SAAHE podcast on building a career in HPE

In addition to the In Beta podcast that I host with Ben Ellis (@bendotellis), I’m also involved with a podcast series on health professions education with the South African Association of Health Educators (SAAHE). I’ve just published a conversation with Vanessa Burch, one of the leading South African scholars in this area.

You can listen to this conversation (and earlier ones) by searching for “SAAHE” in your podcast app, subscribing and then downloading the episode. Alternatively, listen online at http://saahe.org.za/2019/06/8-building-a-career-in-hpe-with-vanessa-burch/.

In this wide-ranging conversation, Vanessa and I discuss her 25 years in health professions education and research. We look at the changes that have taken place in the domain over the past 5-10 years and how this has impacted the opportunities available for South African health professions educators in the early stages of their careers. We talk about developing the confidence to approach people you may want to work with, from the days when you had to be physically present at a conference workshop, to explore novel ways to connect with colleagues in a networked world. We discuss Vanessa’s role in establishing the Southern African FAIMER Regional Institute (SAFRI), as well as the African Journal of Health Professions Education (AJHPE) and what we might consider when presented with opportunities to drive change in the profession.

Vanessa has a National Excellence in Teaching and Learning Award from the Council of Higher Education and the Higher Education Learning and Teaching Association of South Africa (HELTASA), and holds a Teaching at University (TAU) fellowship from the Council for Higher Education of South Africa. She is a Deputy Editor at the journal Medical Education, and Associate Editor of Advances in Health Sciences Education. Vanessa was Professor and Chair of Clinical Medicine at the University of Cape Town from 2008-2018in health and is currently Honorary Professor of Medicine at UCT. She works as an educational consultant to the Colleges of Medicine of South Africa.

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.

SAAHE 2016 conference

I usually post my notes after a conference but this year at SAAHE I mainly used Twitter to keep track of my thoughts during the sessions, which was great because we probably saw more activity on Twitter in PE than ever before. Here is the conference feed using the #saahe2016 hashtag.

Note : While it’s great that Twitter gives you the ability to embed a conference feed in a post like this, I always wonder what will happen when Twitter goes away?


Design principles for blended learning environments: a presentation

I’m going to be at the 2015 SAAHE conference for the next couple of days, which is being held in association with The Network: Towards Unity for Health. Yesterday I gave a workshop on Setting up and running an open online course, as well as a presentation on developing Design principles for blended learning environments. These principles are the outcomes of my PhD project, as well as further studies that I’ve done in the area. Here are the slides for the presentation.

Starting new projects and catching up with old ones

It’s been a long time since I’ve updated my blog, for a few very good reasons. The first and most important is that in the middle of last year my daughter was born. I took time out from as many non-essential work-related activities as possible so that I could spend time with her whenever I could.

During this same period of time I also developed and ran an open online course on Professional Ethics in collaboration with Physiopedia as part of a sabbatical project I was working on. While I blogged extensively as part of the course, it meant that I had no time to write about other things I found interesting.

At about the same time, I agreed to chair the organising committee of the 2014 SAAHE conference, which was recently held in Cape Town. The conference organisation and sabbatical research project, together with my normal workload and commitment to family time meant that I had to take a step back from blogging.

However, now that the conference and research project is over and our family have settled into a more structured routine, I’m finding that I have a little more time to start blogging again. I thought that I’d get back into the swing of things by saying a little bit about the main projects that I anticipate working on during the next few months.

The first is my Clinical Teacher mobile app. It’s been ages since I’ve added any new content and I’m feeling really guilty about that, especially since interest in the project seems to be growing. I’ve slowly been adding bits and pieces to a few articles that I wanted to write but never had the time to finalise any of them. Over the next few months I’m hoping to finish 2 or 3 articles and get them published into the app. I’m also going to work on a visual refresh for the app. I’ve been really impressed with the material design principles highlighted in the the developer preview of Android “L”. The flat design and use of colour and depth, together with new ideas about fonts and how they display on many different screen sizes, has got me thinking differently about the app.

layout-principles-responsive-responsive-01_large_mdpi

The change won’t be anything drastic but I do want to give the app a more modern look and feel, and remove the faux leather covers and gradients. I also want to come up with a consistent image theme for article headers. The more recent articles have had an “animal” theme, where I try to find an image of an animal that somehow speaks to the topic (even if the link is only in my mind). However, there have been times when I’ve ignored that trend and just used something clearly related. I haven’t yet decided what to do but am clear that it will be a design decision that will be consistently applied moving forward. Finally, I want to experiment with the new features that Snapplify have been building into the platform, including publishing video and audio, annotations, and text highlighting.

