‍A patient receives a letter saying her surgery will be scheduled within the next three months. Relieved, she plans her work and family life around that timeline. Weeks later, another letter arrives - the original date was wrong, her case wasn’t properly recorded on the system, and she’s been placed much further down the waiting list. The trust apologises, but the damage is done: trust is eroded, anxiety rises, and the system’s credibility takes another hit.

This is not an unusual story in the NHS. And it highlights a crucial truth: before the health service can truly benefit from the promise of artificial intelligence, it must first confront the issue of data quality.

AI’s Promise vs. the NHS Reality

AI has already shown it can reduce administrative burdens, improve coding accuracy and even flag patients at risk of deterioration earlier than clinicians alone. But to deliver this safely and consistently, algorithms need clean, reliable and timely data.

And here lies the rub. NHS data, while vast, is often:

• Fragmented across multiple systems that do not speak to one another.
• Inaccurate, with pathway errors, miscoding, or incomplete records.
• Delayed, meaning decision-makers work from outdated pictures of patient demand.

Put bluntly, if data is flawed, AI will magnify the flaws. Instead of empowering clinicians and patients, it risks misdirecting resources, worsening inequalities, or eroding trust.

Why Data Quality Matters More Than Ever

At Acumentice, our work in elective care and RTT validation makes one thing crystal clear: before automation, comes accuracy.

For example:

• An AI model trained on incorrect waiting list data will not improve elective recovery - it will simply validate the wrong pathways faster.
• Patient facing AI tools are only empowering if the underlying records are accurate and joined up. Otherwise, they risk confusing patients or undermining confidence.
• Predictive analytics for workforce and capacity planning are only effective when based on validated, standardised data.

This is why data quality is the real superpower. Without it, AI is just another layer of complexity. With it, AI becomes a genuine accelerator of transformation.

Building the Right Foundations

So what does it take to unlock AI’s full value in healthcare? We believe there are three non-negotiables:

1. Relentless focus on data quality and governance
   – Accurate waiting list data, for example, must be the starting point for any predictive or automation initiative for elective care improvement.
2. Interoperability as standard, not aspiration
   – AI cannot thrive on siloed systems. Legally enforceable standards are essential to make “one version of the truth” possible.
3. Equity and inclusion baked in
   – Data sets must represent all populations fairly. Without this, AI risks amplifying the inequalities the NHS is working to reduce.

A Sneak Peek: Our RTT Companion Chatbot

At Acumentice, we’re not just analysing the challenge - we’re building solutions. That’s why we’re excited to share a preview of our upcoming open source RTT Companion Chatbot.

This tool is designed to:

• Help NHS teams navigate RTT rules and scenarios in real time.
• Provide quick, consistent answers to validation queries.
• Support new staff in learning complex RTT pathways faster.

By making it open source, we hope to democratise access to expertise, reduce variation, and free up more time for staff to focus on patient care. It’s a small but powerful example of how trusted data + practical AI tools can drive real-world improvements.

Acumentice Perspective

For us, AI in healthcare is not about replacing people - it is about empowering them with reliable insights at the right time. That is why our consultancy and digital tools focus first on getting the data right. Once organisations can trust their numbers, automation and AI can then be applied to scale improvements safely and sustainably.

The NHS does not need more hype. It needs the basics done brilliantly. Because in healthcare, data quality is not just a technical issue - it is a patient safety issue.

Final Thought

AI will not transform the NHS by itself. But if we focus on the fundamentals - better data quality, strong governance and inclusion - it can help the NHS move from firefighting to foresight.

In the end, that may be the greatest innovation of all.

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