By Emily Harrison, Founder of Nexus HE
If there’s one thing healthcare has an abundance of, it’s data. New trial evidence, consensus statements, and expert insights are emerging daily. Yet, it’s still widely cited that knowledge can take up to 17 years to move from research into routine clinical practice.
We spoke to Emily Harrison, Founder of Nexus Healthcare Education, to find out why understanding isn’t translating more quickly into improved patient care. Passionate about teaching clinicians to build change as well as comprehend new findings, she explains why medical education must evolve if healthcare systems are to close the gap between what we know in theory and what actually happens at the bedside.
Hi Emily, thanks for joining us. Healthcare evidence is growing faster than ever before. What’s the problem?
The real challenge is the assumption that once data exists, improved practice will naturally follow. In theory, researchers should generate evidence, which then informs guidance that, in turn, shapes day-to-day healthcare. But anyone working in the sector knows it’s rarely that simple. Trials can change what we know and guidelines can change what’s recommended, but neither automatically changes what actually happens in consultation, routine care, or complex referral pathways. Ultimately, because evidence cannot implement itself, there are multiple institutionalised hurdles to be overcome before change can happen.
What stands in the way?
Change takes time. New evidence often requires clinicians to challenge long-held assumptions, adopt new technologies, and prescribe or treat outside familiar patterns. This requires both confidence and support, particularly as commissioners, managers, and even patients must be brought on board, too.
Things become more complex when decisions need to be made across specialty boundaries, requiring the kind of interdisciplinary collaboration that’s not always embedded in real-world healthcare settings. Even when the proof is there, the system is not always prepared to help doctors and nurses act on it. That mismatch is where most implementation problems start.
Can you give an example?
Yes: SGLT2 inhibitors. These medicines were originally associated with type-2 diabetes, but have since proven effective in non-diabetic heart-failure and cardio-kidney-metabolic-disease patients, too. But because clinicians and hospitals had already put them in one mental box, for diabetes, they remain vastly underused.
This is not solely due to clinician reticence, either. Insurance companies only approved one third of applications for SGLT2 use in non-diabetic heart failure patients, proving that real-world prescribing and the systems that support it need to catch up to research realities. It’s not enough to show that something works. We also need clear contextual guidance on who is eligible, who should initiate and monitor treatment, how it should be explained to patients, and how it fits into existing pathways, as well.
Does the same apply to digital innovation?
Definitely. Healthcare is currently full of promising AI tools, from imaging support to risk prediction and decision assistance. But translating those tools into everyday care is more than a technical challenge. If clinicians don’t trust them, understand their limitations, or know when and how to use them ethically, they will struggle to become part of routine practice. The same is true if workflows are too complex, if there are legal concerns, or if teams simply lack the time and support needed to incorporate something safely.
Medical education has a key role to play here. Teaching clinicians that a tool exists is not the same as preparing them to use it appropriately in real clinical settings. They need to understand uncertainty, question outputs, recognise limitations, and know when human judgement must override recommendation.
Is this one of those situations where awareness alone isn’t enough?
Yes. Most evidence-to-practice gaps are caused by poor infrastructure, rather than a lack of awareness. So, people are learning about things like AI tools or mechanical thrombectomy for acute ischemic stroke, for example, recognising theoretically that the evidence for eligible patients is strong. But delivering the intervention depends on real-world confidence and practice.
It also requires rapid recognition, imaging, ambulance routing, specialist team capability, theatre capacity, and 24/7 service availability. That kind of challenge cannot be solved by theoretical education alone. It requires pathway redesign, workforce planning, and system-wide coordination.
What does this mean for medical education?
It means starting with a better question: what’s actually preventing adoption?
If there’s a knowledge gap, evidence-led learning might be appropriate. If the issue is a lack of confidence, however, clinicians will need things like case-based discussion and expert interpretation. Similarly, low capability can be addressed by simulation and decision-making practice and, if the barrier sits across a pathway, multidisciplinary education is often the best way forward.
One-off, content-led courses are limited because information transfer cannot create the sustained behavioural change required to impact how care is actually delivered in practice. Education needs to be designed around adoption, not just awareness.
Is this what makes independent, impact-led medical education valuable?
Yes. Rather than simply telling clinicians what’s new, impact-led education helps them understand what change means for them, their patients, and their everyday practice. It creates room to explore uncertainty and interpret evidence in context, supporting the behaviours needed for practice to evolve. That’s especially important when change is complex or when new data cuts across traditional specialty boundaries.
So, education needs to change if healthcare is to close the bench-to-bedside gap for good?
Exactly. We need to measure more than course attendance, satisfaction, or completion and start examining how education improves confidence, intention, capability, and ultimately, behaviour in clinical practice.
Implementation has to be treated as part of the evidence journey, rather than an afterthought. Research will continue to advance faster than systems can comfortably absorb, which makes it even more important for organisations to design education and implementation support around real-world barriers.
Do you have one final message?
Ultimately, patients don’t benefit from knowledge just because it’s been published. They benefit when evidence changes bedside decisions, pathway design, and everyday approach. That’s where impact-led education can make a real difference, by helping healthcare professionals move from knowing that change is needed to feeling equipped, confident, and supported enough to act on it.
