Who is planning for the future of physiotherapy?

In the middle ages, cities could spend more than 100 years building a cathedral while at the same time believing that the apocalypse was imminent. They must’ve had a remarkable conviction that commissioning these projects would guarantee them eternal salvation. Compare this to the way we think about planning and design today where, for example, we don’t think more than 3 years into the future simply because that would fall outside of this organisational election cycle. Sometimes it feels like the bulk of the work that a politician does today is to secure the funding that will get them re-elected tomorrow. Where do we see real-world examples of long-term planning that will help guide our decision-making in the present?

A few days ago I spent some time preparing feedback on a draft of the HPCSA minimum requirements for physiotherapy training in South Africa and one of the things that struck me was how much of it was just more-of-the-same. This document is going to inform physiotherapy education and practice for at least the next decade and there was no mention of advances at the cutting edge of medical science and the massive impact that emerging technologies are going to have on clinical practice. Genetic engineering, nanotechnology, artificial intelligence and robotics are starting to drive significant changes in healthcare and it seems that, as a profession, we’re largely oblivious to what’s coming. It’s dawned on me that we have no real plan for the future of physiotherapy (the closest I’ve seen is Dave Nicholls new book, called ironically, The End of Physiotherapy).

What would a good plan look like? In the interests of time, I’m just going to take the high-level suggestions from this article on how the US could improve their planning for AI development and make a short comment on each (I’ve expanded on some of these ideas in my OpenPhysio article on the same topic).

  • Invest more: Fund research into practice innovations that take into account the social, economic, ethical and clinical implications of emerging technologies. Breakthroughs in how we can best utilise emerging technologies as core aspects of physiotherapy practice will come through funded research programmes in universities, especially in the early stages of innovation. We need to take the long-term view that, even if robotics, for example, isn’t having a big impact on physiotherapy today, one day we’ll see things like percussion and massage simply go away. We will also need to fund research on what aspects of the care we provide are really valued by patients (and what they, and funders, will pay for).
  • Prepare for job losses: From the article: “While [emerging technologies] can drive economic growth, it may also accelerate the eradication of some occupations, transform the nature of work in other jobs, and exacerbate economic inequality.” For example, self-driving cars are going to massively drive down the injuries that occur as a result of MVAs. Orthopaedic-related physiotherapy work is, therefore, going to dry up as the patient pool gets smaller. Preventative, personalised medicine will likewise result in dramatic reductions in the incidence of chronic conditions of lifestyle. The “education” component of practice will be outsourced to apps. Even if physiotherapy jobs are not entirely lost, they will certainly be transformed unless we start thinking of how our practice can evolve.
  • Nurture talent: We will need to ensure that we retain and recapture interest in the profession. I’m not sure about other countries but in South Africa, we have a relatively high attrition rate in physiotherapy after a few years of clinical work. The employment prospects and long-term career options, especially in the public health system, are quite poor and many talented physiotherapists leave because they’re bored or frustrated. I recently saw a post on LinkedIn where one of our most promising graduates from 5 years ago is now a property developer. After 4 years of intense study and commitment, and 3 years of clinical practice, he just decided that physiotherapy isn’t where he sees his long-term future. He and many others who have left health care practice represent a deep loss for the profession.
  • Prioritise education: At the undergraduate level we should re-evaluate the curriculum and ensure that it is fit for purpose in the 21st century. How much of our current programmes are concerned with the impact of robotics, nanotechnology, genetic engineering and artificial intelligence? We will need to create space for in-depth development within physiotherapy but also ensure development across disciplines (the so-called T-shaped graduate). Continuing professional development will become increasingly important as more aspects of professional work change and over time, are eradicated. Those who cannot (or will not) continue learning are unlikely to have meaningful long-term careers.
  • Guide regulation: At the moment, progress in emerging technologies is being driven by startups who are funded with venture-capital and whose primary goal is rapid growth to fuel increasing valuations. This ecosystem doesn’t encourage entrepreneurs to limit risks and instead pushes them to “move fast and break things”, which isn’t exactly aligned with the medical imperative to “first do no harm”. Health professionals will need to ensure that technologies that are introduced into clinical practice are first and foremost serving the interests of patients, rather than driving up the value of medical technology startups. If we are not actively involved in regulating these technologies, we are likely to find our practice subject to them.
  • Understand the technology: In order to engage with any of the previous items in the list, we will first need to understand the technologies involved. For example, if you don’t know how the methods of data gathering and analysis can lead to biased algorithmic decision-making, will you be able to argue for why your patient’s health insurance funder shouldn’t make decisions about what interventions you need to provide? We need to ensure that we are not only specialists in clinical practice, but also specialists in how technology will influence clinical practice.

Each of the items in the list above is only very briefly covered here, and each could be the foundation for PhD-level programmes of research. If you’re interested in the future of the profession (and by that I mean you’re someone who wonders what health professional practice will look like in 100 years), I’d love to hear your thoughts. Do you know of anyone who has started building our cathedrals?

An introduction to artificial intelligence in clinical practice and education

Two weeks ago I presented some of my thoughts on the implications of AI and machine learning in clinical practice and health professions education at the 2018 SAAHE conference. Here are the slides I used (20 slides for 20 seconds each) with a very brief description of each slide. This presentation is based on a paper I submitted to OpenPhysio, called: “Artificial intelligence in clinical practice: Implications for physiotherapy education“.


