Interview with Richard Corbridge of SEGRO plc.
Richard Corbridge, a seasoned CIO, and British Computer Society Fellow, has spent over twenty years leading digital transformation in the NHS and private sector. Focusing on people and innovation, he drives system change that enhance patient care and customer interaction, streamlining operations, and harnessing data intelligently, shaping a future where healthcare is proactive, connected, and sustainable.
What first inspired your journey into digital leadership, and how has your career shaped the way you think about innovation?
I recall the moment when, early in my career, I walked through hospital wards filled with paper charts, nurses chasing records, doctors waiting for pages to arrive, and fax machines. Endless fax machines! And I thought: there is a better way. It wasn’t a fascination with technology for its own sake, but my frustration with the friction it caused in patient care that drove me and inspired this journey. From that point I realised that digital should not be viewed as a novelty, but as a way of fixing deeply ingrained problems of delay, of duplication, of error.
In my early career I could see how the healthcare system in the UK needed to come together to offer a more joined up approach not just for the patient who had to be at the centre but every element of the system. Data was collected and never turned into real information for clinicians and therefore insight could hardly ever be gathered without reassessment at every touch point with the clinical system.
The 2000s in the UK started to change some of that but new technology that is becoming more and more capable now is really going to revolutionise this change.
Over time, working in hospitals, trusts, public health bodies, clinical research, and outside of the first lien healthcare system, I’ve learnt that innovation doesn’t begin with the “what’s new” but with the “what hurts now?” Fixing the pain points has shaped how I lead: always starting with real problems, listening deeply to those in the industry or those delivering care or receiving it, testing, failing, iterating. And always asking: is this approach sustainable? Will this scale across the industry or the health system? Can we build trust so that change sticks?
Innovation, for me, has become less about spectacular launches and more about continuous improvement: constant small wins that add up; a culture that allows people to say “this is not working” without fear; and governance that enables speed without sacrificing reputation or safety. The next real advance ein how we use technology will be the integration of its possibilities into what we do not a new monolithic ‘thing’ that everyone must adopt.
You’re known for putting people at the centre of transformation. How has that focus influenced the way you lead and deliver results?
Putting people at the centre is not a slogan. It’s the lens through which every decision must pass. But what does this mean in practice? Firstly, it means that I want the people who are doing the work, doing the jobs, in the room from the start. I don’t want feedback forms, I want to talk to colleagues and customers who are experiencing multiple ‘pain points’ in their daily jobs, and to co-create systems that deliver solutions that address these problems, digital will either be a catalyst for the conversation of a foundation for a solution in almost all cases I believe.
This is why I aim to build psychological safety: to make sure people at every level or the organisation are comfortable and keen to speak openly about what we don’t know.
Secondly, it’s about empathy. A trait that is far too often overlooked in healthcare or enterprise IT. It’s about recognising that people are busy, change is tiring, and resistance to technological or digital transformations often reflects past change fatigue. This is why I aim to build psychological safety: to make sure people at every level or the organisation are comfortable and keen to speak openly about what we don’t know. From the board room down to the shop floor, so to speak. To admit mistakes and to reward those who flag problems early.
Digital transformations succeed when ownership is distributed, because when people see their input reflected, they become ambassadors for change rather than obstacles. I want every person how works in digital to refer to ‘our’ organisation not ‘the’ organisation as if it is some separate almost mythically unconnected thing.
You need to be crystal clear about what you are trying to achieve, and to communicate “what good looks like” effectively and often. You also need patience, particularly about the pace of change. Change in large healthcare systems, for example, doesn’t happen overnight. There is always a temptation for a CIO to overpromise. Instead, I always advise them to under-promise and over-deliver.
Finally, it’s about providing visible and accessible leadership. Not hiding behind screen, or dashboards, or app development beta test, or whatever it may be. But instead, being present across the organisation, listening, and gathering information and stories, genuinely building culture. And it’s often slow, and messy, doing things this way. But if you try to sprint without pulling everyone with you, you risk collapse or backlash. Transparency of what you are doing today, tomorrow and next week will enable engagement to be honest and support to be positive.
Having worked across both healthcare and the private sector, what key lessons about digital transformation stand out to you?
Drawing from both sectors gives distance and insight. Being clear about the incentives from the start is vital. In the private sector, the incentives are often clearer: revenue, profit, customer satisfaction. In healthcare, many of those incentives are implicit – patient safety, public health, social justice: less easily monetised but no less real. Transformation must align to those harder-to-measure incentives, or it won’t drive change. I have believed for some time that transformation in both sectors is achieved in a very similar way, a people first approach building a digital mindset that each orgnaisaiton defines and seeks the outcomes it wants from such a change.
