Just to show off what Jupyter notebooks can do, this post will render part 1 of lesson 1 of my lecture series on complex variables. Have a look.
After many months of preparation, my massive open online course (MOOC) on healthcare statistics has gone live on Coursera today, December 01, 2015. To sign up follow this link: Coursera.
This course build an intuitive understanding of statistics, without the use of complicated mathematical equations. Everything from descriptive statistics to hypothesis testing, confidence intervals, p-values, Student’s t-test, chi-square tests and many more are explained.
On completion of this course you should feel confident in properly evaluating the published literature or even embark on your own research.
So, how can an academic surgical unit benefit from the computer code development skills of people such as Wes McKinney of pandas fame or the educational skills of an engineering professor such as Lorena Barba of Numerical MOOC (numerical massive open online course) fame? Answer: A lot. This post is about our efforts to transition from antiquated to more modern forms of surgical training and assessment, all with the help of the one of the best software projects out there, Project Jupyter. This is Groote Schuur after all!
The teaching and assessment paradigm has stood for many, many decades. Do four years of surgical rotations, watch what your superiors do, present on ward rounds, go to the clinic, take calls, assist in theatre, do some cases, attend (most) academic meetings (read: watch yet another PowerPoint presentation), pass three exams. Presto. Specialist. That’s how its done now, that how is was done in the 00’s, the 1990’s, 80’s, 70’s, 60’s, 50’s, 40’s,… You get the point. Hey, depending on which source you read, it was in the the 40’s that the overhead projector was first used by the military in World War II. If you think about it, an overhead transparency projector is just PowerPoint without a computer. If you slipped in one transparency while the other is still showing, it;s just like a slide transition!
Depending on your working environment, you might be surrounded by people in full support of this form of education. It has always worked that way. Why change now? Well, as the argument goes, by that logic bloodletting should still be all the rage. You will note that in contrast to medical education, actual medicine has come on in leaps and bounds. We buy into the new paradigm that is evidence-based medicine. So why is it so difficult to accept and, even more difficult, to practice evidence based medical education?
Some of us are fortunate enough to work in countries where there are national efforts and frameworks in place to motivate for change. Have a look at the CanMEDS program in Canada. Two of the key concepts in their program are patient-centred care and competency-based assessment. Without going into the detail of their programs, I want to concentrate on these two aspects. Reason being, it gives us a practical starting point. For those unfortunate enough not to work in countries with national frameworks and support, small steps have to be taken.
So what solutions have we implemented in the Acute Care Surgery Unit at Groote Schuur Hospital? First and foremost, involve the patients. They are at the centre of what we do after all. Why should they have no say in the evaluation of their care? Fortunately, validated tools are available when you turn to the literature. At this time we use the Jefferson scale of patient’s perception of physician empathy. Moving on to competency assessment, there is the Ward Round Assessment tool amongst many others. Point being, we are moving away from the 20-second, mark either average or above average on the end-of-rotation subjective question scorecard. You know the one: (1) Knowledge, (2) Surgical skill, (3) Punctuality…
Now, the Acute Care Surgery Unit is brand new (you can learn more about us from my talk at this year’s Association of Surgeons of South Africa conference here). We certainly have no research assistants, money, or personnel to help us in our efforts towards patient-centred, competency-based education. This whole process has to be self-driven. Solutions to the problem? Well, that’s the easy bit. The World has changed over the last few years. No longer is knowledge locked away behind expensive paywalls. If you want to learn something, go online. For me, it all started with the Massachusetts Institute of Technology (MIT). Their open courseware opened a whole new world to me. MIT and the massive open online course platforms such as Coursera (to which I will shortly contribute), EdX and FutureLearn (to name but a few) are handing the keys of knowledge to all humankind.