The graph shows how traffic to a variety of news websites changed after Facebook made a change to their Newsfeed algorithm, highlighting the influence that algorithms have on the information presented to us, and how we no longer really make real choices about what to read. When algorithms are responsible for filtering what we see, they have power over what we learn about the world.


The graph shows the near flat line of social development and population growth until the invention of the steam engine. Before that all of the Big Ideas we came up with had relatively little impact on our physical well-being. If your grandfather spent his life pushing a plough there was an excellent chance that you’d spend your life pushing one too. But once we figured out how to augment our physical abilities with machines we saw significant advances in society and industry and an associated improvement in everyones quality of life.


The emergence of artificial intelligence in the form of narrowly constrained machine learning algorithms has demonstrated the potential for important advances in cognitive augmentation. Basically, we are starting to really figure out how to use computers to enhance our intelligence. However, we must remember that we’ve been augmenting our cognitive ability for a long time, from exporting our memories onto external devices, to performing advanced computation beyond the capacity of our brains.


The enthusiasm with which modern AI is being embraced is not new. The research and engineering aspects of artificial intelligence have been around since the 1950s, while fictional AI has an even longer history. The field has been through a series of highs and lows (called AI Winters). The developments during these cycles were fueled by what has become known as Good Old Fashioned AI; early attempts to explicitly design decision-making into algorithms by hard coding all possible variations of the interactions in a closed-environment. Understandably, these systems were brittle and unable to adapt to even small changes in context. This is one of the reasons that previous iterations of AI had little impact in the real world.


There are 3 main reasons why it’s different this time. The first is the emergence of cheap but powerful hardware (mainly central and graphics processing units), which has seen computational power growing by a factor of 10 every 4 years. The second characteristic is the exponential growth of data, and massive data sets are an important reason that modern AI approaches have been so successful. The graph in the middle column is showing data growth in Zettabytes (10 to the power of 21). At this rate of data growth we’ll run out metric system in a few years (Yotta is the only allocation after Zetta). The third characteristic of modern AI research is the emergence of vastly improved machine learning algorithms that are able to learn without being explicitly told what to learn. In the example here, the algorithm has coloured in the line drawings to create a pretty good photorealistic image, but without being taught any of the concepts i.e. human, face, colour, drawing, photo, etc.


We’re increasingly seeing evidence that in some very narrow domains of practice (e.g. reasoning and information recall), machine learning algorithms can outdiagnose experienced clinicians. It turns out that computers are really good at classifying patterns of variables that are present in very large datasets. And diagnosis is just a classification problem. For example, algorithms are very easily able to find sets of related signs and symptoms and put them into a box that we call “TB”. And increasingly, they are able to do this classification better than the best of us.


It is estimated that up to 60% of a doctors time is spent capturing information in the medical record. Natural language processing algorithms are able to “listen” to the ambient conversation between a doctor and patient, record the audio and transcribe it (translating it in the process if necessary). It then performs semantic analysis of the text (not just keyword analysis) to extract meaningful information which it can use to populate an electronic health record. While the technology is in a very early phase and not yet safe for real world application it’s important to remember that this is the worst it’s ever going to be. Even if we reach some kind of technological dead end with respect to machine learning and from now on we only increase efficiency, we are still looking at a transformational technology.


An algorithm recently passed the Chinese national medical exam, qualifying (in theory) as a physician. While we can argue that practising as a physician is more than writing a text-based exam, it’s hard not to acknowledge the fact that – at the very least – machines are becoming more capable in the domains of knowledge and reasoning that characterise much of clinical practice. Again, this is the worst that this technology is ever going to be.


This graph shows the number of AI applications under development in a variety of disciplines, including medicine (on the far right). The green segment shows the number of applications where AI is outperforming human beings. Orange segments show the number of applications that are performing relatively well, with blue highlighting areas that need work. There are two other points worth noting: medical AI is the area of research that is clearly showing the most significant advances (maybe because it’s the area where companies can make the most money); and all the way at the far left of the graph is education, showing that there may be some time before algorithms are showing the same progress in teaching.


Contrary to what we see in the mainstream media, AI is not a monolithic field of research; it consists it consists of a wide variety of different technologies and philosophies that are each sometimes referred to under the more general heading of “AI”. While much of the current progress is driven by machine learning algorithms (which is itself driven by the 3 characteristics of modern society highlighted earlier), there are many areas of development, each of which can potentially contribute to different areas of clinical practice. For the purposes of this presentation, we can define AI as any process that is able to independently achieve an objective within a narrowly constrained domain of interest (although the constraints are becoming looser by the day).


Machine learning is a sub-domain of AI research that works by exposing an algorithm to a massive data set and asking it to look for patterns. By comparing what it finds to human-tagged patterns in the data, developers can fine-tune the algorithm (i.e. “teach it) before exposing it to untagged data and seeing how well it performs relative to the training set. This generally describes the “learning” process of machine learning. Deep learning is a sub-domain of machine learning that works by passing data through many layers, allocating different weights to the data at each layer, thereby coming up with a statistical “answer” that expresses an outcome in terms of probability. Deep learning neural networks underlie many of the advances in modern AI research.


