Edited by the Author 23/08/2026
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Have we confused academic achievement with clinical readiness in nursing and ODP education? Exploring competence, capability and clinical practice.
There is little doubt that nurses and Operating Department Practitioners (ODPs) are better educated academically than they have ever been.
Degree-level education has brought significant benefits. It has given practitioners a stronger understanding of science, evidence-based practice, research, ethics and professional accountability. Those are important things, particularly in a healthcare system that is becoming increasingly complex.
So this isn’t an argument against university education.
But I do wonder whether, in making healthcare education increasingly academic, we have placed too much emphasis on what can be taught, measured and assessed in an educational environment, and not enough on what practitioners actually need to be able to do when they are faced with a real patient.
Perhaps the problem isn’t that healthcare education has become too academic. Perhaps academic achievement has become too easily confused with clinical readiness.
Because knowing something and being able to use that knowledge effectively are not necessarily the same thing.
Clinical competence is more than knowledge. It is more than being able to perform individual skills.
It is about bringing knowledge, technical ability, communication and judgement together when circumstances are uncertain, information is incomplete and things don’t go according to plan.
And that raises an uncomfortable question.
If our assessment systems cannot reliably tell us whether a student can bring all of those things together when it matters, can we really say that we have assessed clinical competence?
What did university education give us?
We shouldn’t romanticise the past.
Moving nursing and ODP education into higher education was not necessarily a mistake, and I don’t think anyone would seriously argue that healthcare professionals should return to an education system based largely on learning tasks and following established routines.
Healthcare has changed.
The science is more complex. Patients are often older and have more comorbidities. Treatments are more sophisticated. Professionals are expected to understand the evidence behind what they do, question established practice and recognise when something isn’t right.
Nurses and ODPs therefore need much more than a list of technical skills.
They need to understand why they are doing something, not simply how to do it.
For ODPs in particular, the move into higher education helped establish the profession as an autonomous, registered healthcare profession. The HCPC expects ODPs to practise safely and effectively, recognise the limits of their practice and exercise appropriate professional judgement.
That is a long way from simply being trained to complete a series of tasks.
And that is why I don’t think this debate can sensibly be reduced to academic versus vocational education.
Both have value.
The real question is whether we have got the balance right.
Competency isn’t the same as competence
I think there is an important distinction here that is easily missed.
We have become very good at defining competencies.
A competency can usually be described, taught, observed and assessed. It might be the ability to perform a particular procedure, demonstrate a particular skill or show that a particular learning outcome has been achieved.
That’s useful.
But competence is something bigger.
It is the ability to bring those individual competencies together and use them appropriately in the circumstances in which they are required. But there is another distinction worth making. Competence is often demonstrated against a defined standard. Clinical practice, however, doesn’t always happen in standard situations. The practitioner may have to adapt what they know to circumstances that are unfamiliar, changing or unpredictable.
That is where capability becomes important. It isn’t simply knowing what to do. It is being able to use what you know, exercise judgement and adapt when circumstances change.
And that is much harder to assess.
Imagine an anaesthetic emergency.
The patient isn’t responding as expected. The airway is becoming difficult. The equipment you need isn’t immediately where you expect it to be. Someone else in the team is asking you a question. The anaesthetist changes the plan. There is pressure building in the room.
The difficulty is that these things are not necessarily separate competencies. They interact. The practitioner has to recognise what is happening, decide what matters most, communicate effectively and adapt as the situation develops.
That is much closer to capability in practice than simply demonstrating a series of individual competencies.
There isn’t necessarily a single correct sequence of actions that can simply be recalled.
That is judgement, and it is considerably harder to assess than knowledge.
We have become very good at breaking competence down into manageable pieces: learning outcomes, competencies, skills, assessments and sign-offs.
All of these have a purpose.
But clinical practice doesn’t arrive in neatly separated compartments.
The patient doesn’t know that the student has successfully completed the airway competency, passed the physiology examination and demonstrated effective communication in three previous assessments.
What matters is whether those things can come together when they are needed.
That is the difference between demonstrating competency and demonstrating competence.