Private sector businesses often tolerate more risk and are driven by more of that “move fast and break things” mentality. In the public sector, in healthcare, you simply cannot risk breaking things, because failures have higher stakes. So, one learns to build a pathway for experimentation with safety: pilots, simulations, governance, rollback options.
The private sector also tends to invest more in customer experience, personalisation, and so on. Whereas in the NHS, there’s a legacy of one-size-fits-all systems. I’ve seen how applying the user-centric mindset from retail or finance – personalising care, considering patient portals as “customer journeys” – dramatically improves engagement and satisfaction. By having this level of engagement the transformation is way more supported and the journey is one of supported collaboration rather than demand and control.
Breaking down data silos is always an ongoing challenge: in whatever industry or sector you work in. In healthcare, a hospital is not independent; you need data from primary care, community, labs, social care. That interconnectedness makes interoperability, standards, and regulation absolutely critical. CIOs also have to think in longer time-horizons, and act far more sustainably than in the past. While private companies might invest in upgrading tech on a regular basis, in healthcare, the choices you make now may live for decades. So, architecture, maintainability, security, and ethics have to be embedded from the start.
Finally, there is a saying I often use, which is that “we are witnessing the end of the expert and the rise of the collaborator”I’ve seen state-of-the-art technologies fail because culture, leadership, and mindset did not change. Conversely, modest technologies succeed when the people are ready, supported, and motivated and collaboration is at the heart of the goal they are trying to achieve.
Why has the NHS struggled to adopt new technologies at pace, and what do you see as the biggest barriers to change?
If I were to draw on a much used analogy, the NHS is like a huge ocean liner changing course: there’s immense momentum, and while you can move the rudder, it takes time and plenty of force before the ship responds. So many industries are quoted at the NHS as being successful in making the change, asking why hasn’t it, the NHS is not banking, tourism or leisure, it can not make mistakes and fix them.
In terms of the barriers to technology change that I see, the primary challenge in the NHS is that of legacy and fragmentation: different trusts, primary care networks, community services, social care all using different systems, different data standards. Integration is hard, costly, and often designed on top of decades of older infrastructure.
Additionally, there are very specific regulatory and governance challenges. The NHS must satisfy many layers of oversight: data protection, patient safety, clinical governance. Those are essential, although they can often become bottlenecks if not designed with agility in mind. Funding models are also a challenge in the NHS, because contracts and budgets are often annual, which means that investment in infrastructure or change programmes may need multi‐year horizons. But political cycles and short funding windows make that difficult. Also, cost pressures mean risk aversion becomes default.
There are the well-publicised workforce constraints which you have to content with in the NHS, too. Clinical staff are always under pressure. And digital, informatics, and data-science skills are always in short supply. Many clinicians are not trained for the digital tools we ask them to use. Time is always in short supply: if you’re already overburdened, asking someone to change practice without freeing up time is unlikely to work.
There is change fatigue combined with a challenging trust deficit in the NHS when it comes to adopting new technology‐enabled initiatives. Many are launched with enthusiasm, but then they are delayed, or they are left disconnected from frontline workflows. That inevitably builds scepticism. Without trust, new technologies are always viewed with suspicion.
Finally, while NHS procurement rules are rightly cautious, they can end up being far too slow and rigid. Often new hardware, software, apps or other products are selected without sufficient input from users, or with contracts that limit speed, flexibility, or interoperability. And it is that last point, interoperability – sharing data privately and securely across multiple stakeholders, organisations, different systems, and so on – that continues to be such a huge challenge with such varied data quality, and a lack of consistent standards and frameworks
How can AI, cloud computing, and secure data sharing help address challenges such as faster diagnoses, patient discharge, and easing workforce pressures?
I see these not as technologies in isolation, but as parts of a tapestry; what matters is how they interweave to deliver impact. When done well, the compound effect of these technologies is not just incremental improvement, but a shift in how care is delivered: more proactive, connected, and patient centric.
In terms of a few concrete ways this can be done, I will always highlight the importance of AI diagnostic support: AI tools in imaging, or pathology, for example, can surface an abnormality earlier, detect patterns humans might miss, and accelerate the triage workload. They can also flag any potential deteriorations in monitored patients or assist radiologists by pre-scanning imaging. But these must always be validated, explainable, and integrated into clinician’s workflows.
Clear and robust governance, as ever, is key. Because mistakes cost lives, so we must always focus on data quality, AI validation, bias checking, transparency, and human oversight. Building public trust is not optional.