Because machine and deep learning algorithms are trained on (biased) human-generated datasets, it’s easy to see how the algorithms themselves will have an inherent bias embedded in the outputs. The Twitter screenshot shows one of the least offensive tweets from Tay, an AI-enabled chatbot created by Microsoft, which learned from human interactions on Twitter. In the space of a few hours, Tay became a racist, sexist, homophobic monster – because this is what it learned from how we behave on Twitter. This is more of an indictment of human beings than it is of the algorithm. The other concern with neural networks is that, because of the complexity of the algorithms and the number of variables being processed, human beings are unable to comprehend how the output was computed. This has important implications when algorithms are helping with clinical decision-making and is the reason that resources are being allocated to the development of what is known as “explainable AI”.


As a result of the changes emerging from AI-based technologies in clinical practice we will soon need to stop thinking of our roles in terms of “professions” and rather in terms of “tasks”. This matters because increasingly, many of the tasks we associate with our professional roles will be automated. This is not all bad news though, because it seems probable that increased automation of the repetitive tasks in our repertoire will free us up to take on more meaningful tasks, for example, having more time to interact with patients. We need to start asking what are the things that computers are better at and start allocating those tasks to them. Of course, we will need to define what we mean by “better”; more efficient, more cost-effective, faster, etc.


Another important change that will require the use of AI-based technologies in clinical practice will be the inability of clinicians to manage – let alone understand – the vast amount of information being generated by, and from, patients. Not only are all institutional tests and scans digital but increasingly, patients are creating their own data via wearables – and soon, ingestibles – all of which will require that clinicians are able to collect, filter, analyse and interpret these vast streams of information. There is evidence that, without the help of AI-based systems, clinicians simply will not have the cognitive capacity to understand their patients’ data.


The impact of more patient-generated health data is that we will see patients being in control of their data, which will exist on a variety of platforms (cloud storage, personal devices, etc.), none of which will be available to the clinician by default. This means that power will move to the patient as they make choices about who to allow access to their data in order to help them understand it. Clinicians will need to come to terms with the fact that they will no longer wield the power in the relationship and in fact, may need to work within newly constituted care teams that include data scientists, software engineers, UI designers and smart machines. And all of these interactions will be managed by the patient who will likely be making choices with inputs from algorithms.


The incentives for enthusiastic claims around developments in AI-based clinical systems are significant; this is an acdemic land grab the likes of which we have only rarely experienced. The scale of the funding involved puts pressure on researchers to exaggerate claims in order to be the first to every important milestone. This means that clinicians will need to become conversant with the research methods and philosophies of the data scientists who are publishing the most cutting edge research in the medical field. The time will soon come when it will be difficult to understand the language of healthcare without first understanding the language of computer science.


The implications for health professions educators are profound, as we will need to start asking ourselves what we are preparing our graduates for. When clinical practice is enacted in an intelligent environment and clinicians are only one of many nodes in vast information networks, what knowledge and skills do they need to thrive? When machines outperform human beings in knowledge and reasoning tasks, what is the value of teaching students about disease progression, for example? We may find ourselves graduating clinicians who are well-trained, competent and irrelevant. It is not unreasonable to think that the profession called “doctor” will not exist in 25 years time, having been superseded by a collective of algorithms and 3rd party service providers who provide more fine-grained services at a lower cost.


There are three new literacies that health professions educators will need to begin integrating into our undergraduate curricula. Data literacy, so that healthcare graduates will understand how to manage, filter, analyse and interpret massive sets of information in real-time; Technological literacy, as more and more of healthcare is enacted in digital spaces and mediated by digital devices and systems; and Human literacy, so that we can become better at developing the skillsets necessary to interact more meaningfully with patients.


There is evidence to suggest that, while AI-based systems outperform human beings on many of the knowledge and reasoning tasks that make up clinical practice, the combination of AI and human originality results in the most improved outcomes of all. In other words, we may find that patient outcomes are best when we figure out how to combine human creativity and emotional response with machine-based computation.


And just when we’re thinking that “creativity” and “originality” are the sole province of human beings, we’re reminded that AI-based systems are making progress in those areas as well. It may be that the only way to remain relevant in a constantly changing world is to develop our ability to keep learning.

OpenPhysio abstract: Artificial intelligence in clinical practice – Implications for physiotherapy education

Here is the abstract of a paper I recently submitted to OpenPhysio, a new open-access journal with an emphasis on physiotherapy education.

About 200 years ago the invention of the steam engine ushered in an era of unprecedented development and growth in human social and economic systems, whereby human labour was supplanted by machines. The recent emergence of artificially intelligent machines has seen human cognitive capacity augmented by computational agents that are able to recognise previously hidden patterns within massive data sets. The characteristics of this second machine age are already influencing all aspects of society, creating the conditions for disruption to our social, economic, education, health, legal and moral systems, and which will likely to have a far greater impact on human progress than did the steam engine. As AI-based technology becomes increasingly embedded within devices, people and systems, the fundamental nature of clinical practice will evolve, resulting in a healthcare system requiring profound changes to physiotherapy education. Clinicians in the near future will find themselves working with information networks on a scale well beyond the capacity of human beings to grasp, thereby necessitating the use of intelligent machines to analyse and interpret the complex interactions of data, patients and the newly-constituted care teams that will emerge. This paper describes some of the possible influences of AI-based technologies on physiotherapy practice, and the subsequent ways in which physiotherapy education will need to change in order to graduate professionals who are fit for practice in a 21st century health system.

Read the full paper at OpenPhysio (note that this article is still under review).