Are we measuring achievement or readiness?
Of course, modern healthcare education isn’t simply about university essays and written examinations.
That is a good thing.
But there is still an important distinction between demonstrating that somebody has achieved a learning outcome and demonstrating that they are ready to practise.
A student can produce excellent academic work, explain the relevant physiology, discuss the evidence, demonstrate the technical skill and complete the competency documentation.
Yet we may still not know how they will respond when several things happen at once.
That is not a criticism of students. It is a challenge for the way we educate and assess them.
We are generally very good at assessing things that are observable, repeatable and measurable. But some of the things that matter most in clinical practice are much harder to capture: judgement, prioritisation, adaptability and recognising when a situation is changing.
If we assess the individual components and then simply assume that the ability to integrate them will follow, are we actually assessing competence — or are we inferring it?
The question is not simply, “Can you demonstrate this?” but “Can you use it effectively when circumstances become unpredictable?”
There is a difference.
The NMC is asking some important questions too
This isn’t simply an argument being made by practitioners who remember the old days and think everything was better then.
The Nursing and Midwifery Council has been looking at practice learning and, in 2026, consulted on proposals intended to strengthen nursing and midwifery students’ practice-learning experiences.
The consultation followed work identifying variation in students’ experiences and supervision. That suggests the balance between university education and clinical experience is not necessarily a settled question, and perhaps that is no bad thing.
Professional education should never stand still. We should continually ask whether the way we educate people is actually preparing them for the reality they are going to encounter.
If there is a gap between academic achievement and clinical readiness, the answer is unlikely to be less education. The better response may be stronger integration between academic and clinical learning, longer periods of meaningful practice exposure, progressive responsibility under supervision, and assessment approaches that focus not only on what students know, but on how they apply that knowledge when circumstances are uncertain.
Simulation has a place. But it isn’t the real world.
Simulation is another area where this debate becomes interesting.
I am certainly not against simulation.
Used well, it allows students to practise emergencies they might rarely encounter, make mistakes safely, receive feedback and develop confidence.
But there is a difference between simulating a clinical situation and actually being in one.
Take an airway emergency.
A simulated scenario can test whether a student recognises the problem, knows what to do and can perform the necessary skills.
The real clinical environment adds something else.
There are interruptions, unexpected equipment issues, communication challenges, changing priorities and genuine consequences. That doesn’t mean simulation cannot reproduce some of these challenges. It can, and increasingly sophisticated simulation can provide very valuable learning. But simulation should enhance clinical experience rather than become a substitute for it.
The patient’s response may not follow the script, and neither does the team around them.
These factors are not peripheral to practice. They are part of practice.
The NMC itself recognises that simulated practice learning should enhance rather than simply replace experience in clinical practice.
That seems entirely sensible.
Perhaps the greatest value of simulation isn’t that it can reproduce clinical practice perfectly.
Perhaps its value is that it prepares students to make better use of the clinical experience that simulation cannot reproduce.
The ODP provides an interesting comparison
This is where I think the history of ODP education becomes particularly interesting.
Before ODP education moved firmly into higher education, the City & Guilds 752 Operating Department Assistant qualification provided a vocational route into operating department practice.
It wasn’t simply a classroom qualification.
It was closely tied to the clinical environment. Learners worked alongside experienced practitioners, spent prolonged periods in operating departments and progressively developed responsibility.
That matters.
The City & Guilds 752 qualification was more than a route into operating department practice. It helped provide a foundation on which the ODP profession was later built.
I’m not suggesting that the City & Guilds 752 system was perfect, or that everything that came afterwards was a mistake. Far from it.
But the older model did place considerable emphasis on sustained exposure to clinical practice, progressive responsibility and learning alongside experienced practitioners. Those characteristics are worth considering when we think about what may have been lost as education became increasingly academic.
The move into higher education brought significant benefits. ODPs became graduates, autonomous registered professionals with a much stronger academic foundation and a clearer professional identity.
But something else changed.
The balance between learning the job and learning about the job changed.
That distinction is worth thinking about.