I mentioned earlier in the post that in 2013 I ran an open online course on ethics, and would now like to build on that work. I’ve submitted a funding proposal to support the next phase of the project, which is to offer the course in a variety of countries and educational contexts, and across a range of professional disciplines. We learned an enormous amount during the 2013 experience and we want to build on those lessons by doing something that really challenges how we think about physiotherapy education in an international context. I’m definitely going to work with Physiopedia again, since we had a really great experience during the first course and their input was invaluable. I’ll post more about that project once I’ve found out about the funding outcome and ethics approval.

Finally, and on a somewhat related note, we’re going to be developing a few courses within our department, which we will offer to our clinical supervisors and clinicians at the placements where our students work. They will most likely be a blend of online and physical components, and be relatively short in duration (ranging from a few hours to 2-4 weeks). Our supervisors have identified several areas where they would like additional input that they feel will help them to better support our students. For example, assessment and feedback are two areas that could be improved. So, we’ll be exploring different ways to support our clinicians and supervisors over the next few months.

In addition to these projects, I’m also going back to Brazil in November to attend The Network: Towards Unity for Health conference. The main reason for attending is to try and establish partnerships with colleagues from other institutions, who might like to be involved in the international ethics project that I mentioned earlier. There are many parallels and similarities between Brazil and South Africa, and I’d like to develop stronger links between my own institution and others over there because there’s a lot we can learn from each other.

So that’s it. My tentative plans for the rest of 2014.

My presentation at the 2014 SAAHE conference

Here is the presentation I plan on giving at the SAAHE conference tomorrow. It describes an open online course that I ran in collaboration with Physiopedia last year, and now presents some of the results obtained from student interviews.

2014 SAAHE conference in Cape Town

One of the reasons that I’ve been quite on this blog lately is that I’m working on the SAAHE 2014 organising committee, and we’re starting to gear up for the conference in a little over a month. I thought I’d write a little progress update, just in case you’re considering registering but hadn’t made up your mind.

The SAAHE conference is perhaps the largest annual gathering of health professions educators on the continent, represented by academics, researchers and clinicians from many of the higher education institutions in the country. It’s a wonderful opportunity to share insights, research findings and experiences with colleagues who are passionate about teaching and learning in the context of health care.

We are really excited that, for the first time in SAAHE’s history, we are having a South African keynote speaker along with our international speakers. Steve Reid joins Debbie Murdoch-Eaton and Jason Frank, along with the winner of the SAAHE Distinguished Educator award (still be announced), in the lineup of keynote presenters.

This year the conference will be held off campus at the DoubleTree Hotel, due to planned rennovations that would make hosting on site logistically complicated. The hotel is a great venue, conveniently located near the city and also able to provide accomodation to out of town delegates.

Entrance lobby at the DoubleTree Hotel.
Entrance lobby at the DoubleTree Hotel.

If you are interested in attending the SAAHE conference, please visit our information page, or contact the conference manager, Debbie Rorich, for additional details. We look forward to seeing you at the 2014 SAAHE conference in Cape Town.

Twitter Weekly Updates for 2012-06-25

  • RT @rikusmellet: RT @PaulaBarAsh: Dr McMillan: 92% SA schools don’t have functioning libraries. #SAAHE #
  • RT @paulabarash: Dr Evans: replies= visit radiography, make friends with Nurses they know a lot, go to Paeds -kids always want to have fun #
  • RT @paulabarash: Dr Evans: asked students what the learning opportunities on the ward were when u unsupervised #SAAHE12 #
  • RT @paulabarash: Dr Brown: 4 domains for Recontextualisation: content, pedagogic, workplace & learner #SAAHE12 #
  • Bloom reheated http://t.co/aRSfg90L #
  • @carinavr Thanks Carina 🙂 #
  • SAAHE conference started this morning. No one tweeting (yet). Was hoping to keep up with the presentations. Sad face… #saahe12 #