We’re all in beta

I was talking to Ben Ellis (@bendotellis) from Oxford Brookes University at the ER-WCPT conference in Liverpool last year and bemoaning the fact that the most interesting conversations – for me anyway – were happening outside of the sessions. This is probably not news for anyone who’s gone to more than a few conferences. We started chatting about how we could take the things “that work” from conferences e.g. sharing ideas, and removing the things that “don’t work” e.g. a few people talking at other people for 10 minutes at a time.

We’ve been toying with a few different options – influenced by unconferences and teachmeets – but always trying to keep the following broad principles around the format in focus:

  • Broad perspectives. Encourage input from a wide variety of expertise, rather than have the “expert” talk to everyone else.
  • Collaborative. Obviously.
  • Multiple activities. The session should include presenting, writing, and discussion, all around an artifact of some sort.
  • Preparation. We wanted the presenter to bring something to the discussion in the form of an idea that the conversation would centre on.

We’ve decided that a Google+ community seems to have the basic features we need to implement the principles described above. We also wanted to reinforce the idea that we’re all constantly learning how to do things better, and that it makes sense to learn from each other. It seems likely that the things we want to do in our classrooms have been done, in some form, somewhere else. And there’s no need to wait until the next conference and hope that your abstract gets accepted. As Ben said in his post on the project:

The idea of In Beta is for physiotherapy educators to bring a teaching and learning idea that they have been working on themselves or with colleagues within their institution to a group of peers in order to spark discussion, feedback and collaboration to improve and develop the original idea prior to releasing it into the classroom or clinic.

In essence, we want to use this space to encourage physio educators to experiment on their own approaches to teaching and learning practices. We have a basic structure for each session, which may change as we experiment with the format:

  1. Members of the In Beta community share an idea for a change in their teaching and learning practice.
  2. Community members choose a topic on a monthly basis, and help the person who submitted the topic develop the idea for presentation.
  3. The presenter shares their idea with the community, probably in a formal presentation (e.g. slideshow), but could equally be something different (e.g. collection of Pinterest images).
  4. There is a discussion around the idea where everyone gets to share their ideas and suggestions for improvement.
  5. During this process, the original submission is open for editing, so the community members present in the session are able to comment, make suggestions, share resources, generate a reading list, etc.
  6. We encourage those who presented to share their experiences back into the community following implementation of the idea.
  7. Finally, we hope that the process generates a collection of shared resources and ideas for other community members who may be interested in similar contexts.

Anyone with an interest in any aspect of physiotherapy education (clinical or practice-based educators as well as academics) can join the Google+ community for In Beta (Google account required).

The first In Beta session will be at 2pm (UK) on Wednesday 19th July 2017. The topic is teaching long term condition management and the background and outline of the teaching idea is available via the In Beta Google+ community. Thanks to Ben for taking the plunge and offering to be the guinea pig in this experiment.

Remember, it’s not defective. It’s in beta.

Our students succeed despite their education, not because of it

Note: Thank you to Dave Nicholls from the Critical Physiotherapy Network for his insight and comments that helped inform this post.

Foucault said that the most dangerous ideas were the ones that we’re not even aware of; the ones we accept as being fundamentally true. He emphasised the need to examine our everyday practices and to critically analyse the discourses that make these practices possible. He believed that the most powerful disciplinary ideas are the ones that are most benign – the ones that we readily accept. This post is an introduction to a series of critiques (some might say, rants) against the ideas that we most take for granted in our teaching practices. The things that we readily accept as being self-evidently true.

These ideas form the foundation of every professional education programme, yet I will argue that they are also the most dangerous obstacles to real learning. I think that our current educational system not only prevents students from working towards deeper understanding with open minds but actually provides incentives to do the opposite. In this series of posts I’ll present some of the ideas that we accept to be foundational in the undergraduate curriculum but which actually lead students away from developing the outcomes we say we value.

I think that our students succeed despite their education, not because of it.

After decades of research in the fields of cognitive psychology and neuroscience we can be confident of one thing…we can do better. If I look at what a modern health system needs – creative problem solvers, innovative leaders, collaborative team players, critical thinkers – it seems evident that these are exactly the characteristics that our current programmes cannot provide. Our legacy systems are broken, outdated and unfit for the purpose of graduating clinicians with the attributes necessary to address the complex health needs of people in the the 21st century.

What if we designed a curriculum from scratch using everything that we’ve learned from the research into learning and cognition? What would a curriculum look like if we critically questioned every aspect of it, asking if those components lead effectively towards the achievement of our goals? How would we choose the curriculum configuration if we were not constrained by what the institutional LMS and the timetable required? I wonder what a curriculum might look like if it didn’t have to conform to the requirements of a system that hasn’t changed much in 500 years. I think that that it could be an exciting and inspiring thing of beauty.

As a thought experiment I’m going to write a series of posts looking at the ideas that we simply accept as being fundamental to the curriculum, and then argue for why those are the very things that need to go. In each post I’ll take a future position where we have already implemented the changes that I think are necessary, and then argue for why the changes were made. The series is called altPhysio.

Research is about pushing and extending the boundaries of knowledge in order to create new spaces for practice. But despite all the evidence that change is necessary we continue teaching in much that same way that we always have. We’re creating the conceptual spaces for new and innovative practices in physiotherapy education…it’s time we started occupying them.

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Physiotherapy in 2050: Ethical and clinical implications

This post describes a project that I began earlier this week with my 3rd year undergraduate students as part of their Professional Ethics module. The project represents a convergence of a few ideas that have been bouncing around in my head for a couple of years and are now coming together as a result of a proposal that I’m putting together for a book chapter for the Critical Physiotherapy Network. I’m undecided at this point if I’ll develop it into a full research proposal, as I’m currently feeling more inclined to just have fun with it rather than turn it into something that will feel more like work.