Students learned not only how to perform tasks, but how operating departments functioned, how teams behaved, how priorities changed and how practitioners responded when things did not go according to plan.
In essence, they were learning how to become practitioners.
The modern degree model provides something the old system could not provide to the same extent: a much stronger academic foundation.
Research literacy, critical appraisal, a stronger understanding of evidence, greater professional autonomy and a broader understanding of the science behind practice.
We shouldn’t throw any of that away.
But perhaps we should ask whether, during the transition from vocational education to higher education, we retained enough of the clinical immersion and progressive responsibility of the old model to complement everything the modern academic model gives us.
In other words:
What did we gain through academicisation, and what might we have lost?
And perhaps the more uncomfortable question is:
Have we retained enough of the clinical formation of the old model to make the modern academic model work as effectively as it should?
Graduates who can also be practitioners
I don’t think the answer is to produce less academic nurses or ODPs.
Quite the opposite.
We need professionals who can understand evidence, question practice and make decisions that can be justified.
But we also need professionals who can turn that knowledge into action.
A practitioner may understand the physiology, know the guideline and appreciate the evidence that underpins practice.
The real test comes when the patient does not behave like the scenario in the textbook, information is incomplete, priorities compete and someone has to make a decision.
That is where clinical competence lives.
Teaching, assessment and academic success remain essential. However, none of them automatically guarantees clinical readiness. The challenge is ensuring that knowledge, judgement and practical performance are developed and evaluated together, rather than assuming one is a reliable substitute for the others.
The challenge isn’t to choose between the two.
It is to make sure that the graduate walking into clinical practice is capable of being more than a graduate.
They need to be a practitioner.
The question is not whether healthcare education has become too academic. It is whether we have become too comfortable measuring what graduates know and can demonstrate individually, while being less rigorous in demonstrating what they can do when it matters most.
Ultimately, patients do not experience our curriculum, learning outcomes or competency documentation. They experience the practitioner standing in front of them. Perhaps we need to think less about whether someone has completed each individual competency and more about whether they can integrate those competencies when the situation demands it.
And when the situation becomes difficult, what matters is whether that practitioner can bring everything they know, everything they have learned and everything they have practised together — and exercise appropriate judgement when it matters.
That, surely, is what clinical competence is supposed to mean.
NHS Futures & Faultlines 
If AI Is Here to Augment Healthcare, What Happens When Augmentation Becomes Substitution?
Posted: August 26, 2026 in Artificial Intelligence in Healthcare, Clinical Leadership, Human Factors, Opinion & Commentary, Patient Safety, Uncategorized, Workforce & StaffingTags: AI in Healthcare, anaesthesia, Artificial Intelligence, Clinical AI, Clinical Decision Support, Healthcare Technology
Professional expertise, workforce transformation and the future NHS clinician
Meta Description: Artificial intelligence promises to augment healthcare professionals, but could it reshape clinical roles, expertise and workforce design over time?
Whenever artificial intelligence is discussed in healthcare, we are offered a familiar reassurance.
AI isn’t going to replace doctors.
It is going to augment them.
The distinction between augmentation and substitution matters.
Augmentation occurs when technology helps professionals perform their role more effectively while preserving their responsibility, judgement and expertise. The clinician remains the decision-maker, using technology as an additional source of support.
Substitution is different. It occurs when technology begins to replace elements of professional judgement, expertise or decision-making. The clinician’s role shifts from actively thinking and deciding to monitoring and accepting recommendations generated elsewhere.
At first glance the difference may appear subtle. In reality, it represents a fundamental change in the relationship between healthcare professionals and technology. Augmentation strengthens human capability. Substitution risks allowing that capability to gradually diminish.
It will reduce administration, support clinical decision-making, improve access to information and give clinicians more time to spend with patients.
On the face of it, that sounds entirely reasonable. Healthcare contains an enormous amount of work that could be done differently. Clinicians spend considerable time documenting, searching for information, processing data and completing administrative tasks. If technology can remove some of that burden, why wouldn’t we want it to?
The trouble is that healthcare has heard similar promises before.
