The potential for AI to drive significant change in healthcare is widely acknowledged, but most of the discussion so far has been on clinical advances in areas like radiology, imaging, and drug discovery that promise transformative change.
While the potential of these future innovations is hugely exciting, AI is not just about the big-ticket items, far from it. In fact, every single process and every single person in healthcare - from hospital managers to patients – could stand to benefit from it.
The broader, operational value AI offers today comes from embedding high-quality data and intelligent solutions into processes that eliminate everyday inefficiencies, or bring quickly-achievable day-to-day improvements in patient care.
While the potential of these future innovations is hugely exciting, AI is not just about the big-ticket items, far from it
The healthcare sector is in urgent need of these kinds of efficiencies. According to 2025 figures from Statista, the number of hospital admissions to NHS hospitals in England reached 17.6 million in 2023/24, showing that admission numbers have exceeded pre-pandemic levels.
The expectation is that pressure on the health service will continue to ramp up, with an ageing population and a recent Health Foundation briefing confirming that England is on course for 9.3 million people living with major illness by 2040, 2.6 million more than in 2019.
This extra pressure puts a growing strain on the way the healthcare system operates. Wards, operating theatres, and workforce pipelines are struggling to keep pace with this increasing demand. To allow healthcare to extract more value from every clinical minute and every square metre of the estate requires healthy data that can be used to make decisions quickly.
Healthy data: the indispensable foundation
Before AI can be put to use to benefit clinicians and patients, healthcare providers must make certain their data is ready for it.
Teams wanting to turn AI promise into day-to-day value must ensure the data within their hospital is reliable and fast to access.
To do that effectively in healthcare requires unified data. Simply connecting data from separate systems is not sufficient when it comes to AI.
Before AI can be put to use to benefit clinicians and patients, healthcare providers must make certain their data is ready for it
Healthcare providers need to gather each patient’s information across myriad sources, including GP notes, hospital records, lab results, imaging, and prescriptions, into one consistent record, stored in the same format and without duplicates.
This kind of approach allows healthcare organisations to build better indexing, vector search capabilities, and API connections, which translates into faster consultations, accelerated drug discovery, and simpler solution upgrades.
It also effectively creates the foundation for a hub or future-ready structure, capable of managing any AI application that is brought to it.
From prediction to practice: putting AI to work
Once they have achieved data readiness, providers can move on to practical AI implementation.
If the data fuelling AI models is of high quality, individual AI use cases can have transformational benefits on day-to-day activities and ensure that AI hype becomes everyday habit.
On the operational side, this could include automatic notifications sent to engineers when a piece of technology such as an MRI machine is out of order but a patient is scheduled for an exam, or a discharge process informing the nursing team that a room is now available for a patient to move wards.
One real-world example of this is The King Khalid Eye Specialist Hospital in Saudi Arabia, which tackled a persistent no-show problem by developing a machine learning model to predict which patients were most likely to miss appointments.
Moving beyond the purely operational gains, AI can also drive a raft of benefits when it is applied to direct patient care
Instead of relying on generic reminders or risky overbooking, they used data to target follow-ups more effectively. As a result, the hospital now schedules more appointments each day and significantly reduces lost clinician time.
The same kind of data-driven model could potentially help in several other hypothetical use cases relevant to UK healthcare.
A district-general site could stream prescribing data and central-store levels into a predictor that flags likely drug shortages forty-eight hours ahead, turning emergency couriers into scheduled deliveries.
A teaching trust might combine live bed states, theatre lists, and electronic observations in a discharge-forecasting service refreshed every quarter-hour, allowing surgical patients arriving at 8 am to find their beds ready, rather than having to wait until mid-afternoon to be made comfortable.
AI can sit silently in the background as a clinician carries out their consultation
Using AI to reduce delays in service delivery in this way across a hospital or trust from operating theatre to emergency department to outpatients is key, of course.
That’s because every delay has a knock-on effect because of how tightly connected different parts of the healthcare system are.
Moving beyond the purely operational gains, AI can also drive a raft of benefits when it is applied to direct patient care. AI can sit silently in the background as a clinician carries out their consultation, collecting key data about the patient and their medical condition, and their treatment history and plans.
That helps to inform the clinician’s engagement but also helps the hospital or GPs’ practice ensure that all information, including details from spoken conversations, is captured accurately.
Better patient care also stems from clinicians simply having more time available to focus on it
That means that a diabetic patient is not served with generic diabetes information in isolation. Instead, they receive personalised information that reflects their particular combination of diabetes, underlying heart condition, and thyroid problem, for example, rather than three separate leaflets that mean little without the broader context.
Better patient care also stems from clinicians simply having more time available to focus on it, of course. And AI can help here too, particularly in accelerating administrative tasks for clinicians.
In particular, it can play a key role in speeding up handover processes and form-filling, which nurses typically spend hours doing every week. Staff can often access the material they need from files in a matter of seconds, for example, rather than spending 10-15 minutes per file getting to the information they need.
Together, all of these practical use cases of AI in healthcare today show the great potential of the technology to turn hype into habit.
Looking to the future
Today, the perception of AI is that it will be used for making major technological breakthroughs in the fight against diseases and driving groundbreaking medical discoveries.
However, the reality is that there are many uses for AI in helping facilitate day-to-day operational improvements: from speeding up administrative tasks to better capturing and sharing patient information to deliver more personalised care.
The key for healthcare providers is identifying where the opportunity is in the here and now, and how it can be leveraged to improve the quality of care that clinicians, GPs, and other health professionals can deliver.