From pain to progress: How NHS trusts are tackling the complaints crisis with AI

Summary

Healthwatch England's report, 'A pain to complain', released earlier this year reveals a stark picture: only 9% of patients experiencing poor NHS care make formal complaints, with many losing faith that their voices will drive improvement. Over half of those who do complain remain dissatisfied with both the process and outcome, while NHS organisations struggle with delayed responses and missed learning opportunities. Yet pioneering NHS organisations are finding ways to transform this dysfunctional system. In this blog, Ben Kenyon discusses how artificial intelligence (AI) can be used within NHS trusts to improve and uncover hidden insights in patient feedback

Content

The scale of the problem

In January 2025, Healthwatch England published A pain to complain, revealing a deeply critical picture of NHS complaints handling. Nearly one-quarter of adults experienced poor NHS care in the past year, yet more than half took no action at all. Of those who did act, fewer than one in ten made a formal complaint—a significant drop from 39% in 2014.

The reasons paint a picture of institutional failure: 34% believed the NHS wouldn't use their complaint to improve services, 33% doubted they'd receive an effective response and 30% felt their concerns wouldn't be taken seriously. For those who did complain, over half remained dissatisfied with both process and outcome, while 43% waited more than six months for responses.

Perhaps most troubling was the revelation that NHS organisations aren't systematically learning from complaints, with traditional manual analysis missing critical patterns and leaving dangerous gaps in safety oversight.

Breaking through: real solutions from the frontline

Against this backdrop, I've been working with forward-thinking NHS trusts recently using our Patient Experience Quality AI and Learning tool (QUAIL) to systematically improve and uncover hidden insights in patient feedback. These collaborations offer hope that the crisis isn't insurmountable.

Uncovering the invisible

Working with one major NHS trust, we deployed QUAIL's AI-powered analysis to their existing patient feedback and revealed important gaps between what they thought was true and what was taking place. In one example, traditional manual analysis had identified just two end-of-life care complaints over nearly a 2-year period. The AI-led analysis revealed 44 such cases—meaning 95% of these important patient and family experiences had been invisible to improvement efforts.

In another example within cardiology services, complaints showed minimal service accessibility issues. But when we used QUAIL to analyse Patient Advice and Liaison Services (PALS) data, a different picture emerged: complaints represented only 1% of actual patient concerns about accessing care. The remaining 99% had been hidden in hundreds of PALS enquiries that couldn't be manually themed at scale and were not visible to the organisation in a way they could act upon.

From hours to minutes

At another NHS organisation, we tackled the time pressure preventing quality responses. Complaints officers were spending 2–4 hours crafting each response letter. Through intelligent automation generating high-quality draft responses in seconds, complaints breaching the response deadline dramatically decreased freeing up staff time to focus on the human investigation, contributing towards actual quality improvement and not peripheral administrative tasks.

The ripple effect

The benefits extended far beyond individual departments. CEOs are incorporating insights into governance meetings, while analysis supported Patient Safety Incident Response Framework (PSIRF) implementation. Crucially, QUAIL enabled us to link patient complaints directly to specific action plans and improvement initiatives. Instead of generic responses, trusts could implement targeted interventions—from additional administrative staff recruitment to new telephony systems—based on precisely identified patterns.

Response times improved and time spent preparing governance reports reduced by 80% due to having relevant information available dynamically, at the touch of a button.  Most importantly, complaints were now driving measurable service improvements rather than disappearing into administrative processes.

Addressing Healthwatch's challenge

The NHS trusts we've worked with have embraced AI directly to address the systemic failures identified in the Healthwatch report by building confidence through visible pattern identification, improving responsiveness through streamlined processes, enabling genuine learning through insight-driven quality improvement, and ensuring systematic rather than biased analysis.

A new paradigm

My work with these NHS trusts demonstrates that the crisis identified by Healthwatch isn't insurmountable. We can create systems where every patient voice is heard, understood and acted upon.

This isn't about replacing human judgement with AI—it's about empowering that judgement with better insights, faster responses and deeper understanding of patient needs.

As Healthwatch concluded: "We must treat feedback, concerns and complaints as 'gold nuggets' that drive improvements to care." The NHS organisations I've worked with show us how to mine those nuggets effectively, transforming patient voices from administrative burden into the driving force for better, safer care.

Further reading on the hub:

• How do we harness technology responsibly to safeguard and improve patient care?
• Patient Safety Spotlight Interview with James Munro, Chief Executive of Care Opinion
• One size does not fit all. How AI and better data can help us embrace complexity in diagnosis and treatment
• AI in healthcare: Shifting the frame from ‘what’ to ‘how’

About the Author

Ben has spent his career solving some of healthcare's biggest problems using data, analytics and, more recently, AI. From starting on the NHS Graduate Informatics Training Scheme to leading the Business Intelligence Team at the UK's largest hospital trust, he has always striven to ensure that NHS professionals can make evidence-based, informed decisions with the best insights possible.

He also spent a significant portion of his career in private sector consultancy, engaging with NHS organisations across the country to adopt AI where it makes sense to support human judgment. In his current role at Quantium, he has been at the forefront of applying AI to patient experience, quality, and safety datasets, helping healthcare leaders better understand their operations and enact changes that benefit both their services and patients.