Secure data sharing is the glue that holds it all together. If patient data flows across primary, secondary, community care, with patient consent, privacy protections, and standards, we can smooth transitions (such as discharge planning), prevent duplication of tests, and monitor follow-ups remotely. It allows for a much better coordination between home care, social care and hospital. Data sharing plus predictive analytics can help to identify in advance which patients are likely to need post-hospital support, social care, or community beds, and trigger those arrangements proactively. Cloud platforms can host shared dashboards across organisations.
Perhaps the biggest benefit of AI is in automating repetitive and administrative tasks such as document reviews, letter summarisations, scheduling, and so on, freeing clinical staff for care. And in AI-assisted decision support, auto-triaging, helping junior staff with guidance, and in providing new tools to upskill and enable staff across the NHS to work more efficiently. Clear and robust governance, as ever, is key. Because mistakes cost lives, so we must always focus on data quality, AI validation, bias checking, transparency, and human oversight. Building public trust is not optional.
What role should public–private collaboration play in driving sustainable innovation in UK healthcare?
For me, public–private partnership is not just useful, it’s essential. But it must be done thoughtfully, or it risks being extractive rather than additive. When it comes to research and development, and piloting new tech projects, for example, the private sector often has agility, deep technical expertise, and resources to build prototypes. Conversely, the public sector has scale, access to delivery settings, regulation, and legitimacy. Together you can test ideas – such as new AI tools, diagnostics, or apps – in real clinical contexts.
Then, once something works in a trust or region, the private sector can help with scaling, deployment, and maintenance. And similarly, shared investment in platforms or federated data-infrastructures makes sense. Private firms must align with public good, and help establish transparent standards for data, privacy, ethics, and safety. And the public sector has a role in ensuring fairness, equity, and accountability.
Talent exchange, secondments and bringing in skills from private tech firms is important, in terms of capacity building. So that the NHS can learn better from sectors where digital transformation has been faster, such as in finance or retail). It also helps to create supply chains of innovative SMEs, and start-ups, so the NHS becomes far less reliant on large incumbents.
Contracting and funding models need to be more innovative and flexible. Which means outcome-based contracts, risk-sharing, and partnering rather than simply procuring, investing in infrastructure jointly, and balancing short-term costs with long-term public value. And, of course, these collaborations must never compromise access, equality, and data privacy. The NHS must retain ownership of health data, properly anonymised where needed, to ensure benefits are not captured only by private interests.
When all of this is done well, innovation can be sustainable: not one-off or ad hoc projects but ongoing pipelines of improvement, continuously feeding back into the system.
Looking ahead to the next decade, what do you envision as the most transformative change AI and data innovation could bring to the NHS and its patients?
There are several future horizons I believe are both plausible and potentially transformative, drawn from patterns I see already emerging. Primarily, the shift from reactive to predictive care: using large‐scale population data, wearables, genomics, environmental and social determinants, to predict risk before disease manifests. And to enable early interventions to prevent hospitalisation rather than waiting for crises.
Secondly, providing personalised medicine at scale. Not just genomics but integrating phenotypic, behavioural, and social data. Tailoring care pathways and choosing diagnostics and treatments that suit individual patients, for reduced adverse events and better outcomes.
AI offers the incredible potential for the NHS to have real-time decision support embedded everywhere. AI will become part of the routine in EHRs: prompts, alerts, and suggestions. Not as “bells and whistles” but integrated with how clinicians work day to day, aware of context, history, and risk. We’ll see “AI assistants” that help, not replace.
AI and data innovation will aim to enable the home as the hub. Where wearables, remote monitoring, and telehealth will all provide rich data that flows back to centralised oversight. This means far more care delivered outside the hospital, which becomes a safety net rather than the default treatment site. And within the hospital itself, operations will become optimised by advances in predictive staffing, optimised scheduling (theatres, clinics, imaging), smarter bed management and other supply chain and logistics efficiencies that will help to wring inefficiencies out, and free up resources for care.
The wider democratisation of data and health literacy will see patients increasingly owning their data and understanding their own health trajectories; tools that help them be partners in care decisions. Transparency, fairness, and trust are all essential here.
The NHS needs AI systems that are clear, explainable, and auditable; with oversight that is visible; and with bias mitigated and more mature regulation. It also needs to think more in terms of data interoperability as infrastructure: not an afterthought. Data standards, shared platforms, seamless connectivity across hospitals, primary care, community, social care. In a decade I hope we will see this kind of data flow as the norm.
If we achieve those shifts, the NHS could move toward being a system that is resilient, anticipatory, and more humane. Patients will feel seen, doctors and nurses will feel supported, and administrators will see inefficiencies removed. And the public trust in the system will grow because these changes will be visible, thoughtful, and safe.
