Let me start with a personal story. My Grandmother turns 94 this year, is the matriarch of our family and an absolute warrior. In recent years her health has started to fail her, which has unfortunately coincided with a rapid decline in patient care from the NHS, meaning she has experienced prolonged waiting times to see clinicians, leading to increased concerns from her, and the rest of our family.
Now let’s set one thing straight, I am no ‘NHS-basher’. I have various family members and friends that work for the NHS who do an outstanding job under very difficult conditions, but speaking as someone that has first-hand experience, I’m sure we can all agree that the NHS is under immense pressure. According to NHS England, the Referral to Treatment (RTT) waiting list is now at over 7.4 million cases, and despite the incredible work of NHS teams, many patients are waiting far beyond the 18-week target for treatment. In fact, nearly 3.06 million patients have been waiting more than 18 weeks, and almost 199,000 have been waiting over a year NHS England.
That’s not just a statistic, it’s people; patients dealing with pain, anxiety, and delayed recovery, and while the reasons behind the backlog are complex, it’s clear that traditional approaches aren’t keeping up with demand.
So, where does agentic AI come in?
The Problem: Why the NHS RTT Target Feels Out of Reach
Let’s be honest, no amount of goodwill, overtime, or process tweaks is going to single-handedly fix a system that has been creaking for many years. The NHS needs systems that can work smarter, not just harder. A huge part of the challenge isn’t just about clinical resources, it’s about coordination; managing patient flow, scheduling, diagnostics, referrals, and ensuring that people get seen at the right time, in the right place.
At the moment, much of this relies on archaic manual admin processes, fragmented data, and overstretched teams juggling competing priorities. That’s where agentic AI (AI that can act autonomously, make decisions, and optimise processes without needing constant human input), has serious potential.
What Could Agentic AI Actually Do?
We’re not talking about replacing doctors or nurses here! This is about AI-driven agents that can support healthcare teams, take on repetitive tasks, and ensure that patients don’t get lost in the system.
1. Optimising Appointments & Scheduling AI can predict patient flow, automate bookings, and intelligently reschedule cancellations, reducing wasted slots and making sure clinicians’ time is used efficiently.
2. Proactive Patient Management Agentic AI can triage referrals based on urgency, flagging patients at risk of deterioration before they reach crisis point. By analysing vast datasets, AI can help identify patterns, support faster diagnoses, and recommend personalised treatment plans
3. Reducing Administrative Burden A staggering amount of NHS time is spent on manual data entry, paperwork, and chasing updates. AI can automate referral processing, update records in real time, and free up staff to focus on care.
4. Enhancing Clinical Decision-Making AI-powered decision support tools can help interpret test results, flag anomalies, and even recommend treatment pathways, allowing clinicians to work faster and more accurately.
This Isn’t Just Theory, It’s Already Happening!
While this might sound like a future-gazing exercise, healthcare systems around the world are already leveraging AI to make a tangible impact:
🔹 Apollo Hospitals in India has deployed AI-driven assistants to automate clinical documentation, aiming to save clinicians up to three hours per day Reuters.
🔹 A recent study in the UAE implemented an AI-powered no-show prediction model that resulted in a significant 50.7% reduction in no-show rates. The model analysed various factors to forecast patient attendance, allowing healthcare providers to optimise scheduling and improve operational efficiency PMC.
🔹 Zebra Medical Vision has developed FDA-approved AI algorithms that assist radiologists by automatically detecting conditions such as intracranial haemorrhages and pneumothorax in medical imaging. These tools enhance diagnostic accuracy and expedite patient management ITN.
Could This Work for the NHS?
Absolutely…but only if it’s implemented in the right way. AI isn’t a magic fix, and it comes with challenges: data integration, ethical considerations, and the need for trust and transparency. But with the right governance, human oversight, and investment, the potential for agentic AI to reduce NHS backlogs and improve patient care is huge.
The conversation isn’t about whether AI should be involved in healthcare, it already is. The question is: how can we harness it in a way that delivers meaningful, measurable impact for both NHS staff and patients?
It’s time to stop looking at AI as a future possibility and start embedding it into solutions today!
What do you think? Is AI the missing link in solving NHS waiting time challenges? I’d love to hear your thoughts, send them to hello@warp.co.uk.
