By: Orlando Agrippa, CEO and Founder of RwHealth
Over the past 18 months, mental health issues have permeated public consciousness like never before. The Covid-19 pandemic has forced the public to contend with a range of exacerbated pressures – isolation, fear, bereavement and the pressures of establishing a ‘new normal’.
Indeed, while the pandemic put a much-needed spotlight on mental health, it’s also revealed that more needs to be done to strengthen the UK’s support services.
The mental health sector entered the pandemic on the backfoot. Our systems were already ill-equipped to handle rising mental health diagnoses; our inability to cope with the additional influx of demand caused by Covid-19 was due to inadequate foundations – most notably, a 10% deficit in the number of mental health nurses and practitioners.
What’s more, in the face of overwhelming demand this lack of capacity has been burdensome for the few mental health staff available to treat patients. Recent research has highlighted that nearly 9 in 10 (87%) mental health nurses have suffered stress as a result of their hefty workloads.
Using data to mitigate the crisis
Our mental health services are currently accelerating towards crisis point. The Future NHS survey indicates there will be a surge in Primary Care mental health referrals across England until at least March 2023.
It will be vital for providers to take the reins themselves and make optimal use of their existing resources. To establish more effective support strategies for patients and practitioners, the NHS and its partners must gather, understand and harness the data in their systems in a more holistic way.
This is where emerging technologies such as Artificial Intelligence (AI) can really help. AI algorithms – which self-learn without human intervention – can provide vital patient insights at both a real-time and predictive level, helping to ascertain what type of support is likely to be needed, and when. On an organisation-wide level, harnessing AI means that frontline staff can gain a full understanding of their current workflow and the developing situation within their local area – allowing them to better prepare for the challenges ahead.
There are already many examples of AI being used elsewhere within public and private healthcare to great effect. For example, hospitals such as XX and YY, which are using data-driven solutions and strategies, have been able to manage patient volume and bed capacity better than those that aren’t. [Can RWH provide an example here please?]
As well as helping to proactively plan the resources needed to support incoming patient requests, using AI to harness large-scale datasets also has the potential to lead to quicker diagnosis and better intervention. AI algorithms are able to recognise patterns in large volumes of information and identify complex features and characteristics that can’t be processed by the human brain – such as neurological patterns which, if left unchecked, may lead to serious health issues. Looking at data this way means that diagnosis is quicker and treatment plans are easier to define. Crucially, it means that preventative measures can be determined to help patients before they reach crisis point. It perhaps goes without saying that prevention is better than cure, but this adage holds particularly true in the world of mental health where AI can play a vital role.
It may be a long road ahead for mental health services, and it’s likely that the NHS will struggle under the sheer weight of demand both now and in the future. But using AI to obtain insights from massive amounts of existing health data has the potential to rapidly improve the capacity situation. Unlocking this data for clinical insights will allow healthcare providers to offer more personalised and preventive care, allowing them to approach mental illnesses in a more targeted way.