For years, healthcare analytics in the UK has been celebrated for its potential yet hampered by one stubborn problem: proving return on investment. The NHS has delivered outstanding patient care gains through data – faster diagnoses, reduced wait times, better use of resources – but too often, these benefits are hard to quantify in purely financial terms. Without clear, credible reporting, promising initiatives risk being dismissed as 'nice-to-have' rather than essential.
The challenge is not just one of technology, but of communication. If we cannot show evidence of success in the metrics that matter most to NHS leaders and policymakers, analytics will never reach its full potential. The good news? AI and automation can help solve this – not by replacing human expertise, but by embedding robust, automated reporting into everyday workflows so that value is visible, consistent and compelling.
A persistent reporting gap
Even today, many NHS analytics projects deliver results locally but fail to resonate at the organisational or national level. One trust might use predictive analytics to optimise A&E flow and cut average triage times – but if those metrics aren't systematically captured and reported, the achievement may never influence broader funding or policy.
We've seen how addressing this gap plays out in reality. University Hospitals of Morecambe Bay NHS used real-time dashboards to monitor ED arrivals and staffing, boosting the proportion of patients triaged within 15 minutes from 70% to 94%. Wrightington, Wigan and Leigh NHS cut MRI waits from 10 days to two through better data scheduling . Both examples prove that when the right metrics are tracked and shared, analytics can show undeniable ROI in patient outcomes and efficiency.
Metrics make or break the perceived success of analytics use cases and overall progress trusts make to become more data-driven in line with the expectations of patients, managers and policymakers alike. We need solutions and AI and automation pave the way to a more systemic way of reporting.
Why AI and automation matter
Modern analytics platforms – especially those with low/no-code design and AI-assisted capabilities – make it easier than ever to integrate automated KPI reporting into the heart of every workflow. At their best, these platforms can connect to multiple NHS data sources, process them securely and generate clear outputs without requiring specialist coding skills. This makes it easier for automated reporting against KPIs to be part and parcel of analytical workflows that can be initiated by a much wider group of employees.
Somerset NHS Foundation Trust's data team, for example, supports over 100 live projects using low-code applications that deliver live waitlist projections, anomaly detection and operational forecasts to 12,000+ staff. Reports are updated automatically, ensuring decision-makers have current, trustworthy data at their fingertips.
The wide availability of low/no-code platforms to build and implement analytics workflows means Somerset's successes are a realistic north star for other trusts. As mentioned, recently added AI features in these platforms – from support for natural language prompting to create workflows to AI-generated reports and presentations from analytics – open up all new possibilities for non-technical staff to work with data. NHS procurement teams can make these features a key criterion when weighing up platforms for their organisation.
Addressing the cultural hurdle
It goes without saying that technology alone won't transform reporting culture. NHS leaders recognise that scaling analytics requires a data-literate workforce – one that sees reporting as part of care delivery, not just a back-office function.
The best initiatives to drive up data literacy combine education on the basics of working with data with hands-on training using the aforementioned low/no-code platforms. Crucially, data literacy isn't injected into a workforce by a single course. It comes via a longer-term culture of data literacy that encourages knowledge sharing and celebrates what individual colleagues are achieving in their teams with data.
Naturally, the guardrails can't be ignored. Any AI or automation in healthcare must meet NHS governance standards – from data privacy under GDPR to compliance with the NHS guidelines on AI safety and bias. NHS staff are far more likely to apply AI and automation into their workflows in a responsible way when they have foundational data literacy skills, presenting another key reason to match any analytics technology procurement and rollout with data literacy training and education.
Building the NHS of 2030
The NHS Long Term Plan and 'Data Saves Lives' strategy are clear: the health service must become genuinely data-driven by 2030. That means thousands more staff equipped to work with data and analytics embedded in everything from cancer screening to rostering. Confidence in the success of analytics use cases, bolstered by automated reporting against KPIs that NHS leaders really care about, will speed up progress made.
If we do this, analytics will no longer have to fight for its place. It will be recognised as the backbone of operational decision-making and patient care improvement.