The project is premised on the idea that health and medicine – embedded within a broader social construct – will be significantly impacted by rapidly accelerating changes in technology. The question we are looking to explore in the project is: What are the moral, ethical, legal, and clinical implications for physiotherapy practice when the boundaries of medical and health science are significantly shifted as a result of technological advances?

The students will work in small groups that are allocated an area of medicine and health where we are seeing significant change as a result of the integration of advanced technology. Each week in class I will present an idea that is relevant to our Professional Ethics module (for example, the concept of human rights) and then each group will explore that concept within the framework of their topic. So, some might look at how gene therapy could influence how we think about our rights, while others might ask what it even means to be human. I’m not 100% how this is going to play out and will most likely adapt the project as we progress, taking into account student feedback and the challenges we encounter. I can foresee some groups having trouble with certain ethical constructs simply because it may not be applicable to their topic.

Exoskeletons are playing an increasingly important role in neurological rehabilitation.
Exoskeletons playing an increasingly important role in neurological rehabilitation.
The following list and questions aim to stimulate the discussion and to give some idea of what we are looking at (this list is not exhaustive and I’m still playing around with ideas – suggestions are welcome):

  1. Artificial intelligence and algorithmic ethical decision-making. Can computers be ethical? How is ethical reasoning incorporated into machines? How will ethical algorithms impact health, for example, when computers make decisions about organ transplant recipients? Can ethics programmed into machines?
  2. Nanotechnology. As our ability to manipulate our world at the atomic level advances, what changes can we expect to see for physiotherapists and physiotherapy practice? How far can we go with integrating technology into our bodies before we stop being “human”?
  3. Gene therapy. What happens when genetic disorders that provide specialisation areas for physiotherapists are eradicated through gene therapy? What happens when we can “fix” the genetic problems that lead to complications that physiotherapists have traditionally had a significant role in. For example, what will we do when cystic fibrosis is cured? What happens when we have a vaccine for HIV? Or when ALS is little more than an inconvenience?
  4. Robotics. What happens when patients who undergo amputations are fitted with prosthetics that link to the nervous system? When exoskeletons for paralysed patients are common? How much of robotic systems will students need to know about? Will exoskeletons be the new wheelchairs?
  5. Aging. What happens when the aging population no longer ages? How will physiotherapy change as the human lifespan is extended? There is an entire field of physiotherapy devoted to the management of the aging population; what will happen to that? How will palliative care change?
  6. Augmented reality. When we can overlay digital information onto our visual field, what possibilities exist for effective patient management? For education? What happens when that information is integrated with location-based data, so that patient-specific information is presented to us when we are near that patient?
  7. Virtual reality. What will it mean for training when we can build entire hospitals and patient interactions in the virtual world? When we can introduce students to the ICU in their first year? This could be especially useful when we have challenges with finding enough placements for students who need to do clinical rotations.
  8. 3D printing. What happens when we can print any equipment that we need, that is made exactly to the patient’s specifications? How will this affect the cost of equipment distribution to patients? Can 3D printed crutches be recycled? Reused by other patients? What new kinds of equipment can be invented when we are not constrained by the production lines of the companies who traditionally make the tools we use?
  9. Brain-computer interfaces. When patients are able to control computers (and by extension, everything linked to the computer) simply by thinking about it, what does that mean for their roles in the world? What does it mean when someone with a C7 complete spinal cord injury can still be a productive member of society? What does it mean for community re-integration? How will “rehabilitation” change if computer science is a requirement to even understand the tools our patients use?
  10. Quantified self. As we begin to use sensors close to our bodies (inside our phones, watches, etc.) and soon – inside our bodies – we will have access to an unprecedented amount of personal (very personal) data about ourselves. We will be able to use that data to inform decision making about our health and well-being, which will change the patient-therapist relationship. This will most likely have the effect of modifying the power differential between patients and clinicians. How will we deal with that? Are we training students to know what to do with that patient information? To understand how these sensors work?
  11. Processing power. While this is actually something that is linked to every other item in the list, it might warrant it’s own topic purely because everything else depends on the continuous improvements in processing power and parallel reduction in cost.
  12. The internet. I’m not sure about this. While the architecture of the internet itself is unlikely to change much in the next few decades (disregarding the idea that the internet as we know it might be supplanted with something better), who has access to it and how we use it will most certainly change.

An artist's depiction of a nanobot that is smaller than blood cells.
Nanobot smaller than blood cells.
I should state that we will be working under certain assumptions:

  • That the technology will not be uniformly integrated into society and health systems i.e. that wealth disparity or income inequality will directly affect implementation of certain therapies. This will,obviously have ethical and moral implications.
  • That the technology will not be freely available i.e. that corporations will license certain genetic therapies and withhold their use on those who cannot pay the license.
  • That technological progression will continue over time i.e. that regulations will not prevent, for example, further research into stem cell therapy.
  • …we may have to make additional assumptions as we move forward but this is all I can think of now

We’ll probably find that there will be significant overlap in the above topics, since some are specific technologies that will have an influence on other areas. For example, gene therapy and nanotechnology may have an impact on aging; artificial intelligence will impact many areas, as will robotics and computing power. The idea isn’t that these topics are discrete and separate, but that they provide a focus point for discussion and exploration, with the understanding that overlap is inevitable. In fact, overlap is preferable, since it will help us explore relationships between the different areas and to find connections that we maybe were not previously aware of.

