Building the Foundations for Healthcare’s Digital Future

2027 is on the horizon and there’s plenty to be excited about when it comes to AI in healthcare. Below our Head of Healthcare, Mel Heath, is interested in what sits behind that ambition, turning her attention to some of the less glamorous – yet critical – questions around data, infrastructure, security, and risk. 

There is an understandable excitement around AI and healthcare is no exception with measurable gains already being made in areas like imaging. In almost every industry conversation I’m part of, AI comes up, with the questions being asked on how and where it could improve services, ease pressure, and ultimately make a meaningful difference to patients and the people caring for them. 

After more than twenty years working across technology and transformation, including major NHS and public sector partnerships, I’ve seen the difference technology can make, and I share in this excitement. Increasingly and collectively though, we must ask ourselves a slightly different question too: are we ready for it? 

When I joined techUK’s Health and Care Summit recently, much of the discussion focused on innovation, regulation, and the recommendations of the National Commission. I confess to being starstruck when Professor Alastair Denniston (Chair, National Commission into the Regulation of AI in Healthcare) thanked me for raising the point of readiness.  Readiness is something that we care about at Littlefish Group, and it continues to be a central theme in conversations with colleagues, customers, partners, and our Healthcare Advisory Board. 

It will not be surprising to industry colleagues, that for a conversation about one of the most exciting technologies of our time, some of the answers are remarkably familiar… 

AI readiness starts with the foundations 

When I talk about AI readiness, I mean giving an organisation the critical components it needs to use AI effectively, with a clear understanding of where the risks lie. That covers technology and architecture, but it also encompasses data, governance, cyber security and, ultimately, people. It isn’t one project with a neat beginning and end. It’s a collection of connected ‘stuff’ that all influence whether AI can be introduced safely and used to deliver the most benefit.   

I was reassured to hear Jason Bonander, Chief Digital and Technology Officer at MHRA, touch on these thoughts when talking at TechUK about the organisation’s direction towards 2030. This included resilient infrastructure, cyber security, architecture, interoperability, information management, and data governance. 

True, some of those foundations feel less glamorous (Jason’s language) than AI itself – and they aren’t necessarily going to generate the same excitement as the latest use case will! However, they have a very direct bearing on how effectively and confidently an organisation can adopt AI, not just implement it.  

Of course, keeping those foundations in good shape isn’t always straightforward. Healthcare organisations are complex and incredibly busy operations, with competing priorities. Technology continues to move quickly too, while programmes are getting larger and expectations are growing. All of that has to be managed with finite time, people, capacity, and budget!  

Much of the focus, quite rightly, must remain on what’s happening today: keeping services running, solving immediate problems, and supporting staff and patients. Balance in everything and readiness can’t mean fixing everything at once. 

We are best to start with understanding where you are today, what’s working well, where the anxiety or excitement is, and then where our attention will make the greatest difference. 

Where to start? 

Another point from Jason Bonander’s talk that resonated with me is his challenge to the technology industry to bring thoughtful problems and creative solutions, rather than simply bringing products.  

As I don’t work for a product company that’s not a difficult ask, our focus as a service provider is exploring the current situation and future ambition so the service we deliver helps the organisation achieve that vision.  

At the core of our approach in health is understanding from our clients ‘what problem are you trying to solve?’ or ‘what objective are you trying to achieve?’.  From here we have a clear understanding of purpose and whether the right foundations are in place to deliver service safely and effectively. 

Using this method, success becomes much easier to define. It might mean giving time back to clinical teams, making information easier to find, removing friction from an everyday process, or improving the experience for patients. The measures will vary depending on the use case, but they should always bring us back to the outcome we set out to achieve. 

How well do you know your data? 

Data is one of the clearest examples of why readiness is critical.  

Before AI can be used confidently, organisations need a good understanding of the data they hold, where it lives, who can access it, and how sensitive information is classified. These are familiar information-management disciplines, but AI brings another dimension. 

The answer cannot be a one-off tidy-up. If an organisation improves the data it already holds while continuing to create poorly classified information, the problem is already manifesting itself for the future. Good readiness should therefore consider the controls and knowledge that help organisations maintain that management for all time.  

This becomes especially important as teams find new ways to use AI, automation, and low-code tools to solve problems in their own areas. Innovation can come from anywhere, which is hugely positive, but leaders need visibility of what is being created, what value it provides, and how it will be supported. A useful solution can quickly become an operational dependency. If the person who understands it moves role or leaves, somebody still needs to know how it works and how it’s maintained.  

Keep people and patients at the centre 

Technology can only take us so far without bringing people with us. Our Healthcare Advisory Board member, Sarah Wilkins, a former NHS CDIO, summed this up well when she talked about the pressure to deliver new technology at pace. If we want people to use new tools in ways that genuinely improve workflows and services, we need to understand the barriers to adoption early. 

That means helping staff understand where AI is being approached, how it should be used, and, importantly, the difference it’s there to make. Trust is key.  

People need confidence in the tools they’re being asked to adopt, or they simply won’t use it.  I experienced this first hand when attending a consultant appointment at a Trust that has just rolled out AVT. On asking if there are new declarations that must now be made, my consultant explained she was only using the new technology to dictate notes after the patient leaves! This of course simply defeats the object. The workforce must be helped to understand ‘the why’ behind the investment in AI, just as patients need to know that their information is being handled responsibly. 

This feels particularly important in healthcare, where highly skilled people are making complex decisions every day. Giving the clinical workforce better information and better tools, while removing the friction involved is truly exciting.  Everybody wins if we give clinicians greater capacity to focus their time and expertise where they can make the greatest difference. 

I think about this as someone working in technology, and as a patient. I want these technologies to work well, I want the experience to make sense and, fundamentally, I want to know that my information is safe. Those are very human expectations, and they should stay at the heart of how we think about AI readiness in healthcare. 

Getting ready to get AI right 

AI is already here, and people across healthcare are already exploring and using it in different ways. It’s important we understand that readiness doesn’t mean waiting until everything is perfect before going any further. Nor can we ignore it just because the genie is out of the bottle.  

The way I see it, readiness simply means understanding where your starting point is and being clear about where attention is needed most. This includes having visibility of your data, governance, and cyber risks, as well as understanding your estate’s digital footprint, and making sure people are equipped to use new tools effectively. Crucially, it also means keeping sight of the problem you set out to solve in the first place. 

There is huge potential for AI in healthcare including lots of small and simple gains to be made. As we head towards 2027, I’m convinced that getting the fundamentals right will help us use it well, so its value is felt by the people providing care and ultimately the people depending on it. 

 

Every healthcare organisation will be starting from a different place. If you’re exploring AI, looking more closely at your data, or simply trying to understand where your readiness gaps might be, the Littlefish Group Healthcare team would love to hear what’s on your mind.