The introduction of physician associates, advanced nurse practitioners and anaesthesia associates was also presented as a way of increasing capacity and making better use of the workforce. The argument was not that doctors were no longer needed. Rather, it was that appropriately trained professionals could undertake work that did not always require a doctor while working alongside the existing medical workforce.
That sounds remarkably similar to the case now being made for AI.
I’m not suggesting that people and algorithms are equivalent. They clearly aren’t. But there is something useful in examining what happens when something introduced to support a workforce begins to reshape how that workforce is organised.
When Augmentation Changes the Role
The distinction between support and replacement sounds straightforward.
A new professional role can add capacity to an existing team.
Technology can help a clinician by providing information, automating documentation or supporting a decision.
The situation becomes more complicated when we stop focusing on individual tasks and start looking at how entire roles evolve.
If someone else can safely undertake a task previously performed by a doctor, that makes sense. We should not require the most highly trained professional in the system to carry out every task simply because they have traditionally done it.
The same principle applies to technology.
If AI can reliably undertake administrative work, clinicians should not need to spend valuable time doing it. If it can identify a possible abnormality on an image or assemble information from a patient’s record in seconds, there is an obvious benefit.
Once we’ve safely delegated one task, it is only natural to ask what else could be delegated or automated. Over time, the distribution of work changes. Professions do not suddenly disappear, but the work that defines them can gradually shift.
That possibility may be far more important than the familiar question of whether AI will “replace” clinicians.
The Associate-Profession Comparison Is Worth Considering
Debate around physician associates, advanced nurse practitioners and anaesthesia associates often becomes polarised.
I’m not interested in revisiting those arguments here.
There is a perfectly reasonable principle behind a multiprofessional workforce. A complex healthcare system does not need every task to be performed by a doctor, just as it does not need every task to be performed by a nurse, ODP or any other professional.
Good workforce design matches the expertise required to the work that needs to be done.
The real issue emerges after a new role has become established.
As new roles develop and demonstrate the ability to undertake additional responsibilities, role boundaries naturally expand. That is a normal part of workforce development.
It also creates a difficult question. If someone else can safely perform the work, why must it remain the responsibility of the original professional?
That is not the same as setting out to replace a profession. Yet over time it may still alter the shape of that profession.
The lesson is that the stated purpose of an innovation does not necessarily tell us what its eventual impact will be.
What Happens to the Time AI Gives Us?
This is perhaps the part of the AI discussion that interests me most.
We are repeatedly told that AI will save clinicians time. However, what happens next matters far more.
Imagine AI saves a doctor an hour a day by reducing documentation and administrative work. That time could be used to deepen patient interactions, support teaching and supervision, manage more complex clinical work, or simply see more patients.
All of those outcomes could be described as making better use of technology.
Yet they are not the same thing.
One is potentially an improvement in care. Another is an increase in capacity.
In a healthcare system under immense pressure, it is not difficult to imagine which option will appeal to organisations facing workforce shortages, financial constraints and growing waiting lists.
Technology can create capacity, but organisations decide what that capacity is used for. Whether it improves care, increases productivity or simply raises expectations of clinicians is ultimately a human choice, not a technological one.
AI Could Change Professional Roles Without Replacing Professionals
For that reason, I do not think the most useful question is whether AI will replace doctors.
It probably will not, at least not in the simplistic way that question is often framed.
The more realistic possibility is that AI progressively takes over parts of what doctors currently do. The same applies to nurses, ODPs and many other healthcare professionals.
AI already assists with documentation and information retrieval. Its role in decision support, image interpretation, risk prediction and patient monitoring will continue to grow.
That does not eliminate the professional. It changes where professional time and attention are directed.
We can already see how this might develop. A radiologist who once reviewed every image may increasingly focus on those flagged by an AI system. A doctor who once drafted clinic letters may find themselves reviewing and editing text generated automatically. In both cases the professional remains involved, but the nature of their work has changed. In surgery, similar changes could occur in preoperative assessment, imaging, intraoperative monitoring and postoperative surveillance.
In many respects, that is exactly what we should want. If technology removes repetitive work, clinicians can spend more time exercising judgement, communicating with patients and dealing with situations that do not fit neatly into established pathways.
