Alongside a redesign of care models to make full use of the amazing power of AI, healthcare providers must also address their ability to handle the data involved.
Healthcare stands to benefit hugely from AI but those highly-sought gains in efficiency and patient outcomes will be harder to achieve if organisations persist with bolting on applications to systems filled with poorly integrated data.
AI must be fast and intuitive in frontline medicine
Take, for example, the need to improve consultation efficiency. Conversations with physicians reveal the urgent need to use AI to address inefficiency and cut out the time spent on administrative tasks. For example, an acute care clinician might have no more than a 10-minute slot for a patient consultation. This is a narrow window in which the clinician must listen, assess, decide, explain, and record notes on a patient interaction to ensure high-quality care.
Clinicians use existing technology to the best of its capabilities, but if they take an extra five minutes after an appointment to input data and notes into the system, they will be late for their next appointment – a pattern that can spiral throughout the day. This leads to lengthy delays for patients and exhausts clinicians trying to make up time. Small inefficiencies in healthcare can compromise the quality and continuity of care.
Here is also where the right technology can be utilised further while shifting the starting point for care itself. For example, it can enable patients to contribute relevant health data ahead of their consultation which in turns means the ten-minute window effectively begins much earlier. By the time the patient walks into the room, the clinician already has a head start, armed with structured information that can be reviewed, analysed, and prioritised by AI before the first question is even asked.
New AI solutions must be fast, intuitive, and designed around real-world clinical workflows if they are going to help create a positive impact within a healthcare setting. This is much easier if the data is clean, standardised, and fully interoperable.
AI’s accuracy opens up greater personalisation
From there, once the information is in an electronic record, AI enables other clinicians and administrative staff to find the exact information they need without having to trawl through every entry. This is not just more efficient, it also enables greater personalisation in medicine by allowing medical professionals to review individual patient needs, regardless of the complexity of the condition.
If an organisation has well-integrated, unified data, information can be shared with relevant colleagues much more easily as part of a wider and more far-reaching level of service improvement. Moving forwards, the hope is that this can become the norm rather than the preserve of high-end institutions if health organisations have the right approach to their data.
The quality of data integration is critical
The question of how well healthcare organisations integrate their data is critical for AI. If they have a unified structure free of duplication, they can build better indexing and search capabilities. Instead of laboriously implementing one application at a time, they will already have their unified data in a hub where any application can connect with it. In many ways it brings AI to the data rather than taking the data back to the AI.
This is a future-ready structure that provides improved supervision of output quality for AI applications and better cost-control by sharing only what is necessary. If we take ICU monitors, for example, the data may show within an electronic medical record, but the values are not in the system. With a unified architecture, the data from the monitors and the hospital’s own system can sit together. This is the whole data picture healthcare organisations need if they want to use an AI application or enable it to learn how to use the data.
Forward-thinking to overcome a historical legacy
Within the healthcare sector, many organisations are held back from this because of their historical divisions and the way each department has used and jealously guarded its data. There has never been any need for an overarching strategy until the dawn of AI.
Now, however, any major healthcare organisation has hundreds of workflows that once transformed by AI will drive up productivity and improve outcomes for patients. But this needs to be part of a wider strategic redesign of healthcare; one that includes shifting the starting line for care earlier, empowering patients to provide data in advance, and allowing AI to enhance and contextualise that information before the in-person consultation begin.
Prepare for the pace of AI innovation
As they move forward, organisations can learn from the experience with electronic medical records. For more than two decades, hospitals have used best-of-breed solutions before realising the cost was too high. At that point, some opted for a system based on unified data – something which is becoming the standard.
Although the adoption curve for important new technologies has been 15 years in most industries, with AI we can expect this to be much shorter – perhaps even as little as five years. This faster pace of adoption means organisations need to think now about data management for their AI use cases.
Safeguards and compliance
That includes the need for safeguards to offer protection from the occasional hallucinations of generative AI. Organisations must also ensure their AI complies with the highly complex web of regulations governing medical devices, patient safety, and privacy, ensuring rigorous control of patient data even though many clinicians are likely to need access to it.
Here again, there are significant advantages to a unified data approach. If an organisation is, in effect, bolting on AI applications to its systems, rather than unifying its data, it may well have to outsource compliance because the body of regulation is constantly changing. This will inevitably cause a higher degree of duplication which is time-consuming when it comes to using that data to make decisions.
Unity, simplification and control
A unified data approach, however, with native AI capabilities that organisations can build themselves, gives far more control, achieving compliance without the duplication. It is infinitely more scalable and delivers results far more quickly – which is important as AI’s impact on all aspects of healthcare constantly grows.
A unified data architecture also removes the need for weeks of work connecting and safety and compliance-testing of each new AI application that is approved for use. The level of control conferred also reduces risk by enabling AI to be introduced more gradually than is possible with a bolt-on approach. This enables healthcare providers to evaluate new capabilities and ground them for adjustment if that proves necessary.
AI, then, is set to improve efficiency and productivity by addressing individual workflows in complex health organisations that are treating thousands of patients each day. Yet these are only pieces in the wider puzzle. The bigger picture is one where entire health pathways are redesigned to bring patients and clinicians the full benefits of AI. Achieving this, however, demands organisations to overcome their siloed structures and urgently address the need for fully harmonised and unified data that is fit for the AI-powered future.
Urgency and opportunity
There’s a clear opportunity to make progress. When data is connected, when patients can share information ahead of time, and when AI is used to focus on the moments that matter most, it takes pressure off clinicians and helps improve both the quality and consistency of care.
Putting the right safeguards, governance, and audit processes in place from the beginning means organisations can scale confidently and avoid costly rework later. Those with strong data foundations today will be in a better position to adopt new technologies as they evolve and to see the benefits sooner.
