2020.
Systemic issues such as our ageing population and health inequalities as well as “vicious cycle” effects have long been ratcheting up the pressure on the elective care system.
“Vicious cycle” effects are the result of long waiting times.
Staff burnout that comes from dealing with more, and unhappier, patients causes mistakes, poor outcomes and high staff absence rates; decreased capacity then exacerbates the situation.
Patient acuity increases as smaller ailments become bigger problems through delayed treatment.
Denser cataracts and more debilitated hips require longer in theatres to deal with, meaning higher costs, and increased use of the private sector that often comes at a premium.
AI in pre-operative planning
These are complex problems and to alleviate the backlog in elective care, we need to examine how we lessen their impact.
There is no simple solution, but one of the most promising approaches lies in applying artificial intelligence (AI) to optimise the scheduling and delivery of elective procedures.
With all the hype surrounding AI, we need to ensure we don’t get either dazzled or daunted by this new technology, but take a strategic approach to how it can be utilised to achieve throughput gains.
Crucially, we need to look big picture. The key to tackling the backlog effectively begins weeks before patients are wheeled into the operating theatre.
Consultants have an incredible wealth of experience and expertise but even the best cannot out-predict AI models when it comes to estimating procedure times.
AI predictions are based on millions of historic procedures and simultaneously consider multiple variables such as the theatre being used, the surgeons’ and anaesthetists’ unique performance and behaviour and even free text entries in patient records.
This prediction improvement leads to two things: the avoidance of early and late finishes, and increased confidence in forecasting throughput.
With more certainty in forecasting, patients can be booked further in advance and expectations better managed.
Patients are happier knowing that their procedures are less likely to be cancelled or moved and staff go to work knowing what to expect.
With more accurate predictions of procedure times, AI models can then tackle the complex task of scheduling.
By factoring in the specific requirements of each procedure, including handover time, anaesthetic time, and urgency, schedules can be optimised for throughput.
Knowing every possible procedure on the waiting list that could fit into a slot, AI can “stack” these to achieve a statistical optimum – for example, a target session-fill that minimises the risk of early or late finishes.
Any complexities that are hard for humans to juggle, like limitations in procedures per day due to equipment constraints, can be handled by AI with ease.
Engaging with AI: our best hope
Given these potential benefits for resource savings and theatre productivity, why isn’t AI already in use on a wider scale and what are the challenges in implementing this type of model?
Well, investment, adoption and engagement of technology is another systemic issue within the NHS.
The NHS operates on many legacy systems. In addition to overcoming the technical challenges of integrating the organisation’s disparate technology, changing behaviours (especially those ingrained over many years) can be difficult.
People can be daunted by adapting to new ways of working and also worry about potential disruption to services.
Onboarding new solutions needs to take an empathetic approach to factor in these concerns, working with those on the ground rather than imposing from above.
Regardless of the efficacy of any solution, the positive experience of those undergoing the change is imperative in realising its value.
Data is another challenge. AI models can only learn effectively if they’re being fed high quality data.
Data recording practices vary from trust to trust, and the ‘free text’ nature of how procedure details are described can make structured analysis difficult, impacting on the accuracy of predictions.
These will be important factors to overcome in the months and years ahead, but they should not be seen as fundamental barriers.
Engaging with AI is not just an option; it’s a necessity for the future of all health services.
The tip of the iceberg
It’s feared that the backlog of care is far worse than it appears. According to the BMA the current 7.57 million elective cases are just the tip of the iceberg.
It refers to a growing ‘hidden backlog’ of patients who require care but have not yet presented to healthcare providers.
And it warns that the current elective care list doesn’t include waiting for non-consultant-led treatment, or patients waiting for follow up appointments once they have begun treatment.
To tackle this backlog the NHS will need to evolve, breaking down entrenched practices and behaviours to engage with new solutions or risk either collapse or radical change to its ‘free at the point of care’ service.
Technology providers need to understand the unique nature of this much loved institution, and focus on providing solutions that work for it and the people it supports.
AI stands at the forefront, offering a way to address the backlog effectively, improving patient outcomes and reducing the strain on healthcare systems.
The path forward will involve embracing its potential, investing in its development and uptake, and overcoming the challenges it presents.
As we look toward a future where healthcare systems are more resilient, efficient, and patient-centred, AI represents the biggest hope we have for tackling the elective backlog and beyond.
Real World Health is a data solutions provider and has been a partner to the NHS for more than 10 years.
The company’s Opfeed solution for theatre management is helping hospital trusts to deliver double-digit increases in theatre productivity with corresponding costs savings and reductions in waiting lists.
