When we talk about ‘data’ in healthcare, what we’re really talking about is people’s health, their story, and their care journey. Clinicians, doctors, nurses all need to know the patient’s ongoing conditions, what was said at their last consultation, what medications they’re taking, and a whole host of other considerations to make sure they provide the best care possible.
And crucially, they need all this information fast. A recent survey from Pulse found that the average UK GP sees 31 patients a day — that’s 31 people with vastly different histories that will require different approaches to care.
What caregivers don’t need, is to have to search through every single letter to find that piece of crucial information. And that’s where AI can step in.
Making data AI ready
The first step in implementing AI into any organisation is ensuring that the data being fed into the models is of good quality. One thing that the medical sector is certainly not lacking is data. The NHS alone has the medical records of around 69 million people, but whether this data is actually usable in the traditional system, is a whole other matter.
Much of healthcare’s most valuable insight still lives in unstructured content, the thousands of letters, notes, and reports that capture a patient journey. Without the right content infrastructure, that knowledge remains inconsistently formatted and scattered across multiple different systems. This makes it hard to analyse, and even harder to use in the moment of care.
Content management, therefore, is critical. Fortunately, recent advances in the space have taken much of the legwork out of the process. There are tools now that use data curation techniques to structure and normalise unstructured content, converting it into a usable format
For example, automating the extraction and classification of unstructured documents (e.g. scanned documents and forms) using AI in accounts payable, human resources and medical records is already dramatically saving time and speeding processes across healthcare organisations. What’s more, they can continuously update and integrate data across different systems, including Electronic Health Records, acting as a living record of activity across the organisation.
So, once the data is in a state where it can be leveraged by AI, how does this translate into helping medical professionals provide better care?
AI in practice
AI, in particular the rise of agentic AI, is already reshaping how hospitals operate behind the scenes. To illustrate this point, picture a standard visit to the GP — an interaction that happens thousands of times a day, but now enhanced with AI.
The patient checks in at the desk and an AI agent immediately gets to work combing through their medical records. The agent identifies the key information and provides a summary to the attending doctor. During the consultation, AI transcribes the meeting and automatically sends the notes to the GP and the patient, highlighting the main points and next steps. After the GP has made their diagnosis, they recommend a follow up appointment in two weeks — a separate AI agent automatically sends a reminder to the patient’s calendar.
It’s not flashy, but it is truly transformative. This streamlined interaction not only means that doctors can actively spend more time with patients but improve patient care outcomes. And that’s ultimately what all of this innovation is about, freeing up doctors from the administrative strain, and empowering them to give more efficient, more accurate, and more human care to those in need.
Importantly, this is just the start. As AI continues to evolve, the use cases become increasingly impactful — things like a single patient record across entire countries as clinical teams currently waste valuable time navigating multiple systems leading to delayed decision-making and potential care gaps. This leads to improved care outcomes by ensuring all team members have access to consistent, up-to-date information — something that has moved from pipedream to possibility with AI. Organisations have already been collecting the data and building the foundations to make this possible; they simply need the right infrastructure to unlock the potential.
Stop hoarding, start healing
The true outcome of AI innovation isn’t technology for its own sake, it is creating the time, insight, and headspace for caregivers to do what only humans can: provide compassionate, and effective care.
Put simply, it’s a matter of when, not if, for implementing AI. And when it’s already making such an impact, the medical sector needs to start at the very least laying the foundations for innovation. Hospitals and clinics are only going to continue building up more and more data, so why not use it to make a tangible improvement to healthcare?
As I said at the beginning, data is essential to informed decision making in the healthcare sector. But it’s also the lifeblood of AI, and it’s time for healthcare organisations to start leveraging it for the betterment of their patients.
By Mike Campbell, Chief Product Officer, Hyland