Giving patients bad news in virtual spaces where we can control the interaction.
Giving patients bad news in virtual spaces where we can control the interaction.
The activities that the students engage in during this project are informed by the following ideas, which overlap with each other:

  • Authentic learning is a framework for designing learning tasks that lead to deeper engagement by students. Authentic tasks should be complex, collaborative, ill-defined, and completed over long periods.
  • Inquiry-based learning suggests that students should identify challenging questions that are aimed at addressing gaps in their understanding of complex problems. The research that they conduct is a process they go through in order to achieve outcomes, rather than being an end in itself.
  • Project-based learning is the idea that we can use full projects – based in the real world – to discuss and explore the disciplinary content, while simultaneously developing important skills that are necessary for learning in the 21st century.

I should be clear that I’m not really sure what the outcome of this project will be. I obviously have objectives for my students’ learning that relate to the Professional Ethics module but in terms of what we cover, how we cover it, what the final “product” is…these are all still quite fluid. I suppose that, ideally, I would like for us as a group (myself and the students) to explore the various concepts together and to come up with a set of suggestions that might help to guide physiotherapy education (or at least, physiotherapy education as practiced by me) over the next 5-10 years.

Augmented reality has significant potential for education.
Augmented reality has significant potential for education.
So much of physiotherapy practice – and therefore, physiotherapy education – is premised on the idea that what has been important over the last 50 years will continue to be important for the next 50. However, as technology progresses and we see incredible advances in the integration of technology into medicine and health systems, we need to ask if the next 50 years are going to look anything like the last 50. In fact, it almost seems as if the most important skill we can teach our students is how to adapt to a constantly changing world. If this is true, then we may need to radically change what we prioritise in the curriculum, as well as how we teach students to learn. When every fact is instantly available, when algorithms influence clinical decision-making, when amputees are fitted with robotic prosthetics controlled directly via brain-computer interfaces…where does that leave the physiotherapist? This project is a first step (for me) towards at least beginning to think about these kinds of questions.

 

Physiotherapy education for the 21st century

Note: This article was first posted on the Critical Physiotherapy Network. Thanks to CPN for permission to cross-post here.

The beginning of the 21st century has seen more technological advances than any other time in our history, at an accelerating rate of change. At the time of writing, we are seeing the introduction of robotics, gene therapy and nanotechnology into larger and larger aspects of health care which, when combined with advances in computing power that will soon exceed the processing power of the human brain, we seem poised on the brink of a shift in our understanding of what it means to be human. In addition to the obvious influence of information and communication technology on social structures, we are also experiencing a shift from vertical communication structures that privilege hierarchies of control, to horizontal structures – like networks – that embody coordination, cooperation and collaboration (Bleakley, Bligh, Browne & Brice Browne. 2011). Regardless of what we call this cultural shift in perspective (postmodernity, late modernity, and posthumanism are all used somewhat interchangably), what matters is how we orient ourselves to the future (ibid.).

Given the scope of these changes and their inevitable impact on health professions education – embedded as it is within a broader socio-cultural context – we should expect to see a significant shift in how physiotherapists are prepared for practice. Yet, physiotherapy education continues to follow traditional lines of thinking and implementation, based in a historical model that not only ignores our understanding of how people learn but also fails to consider the changing needs of the communities we serve (Frenk et al., 2010). In addition, the focus on increasing the intake of health professional programmes that aims to graduate more therapists is insufficient to create a health workforce who are equipped with the appropriate competencies to respond to its evolving needs (WHO, 2013). The education of health professionals is often isolated from service delivery needs and does not adapt to the rapidly changing profile of the population. For example, an excessive focus on hospital-based education, together with education that segregates students into professional silos does not prepare them to work effectively in teams, nor to develop the leadership skills required in 21st century health services (ibid.).

Instead, physiotherapy educators must seek to adapt their curricula in order to produce professionals who have the capacity to identify and adjust to new environments in a continuous process of learning and adapting their competencies (WHO, 2013). As society and the health systems within it become increasingly complex and the needs of populations change accordingly, it seems appropriate to ask if our current education system is capable of preparing physiotherapy graduates to not only work in such environments, but to thrive.

In order to graduate young professionals who are capable of adapting to dynamic and complex systems, we cannot afford to continue learning in spaces that have not changed in 500 years. There is little evidence that physiotherapy educators have acknowledged society’s changing conceptions of therapy and health, nor that they have adapted their teaching practices accordingly. In order to bring about transformative learning experiences for health professions students, Frenk et al., (2010) suggest three fundamental shifts:

  1. from fact memorisation to searching, analysis, and synthesis of information for decision making
  2. from seeking professional credentials to achieving core competencies for effective teamwork in health systems
  3. from non-critical adoption of educational models to creative adaptation of global resources to address local priorities

We need to ask ourselves what attributes health care professionals require in order for them to effectively negotiate the challenges of future working environments and whether or not our current teaching and learning spaces help students become capable, effective leaders in complex health systems? As we develop a more nuanced vision of what it means to be human in an increasingly complex world, we must ask critical questions that challenge the profession to think differently about what it means to be a physiotherapist and consequently, how physiotherapy education needs to change.

