AI isn’t just being used to create bad pictures of people with extra fingers. For years, it has been adopted by a range of healthcare providers as a way to manage and analyse huge swathes of data. As it moves from software to frontline, we look at ways the AI evolution could be used to bring a more human-centric approach to healthcare. 

Re-centering care around people

The pressure currently facing the NHS has rarely been greater. Rising demand, workforce shortages and increasing complexity mean clinicians are being asked to do more, often with less time. 

For many, the challenge is not only clinical workload, but the growing weight of administration that sits around it. 
Time spent on administration is time taken away from patients, reducing the opportunities for meaningful conversations, shared decision-making and compassionate care. AI can help address this, not by replacing clinical judgement but by helping to ease the friction that slows the system down.  

Used responsibly, AI offers the NHS a way to reduce administrative burden, streamline workflows and give clinicians back time. The question is not whether AI will be adopted, but whether the foundations are in place to do so safely, ethically and at scale.

Why AI needs strong foundations

AI is only as effective as the data it relies on. For tools that support triage, prioritisation, routing or summarisation, the quality, timeliness and connectivity of data are critical.

This is why data infrastructure matters. Integrated Care Systems need a complete view of people across organisations, supported by secure data sharing and connected systems. Without this, AI is limited in what it can deliver and difficult to trust.

Graphnet’s role sits firmly at this foundation level. Across ICSs, Graphnet already supports the aggregation, linking and presentation of data across multiple care settings through its shared care records and population health insight.

These capabilities are not AI tools themselves, but they underpin the data environment that AI relies on. As ICSs explore AI adoption, it is this existing infrastructure that enables tools to work with accurate, contextual and up-to-date information.

AI in practice today: not futuristic, but functional

Much of the conversation around AI in healthcare focuses on future possibilities. In reality, many of the building blocks of AI-enabled efficiency already exist within current NHS workflows, including:

• Automated risk stratification: Uses existing data to assess risk across populations, helping teams identify individuals who may benefit from earlier or more proactive support.
• Identification of high-risk cohorts: Brings together multiple data sources to surface groups with shared risk factors, enabling more targeted planning and intervention.
• Natural language processing within analytics: Unlocks insight from unstructured data such as clinical notes and correspondence, adding context that structured data alone often misses.

East Kent Hospitals Case Study 

Take the work done at East Kent Hospitals as an example. Here, perioperative services achieved a 27% reduction in preoperative consultation times through the introduction of a new digital approach to managing the perioperative pathway.

By integrating surgical waiting list data with GP records, clinicians were able to stratify patients by risk, turning a static waiting list into an active clinical tool.

This allowed low-risk patients to be booked quickly and confidently, while those with higher needs were identified earlier and directed to appropriate support. By streamlining information gathering and reducing duplication, consultation times fell, patient flow improved, and clinical capacity was freed across the service. 

Read more about the East Kent Hospitals work

Where AI will deliver value first

In the near future, the greatest impact of AI in healthcare is expected to be administrative and operational rather than clinical. This includes areas such as documentation support, triage and routing of referrals, summarisation of records, scheduling and coordination across services.

These applications are lower risk, easier to manage and directly address some of the biggest pain points experienced by staff. Crucially, they also help to create capacity.

By reducing the cognitive and administrative load on clinicians, AI-enabled tools can support better focus, better conversations and better care. 

Imagine GPs who don’t spend their day inputting data but talking to patients. Imagine care workers who aren’t spending hours writing up reports, but have those reports automatically transcribed and written so they can spend more time actually caring for people.

It’s important to bear in mind that clinical decision making remains a human responsibility, supported by experience and professional judgement. While AI may take on a greater role in judgement over time, its most effective use today is in supporting the systems around those decisions. 

Graphnet as an enabler, not a replacement

Graphnet does not position itself as a provider of clinical AI tools, nor as a replacement for clinical expertise. Its role is as an enabler of safe, responsible adoption.

By ensuring data quality, interoperability and a system-wide view of individuals and populations, Graphnet helps ICSs create an environment where AI can be introduced with confidence.

Contact us today to find out more   

 

Technology in service of human care

The future of AI in healthcare is not one where technology takes over care, but one where it supports it in the background. With administrative burden reduced, clinicians will have more time to listen, explain and connect with their patients, offering more personal care. 

By providing the data foundations that AI depends on, Graphnet supports a health system where technology works in service of people, helping the NHS move closer to what it has always aimed to be: human at its core.

Contact us today to find out more