However, we should be careful about what we mean by human oversight.
There is a significant difference between a clinician using AI as a source of information and a clinician merely approving a recommendation generated elsewhere. If the system performs most of the work and the human intervenes only when something unusual occurs, the professional role has changed substantially.
The clinician may still be in the loop, but that does not automatically mean they retain the same level of capability.
That matters because professional capability is developed and maintained through practice. If clinicians increasingly supervise decisions rather than actively making them, we need to consider what happens to expertise over time.
Competence Isn’t the Same as Capability
A system can become extraordinarily good at specific tasks.
It can recognise patterns in images.
It can summarise information.
It can identify relationships in large datasets.
It can generate differential diagnoses.
It can predict risk.
Healthcare, however, is not simply a collection of isolated tasks.
Clinical practice happens in context.
Patients do not always follow the textbook. Information is often incomplete. Two reasonable options may conflict. A patient’s preferences may not align with the statistically most likely course of action.
Sometimes the most important skill is recognising that something simply does not feel right.
That is much harder to reduce to a defined competency.
The distinction between competence and capability therefore becomes important.
Competence is often demonstrated by performing a task to an expected standard.
Capability is broader. It involves combining knowledge, skills, experience and judgement, then applying them appropriately when circumstances change.
AI may become extremely competent at many things.
The challenge is ensuring that, as routine work becomes increasingly automated, we do not inadvertently erode the professional judgement that remains.
The issue is not simply today’s workforce. Future clinicians develop expertise through repeated exposure to complexity, uncertainty and decision-making. If technology removes too much of that experience, we may discover that adaptability is considerably harder than improving efficiency.
That is not an argument against AI.
It is an argument for being deliberate about the role we expect people to play within an increasingly technology-enabled system.
Defining the Human Role
The more useful question is not whether AI replaces clinicians, but where technology genuinely adds value. Some tasks are likely to be better performed by machines, others require human judgement, and much of healthcare will continue to depend on a combination of both.
The goal should not be to preserve human involvement for its own sake, nor to automate simply because we can. It should be to understand where expertise, adaptability and judgement contribute most and design systems around those strengths.
That sounds straightforward.
It becomes less straightforward when the technology creates significant workforce, operational and financial opportunities.
The Real Test Is What We Do with the Capacity
I do not think healthcare genuinely wants AI to replace doctors.
It wants greater capacity, quicker access for patients, better decisions, fewer errors and relief from the growing administrative burden that surrounds clinical work. AI potentially offers all of those things.
But there is an important difference between using technology to remove unnecessary work and using it to reduce the number of people required to deliver care. Both may happen, and the distinction between them may become increasingly difficult to see.
That is perhaps one of the more important lessons from workforce redesign over recent decades. A role can be introduced to support an existing workforce without any intention of replacing another profession. Yet as work is redistributed, the workforce itself inevitably evolves.
AI is likely to do something similar.
The key issue, therefore, is not what we say we want AI to do when it is introduced. It is what we choose to do with the capacity it creates.
If that capacity gives clinicians more time with patients, greater opportunity to exercise judgement and more space to manage complexity, then AI could make healthcare more human rather than less.
If instead it is used primarily to allow one clinician to manage more patients, make more decisions and undertake more activity, we may arrive at a very different form of productivity.
Perhaps the real measure of AI in healthcare should not be how many professionals it allows us to replace, but how much unnecessary work it removes while preserving and strengthening the capability of the people who remain.
Technology should make it easier for healthcare professionals to do the things that matter. It should not simply make it possible for fewer people to do more.
History suggests that technologies and workforce reforms rarely end up exactly where their architects intended. Once something creates significant new capacity, the question of how that capacity is used becomes an organisational and economic one as much as a technological one.
AI will not decide whether it augments healthcare or substitutes for parts of it. We will. And perhaps the most important decision is not what AI is capable of doing, but what we decide human beings should continue to be capable of doing.
The greatest risk may not be that AI replaces clinicians. It may be that clinicians gradually lose the expertise required to supervise AI safely.