References

  • Bleakley, A., Bligh, J., Browne, J., & Brice Browne, J. (2011). Medical Education for the Future: Identity, Power and Location. Springer.
  • Frenk, J., Chen, L., Bhutta, Z. A., Cohen, J., Crisp, N., Evans, T., … Zurayk, H. (2010). Health professionals for a new century: transforming education to strengthen health systems in an interdependent world. Lancet, 376(9756), 1923–58.
  • Gordijn, B., & Chadwick, R. (2008). Medical enhancement and posthumanity. Springer.
  • World Health Organization (2013). Transforming and scaling up health professionals’ education and training.

Proposal abstract: Training in the ICU for physiotherapy students with a visual impairment (a case study)

Abstract for a project proposal that I submitted for ethics review earlier this week. If it gets approved we’ll begin data collection on our first visually impaired undergraduate student placement in the intensive care unit.

The Department of Physiotherapy at the University of the Western Cape (UWC) began accepting students with visual impairments (VI) into the undergraduate programme in 1996. To date, eight students with visual impairments have graduated with degrees in physiotherapy, all of whom have gone on to successful employment in the health system. In this area, the department has played an important role in leading transformative change, not only in the broader context of higher education but specifically in the area of providing equal opportunities for professional training for all South Africans.

While the department has done well to provide equal opportunities to students with VI in the general undergraduate course, we have yet to place a student with VI into the ICU setting as part of their clinical rotations. Early in 2015 however, the department engaged in a series of discussions with one of our final year students with VI, as well as clinicians and lecturers and decided to explore the possibility of placing the student into the ICU. This would enable us to align ourselves with national policies and priorities. As part of this process of placing a student in the ICU setting we want to describe the facilitators and barriers that exist, as seen from the perspective of those involved in the process. The aim of the study is therefore to explore the experiences of the student, clinicians, academics and peers, with the placement of a student with a VI in the ICU.

How do you negotiate this environment when you can’t see very well?

The project will make use of a case study design that aims to describe the process of placing an undergraduate physiotherapy student with VI in an ICU setting as part of a clinical practice rotation. The case study will include data gathered from the student’s reflective clinical diary as well as in-depth interviews with the student, clinical supervisor, VI and clinical coordinators in the physiotherapy department at UWC, and the clinician who is responsible for overseeing the student in the ICU. Peers who have engaged with the student during the specific clinical placement will also be included, and will be identified during the process.

The interviews will be audio recorded and then sent away for transcription. The transcribed interviews will be anonymised and thematically analysed in order to determine themes related to barriers and facilitators that are relevant to the student’s learning. The transcripts – along with the analyses – will be shared with participants in order to ensure that the themes that emerged are consistent with the meaning that they had intended during the interviews.

Proposal abstract: Case-based learning in undergraduate physiotherapy education

Abstract for a project I submitted earlier this week for ethics clearance. During 2012 – 2014 we converted one of our modules that runs in the 2nd, 3rd and 4th year levels from a lecture-based format to a case-based learning format. We are now hoping to have a closer look at whether or not the CBL approach led to any changes in teaching and learning behaviours in staff and students.

Case-based learning (CBL) is a teaching method that makes use of clinical narratives to create an authentic learning activity in which students navigate their way through complex patient scenarios. The use of CBL in a health professions undergraduate curriculum attempts to convey a multidimensional representation of the context, participants and reality of a clinical situation, allowing students to explore these concepts in the classroom. While the implementation of CBL has a sound theoretical basis, as well as a strong evidence base for use in health professions education, there are challenges in its effective use that are not easily resolved. However, if it can be shown that the approach leads to changes in teaching and learning practice, which enhance student learning, providing additional resources to resolve the challenges can be more strongly justified. This project therefore aims to determine staff members’ and students’ perceptions of CBL as a teaching method, and to find out how it influenced their teaching and learning behaviours.

This study will make use of a mixed method research design in which the experiences and perceptions of student and staff members are used to determine whether or not there was a change in their teaching and learning practice. Qualitative and quantitative data will be gathered using a survey of all students in the population, focus group discussions of students and in-depth interviews of all staff in the department. The survey will determine if the design of the CBL approach led to a change in what the students did. The focus group discussions will gather data on the nature of the changes and the underlying rationale for those changes. The interviews with lecturers will be conducted in order to delve more deeply into whether or not lecturers’ teaching behaviours changed, and again, to explore the underlying rationale of those changes.

The survey will make use of a self-developed questionnaire that will gather quantitative data using Likert scales and other closed-ended questions. The survey will be sent to all 3rd and 4th year students in the 2015 academic year. The same students will be invited to participate in the focus groups, and the researchers will make use of purposive sampling to allocate volunteers into two focus groups in each year level. All lecturers in the department (n=10) will be invited to participate in the in-depth interviews, including those who were not directly involved in the implementation of CBL. In addition, we will also invite ex-staff members who were involved in the process, as well as postgraduate students who assisted with student facilitation.

Qualitative data will be gathered during the focus groups and interviews. This data will be interpreted via the theoretical frameworks used in the design of the CBL cases. The focus group discussions and interviews will be conducted in English and recorded using a digital audio recorder. The audio files will be sent for verbatim transcription and the anonymised, transcribed documents will then be sent to participants for verification. The transcripts will be analysed thematically, coding the data into categories of emerging themes. Trustworthiness of the analysis will be determined through member checking and peer debriefing and participants will be given the opportunity to comment on whether or not the data was interpreted according to what they meant. The transcribed verbatim draft will be given to colleagues who were not involved in the study for comment.

