November 2022 • PharmaTimes Magazine • 18-19
// AI //
Cold comfort?
Decision intelligence can help to ease winter pressures on the NHS
The long tail effects of the pandemic have hit healthcare services hard and winter is set to compound matters further. Huge backlogs in elective care – with over 6.8m now waiting for treatment – combined with increases in flu admissions, COVID-19 surges, other respiratory infections and patients requiring immediate care – have created a ‘perfect storm’.
Never before have medical professionals had a greater need for new tools and technologies to help.
A connected, whole pathway view from primary care to community care and discharge is required – and this is where AI can help. It can help drive operational performance, potentially allowing staff to prioritise care and ensure the right patients are accessing the right care at the right time, despite the intense pressures.
Landscape change
Winter pressures are nothing new, but this year they come against intense efforts to tackle huge treatment backlogs that grew during the pandemic. The Institute for Public Policy Research (IPRR) predicts the backlog could take over a decade to clear and estimate there have been around 20,000 missed cancer diagnoses.
The backlog means capacity challenges within hospitals are already acute, and will likely worsen as increased infections drive higher bed demand throughout winter. So, what can be done? The IPPR highlighted three investments – a larger workforce, more diagnostic equipment and more physical space to provide care. The limiting factor, however, is investment, with at least £8 billion needed to tackle waiting lists.
Regardless of investment, new ways to help the NHS optimise resources are needed, and use technology to do more within the constraints it faces – whether they be staffing, equipment or buildings.
Currently, AI is most commonly known within diagnostics or to augment clinical decision-making. It also has a huge impact on optimising patient services, however, and therefore needs rolling out further to support operational decision-making and help teams handle winter pressures. By doing so, the NHS could realise the following benefits:
1. Improving access to care
On the frontline, operational AI means using local and national data to accurately predict where equipment and resources will be needed. At the height of the pandemic, AI helped forecast patient demand for ventilators and other equipment through using real-time data.
The same technology was also used in the allocation of beds, using thousands of 111 calls and other data to accurately predict how the pandemic was spreading, and how this would impact hospital admissions up to three weeks in advance.
Beyond the pandemic, AI still plays a pivotal role in optimising resources. Where availability appears, AI can predict who needs treatment first, as well as where and when. It can even look at the likelihood of treatments going ahead, and identify those at risk of not attending and tailor communication strategies to improve access.
‘At the height of the pandemic, AI helped forecast patient demand for ventilators and other equipment through using real-time data’
2. Preventing harm for those waiting for care
Operational AI can also help in prioritising patients needing care most and minimise unnecessary harm. For example, AI can help prioritise waiting lists both for those needing urgent treatment and those with a longer management plan to continually monitor a patient’s condition and risk score, helping determine changes in treatment.
Similar applications are powerful in remote patient monitoring and virtual wards when used to create early warning systems and prioritise intervention – be that accelerating access to care, or incentivising self-administration of care.
3. ‘Right-sizing’ clinical services
Using AI-driven demand forecasting and scenario planning, healthcare services can better predict and supply ‘right-sized’ services. Decision-makers can also use AI for scenario planning, helping to optimise resources in critical areas such as real estate and staffing.
4. Evaluating the impact of new models of care
In the face of continually changing pathways (e.g. remote monitoring and virtual clinics) AI can play a crucial role in the on-going monitoring and evaluation of models of care. Sophisticated AI techniques can be used to provide near-real-time understanding of outcomes with a view to supporting timely onward decision-making – challenging the long-standing audit approach.
5. Having embedded decision intelligence
Developing this whole pathway view takes time, yet NHS staff need immediate solutions to help them handle winter pressures. Early access to AI tools in advance of the winter peak is an important step to embedding machine learning within the delivery team’s operational decision-making.
6. Maintaining efficiency
Operational planning in healthcare is a game of chess. What’s important is not just the next move, but confidently predicting the next four or five. It is especially crucial now to look ahead as we move into an intense winter period. That is where operational AI comes in – by ensuring the next moves are calculated with greater precision, and taking in a range of complex factors a human cannot digest alone.
The NHS is a complicated supply chain that can become more efficient. It needs to be as agile as possible, like ‘just-in-time’ production where manufacturers make exactly the right amount at exactly the time customers need it. This agility bolsters resilience, and will help the NHS adapt as winter pressures place operational teams under intense strain.
While staff are extremely busy and are set to become even busier, none should be waiting around in under-utilised screening facilities while elsewhere patients wait for procedures. The beauty of a national health service is in the potential to optimise resources right across the country, despite peaks and troughs in demand.
Zillah Anderson is a Director at Faculty’s Health & Life Sciences Practice. Go to faculty.ai
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