Terry Walby, founder and CEO of Thoughtonomy, looks at how AI is transforming healthcare, exploring examples of how different organisations have been using automation
AI in the present, not the future
Robot-assisted surgery, preventive medical intervention (based on predictive diagnostics) and virtual nursing assistants are just some of the many applications where AI is predicted to transform delivery of healthcare over the next decade.
However, too much of the current narrative around AI remains ‘futuristic’, exploring the potential benefits of future R&D and innovation, rather than focusing on the real benefits that AI could and should be delivering to healthcare providers today. Without doubt, the prospect of AI being used within frontline clinical care is exciting and awe-inspiring, with companies like doc.ai (which uses AI to predict an individual’s health risks) working on some genuinely game-changing innovation.
However, for an industry that is suffering the consequences of monumental budgetary and staffing challenges on a daily basis, it’s simply not enough to be looking 10 years down the road; healthcare providers need solutions to these problems today.
The fact is that AI technology has already reached a level of maturity where it can deliver huge value to the healthcare industry and across social care and all public services. Indeed, AI is already being deployed in small pockets of healthcare provision all over the world to drive efficiencies across the operational and administrative side of healthcare systems. And where AI has been adopted, it is delivering game-changing improvements in terms of cost savings, staff capacity and productivity, and patient outcomes.
So the question is, why aren’t we seeing faster adoption of AI within the healthcare sector?
A pragmatic, scale-up approach to AI
The starting point for AI adoption with healthcare has to be in streamlining administrative workflows, speeding up and optimising processes which are often still manual, repetitive and paper-based, held back by legacy technology, fragmented branches of delivery and a lack of joined-up thinking. AI can solve these problems, acting as the integration layer between people and systems. Indeed, Accenture estimates that the US healthcare system alone could save up to $18 billion per year in this area by 2026.
In the UK, the Darzi Review of Health and Care called on healthcare bodies to ‘embrace full automation to release time to care’ as part of a 10 Point Plan to future-proof health and care services in the UK, with an estimated £6 billion in further savings attributed to automation in social care.
We’ve seen first-hand how Intelligent Automation can deliver substantial savings within the NHS, whilst freeing up critical talent to focus on more valuable tasks which make a difference to patients. For example, East Suffolk and North Essex NHS Foundation Trust (ESNEFT) has saved more than £2 million within a year simply by using automation to reduce the number of missed GP appointments and re-assign these appointments to patients who need them. And this is just in one hospital using Virtual Workers for one small process within its operations – if this was to be scaled nationally, the NHS could save almost £1 billion worth of lost appointments every year.
There are growing numbers of such examples, where automation is delivering game-changing benefits to front-line staff within the NHS, local government and policing. But we’re really only just starting to scratch the surface in terms of reaping the benefits of Intelligent Automation and overcoming the major resourcing and budgetary challenges facing public services organisations.
Rather than fixating on the big picture, healthcare providers need to embrace a new mindset when it comes to AI, focusing on where AI can deliver immediate results and prove its value now. A ‘start small and build-up’ approach to AI is far more manageable for healthcare organisations, enabling them to build a business case for deployment within a specific process or function, to get a feel for the technology, to learn throughout the implementation project and to be able to easily track and evaluate the outcomes. From there, operators can build momentum, picking off ‘the low hanging fruit’ to deliver rapid ROI, and re-deploying these AI ‘building blocks’ to scale across other parts of the organisation.
This approach to AI also ensures that healthcare providers can instil the necessary skills and culture within their workforces over time to pave the way for further AI deployment. Rather than seeing the introduction of AI as something that is threatening or unsettling, operators need to prove the benefits of AI to their existing staff, showing how it can take away the burden of mundane administrative tasks and free them up to spend their time on what they are best at – delivering first-rate patient care.
We cannot continue to think about AI as something that will transform public services in 10 years’ time, whilst the challenges facing providers today become ever more severe. We should forget about robots performing life-saving operations (exciting though that is) and instead concentrate on ensuring that our public organisations begin or accelerate their AI journeys in 2019.