Design principles for clinical reasoning

graphic_design smallerClinical reasoning is hard to do, and even harder to facilitate in novice practitioners who lack the experience and patterns of thinking that enable them to establish conceptual relationships that are often non-trivial. Experienced clinicians have developed, over many years and many patients, a set of thinking patterns that influence the clinical decisions they make, and which they are often unaware of. The development of tacit knowledge and its application in the clinical context is largely done unconsciously, which is why experienced clinicians often feel like they “just know” what to do.

Developing clinical reasoning is included as part of clinical education, yet it is usually implicit. Students are expected to “do” clinical reasoning, yet we find it difficult to explain just what we mean by that. How do you model a way of thinking?

One of the starting points is to ask what we mean when we talk about clinical education. Traditionally, clinical education describes the teaching and learning experiences that happen in a clinical context, maybe a hospital, outpatient or clinic setting. However, if we redefine “clinical education” to mean activities that stimulate the patterns of thinking needed to think and behave in the real world, then “clinical education” is something that can happen anywhere, at any time.

My PhD was about exploring the possibilities for change that are made available through the integration of technology into clinical education. The main outcome of the project was the development of a set of draft design principles that emerged through a series of research projects that included students, clinicians and clinical educators. These principles can be used to design online and physical learning spaces that create opportunities for students to develop critical thinking as part of clinical reasoning. Each top-level principle is associated with a number of “facets” that further describe the application of the principles.

Here are the draft design principles (note that the supporting evidence and additional discussion are not included here):

1. Facilitate interaction through enhanced communication

  • Interaction can be between people and content
  • Communication is iterative and aims to improve understanding through structured dialogue that is conducted over time
  • Digital content is not inert, and can transform interactions by responding and changing over time
  • Content is a framework around which a process of interaction can take place – it is a means to an end, not an end in itself
  • When content is distributed over networks, the “learning environment” becomes all possible spaces where learning can happen
  • Interaction is possible in a range of contexts, and not exclusively during scheduled times

2. Require articulation

  • Articulation gives form and substance to abstract ideas, thereby exposing understanding
  • Articulation is about committing to a statement based on personal experience, that is supported by evidence
  • Articulation is public, making students accountable for what they believe
  • Articulation allows students’ thinking to be challenged or reinforced
  • Incomplete understanding is not a point of failure, but a normal part of moving towards understanding

3. Build relationships

  • Knowledge can be developed through the interaction between people, content and objects, through networks
  • Relationships can be built around collaborative activity where the responsibility for learning is shared
  • Facilitators are part of the process, and students are partners in teaching and learning
  • Facilitators are not gatekeepers – they are locksmiths
  • Create a safe space where “not knowing” is as important as “knowing”
  • Teaching and learning is a dynamic, symbiotic relationship between people
  • Building relationships takes into account both personal and professional development
  • Building relationships means balancing out power so that students also have a say in when and how learning happens

4. Embrace complexity

  • Develop learning spaces that are more, not less, complex
  • Change variables within the learning space, to replicate the dynamic context of the real world
  • Create problems that have poorly defined boundaries and which defy simple solutions

5. Encourage creativity

  • Students must identify gaps in their own understanding, and engage in a process of knowledge creation to fill those gaps
  • These products of learning are created through an iterative activity that includes interaction through discussion and feedback
  • Learning materials created should be shared with others throughout the process, to enable interaction around both process and product
  • Processes of content development should be structured according to the ability of the students

6. Stimulate reflection

  • Learning activities should have reflection built in
  • Completing the reflection should have a real consequence for the student
  • Reflection should be modelled for students
  • Reflections should be shared with others
  • Feedback on reflections should be provided as soon after the experience as possible
  • Students need to determine the value of reflection for themselves, it cannot be told to them

7. Acknowledge emotion

  • Create a safe, non­judgemental space for students to share their personal experiences and thoughts, as well as their emotional responses to those experiences
  • Facilitators should validate students’ emotional responses
  • These shared experiences can inform valuable teaching moments
  • Facilitators are encouraged to share personal values and their own emotional responses to clinical encounters, normalising and scaffolding the process
  • Sensitive topics should be covered in face­to­face sessions
  • Facilitators’ emotional responses to teaching and learning should be acknowledged, as well their emotional responses to the clinical context

8. Flexibility

  • The learning environment should be flexible enough to adapt to the changing needs of students, but structured enough to scaffold their progress
  • The components of the curriculum (i.e. the teaching strategies, assessment tasks and content) should be flexible and should change when necessary
  • Facilitators should be flexible, changing schedules and approaches to better serve students’ learning

9. Immersion

  • Tasks and activities should be “cognitively real”, enabling students to immerse themselves to the extent that they think and behave as they would be expected to in the real world
  • Tasks and activities should use the “tools” of the profession to expose students to the culture of the profession
  • Technology should be transparent, adding to, and not distracting from the immersive experience

We have implemented these draft design principles as part of a blended module that made significant use of technology to fundamentally change teaching and learning practices in our physiotherapy department. We’re currently seeing very positive changes in students’ learning behaviours, and clinical reasoning while on placements, although the real benefits of this approach will only really emerge in the next year or so. I will continue to update these principles as I continue my research.

Note: The thesis is still under examination, and these design principles are still very much in draft. They have not been tested in any context other than in our department and will be undergoing refinement as I continue doing postdoctoral work in this area.