This brings me to Project Jupyter and computer languages such as IPython and Julia. If you have no access to software development teams and big budget research units, do yourself a favor, search for tutorials on these projects. You will find so many wonderful men and women, going out of their way to empower you with these tools. Even a lowly surgeon such as myself have online tutorials. Have a look at these:
The Klopper Lectures on Julia
Mini project: Medical research using Julia
Back to what this post is all about. Here, you will find a link to some of our results using Project Jupiter (Github). To protect patients and trainees, the data have been altered and are not a true reflection of anyone or any given period. What it does show, though, is how easy it is to use data to properly guide the training of our residents; and this is just our first small step.
Julia is a rather new programming language for technical or scientific computing. You will find out a lot more about it on the Julia homepage. Unfortunately, there is not a lot of tutorials on Julia out there and if you do find them, most are by computer scientist for computer scientists. Perhaps rightly so, as Julia is a fantastic tool, capable of some pretty impressive things when it comes to scientific computing. It prides itself on being as simple and easy to use as Python, with speeds approaching that of C or Fortran. It is indeed much speedier than other mathematical languages such as Matlab® and Mathematica®.
On top of this, I believe that it makes for an excellent language for a novice starting off, learning how to code. This is especially true for those who plan to go into the fields of science and technology. Even if you move on to other languages, Julia will stand you in good stead. It might spoil you, though, which means you’ll come running straight back to it.
I do stick to IPython for my medical statistics, but Julia works perfectly here too. I’ve made a lecture on the topic, which you can view here.
Go on, give Julia a spin. There is just something about it that speaks to me. A certain elegance and power. Well done to the brilliant minds that came up with it and to all those who are continuing its development.
You can write Julia code in the cloud using JuliaBox, so no need to install anything at all. At this time, I am having tremendous problems getting it (IJulia) to run in Jupyter, so much so that I am using the very nice Juno development environment. In upcoming lessons I will look at installing Julia, Jupyter, and Juno, but for now, you can follow along without any downloads or installs. Just use JuliaBox and your Google® account to sign in. The notebook files that I use are in a zip file on this page.
A brief report was publish in the Canadian Medical Education Journal titled Re-thinking clinical research training in residency. The authors were struggling with the same questions we have in our department. Perhaps the two most important points relate to the need for specialists to critically appraise research and to fulfil accreditation requirements.
In medicine we have well and truly departed from the era of eminence-based medicine. It is of utmost importance for specialist to be able to evaluate research evidence to inform their practice. This requirement extends well beyond simply browsing the introduction and conclusion sections in abstracts.
Furthermore, it has become necessary for postgraduate trainees in South Africa to complete a mini-dissertation towards a Masters degree in order to qualify to sit the final Colleges of Medicine exams.
The authors then asks three questions. Firstly, is mandating original research the answer? Secondly, what ought to be the central purpose of research training? Lastly, what are the alternatives to original clinical research? They quite correctly point out that there is much more to the development of a clinician-scientist than research training and bring up the necessity to focus trainee research on local patient needs as opposed the emphasis on conducting original research.
The main section of the paper attempts to answer the three question mentioned above. I’ll leave you to read the authors’ response to their first question, most of the suggested programs in aid of producing clinician-scientists are not available in this country.
On the question of the central purpose of research training, the authors focus on the (in my opinion) commendable CANMEDS initiative of placing the patient at the centre of medical education. It might be true that there exists tremendous personal fulfilment in a career in medicine, but by its nature, it is a pursuit aimed at helping patients and not a pursuit of personal gain. As in the South African academic setting, education takes place in institutions that are publicly funded and the authors express the opinion that time, effort, and resources in research education be spent on producing work aimed squarely at direct benefit to the local patient population, as opposed to original research.
As to the alternatives to original clinical research the authors once again explore pathways which they feel might benefit the patient more. They argue for the formation of teams by PhD-trained researches and feel that trainees are in a much better position to come up with relevant clinical questions which should lead to projects managed by these teams. They feel that trainees could learn much more about research in such groups.
Lastly, they raise the important issue of time available for research during training. Their situation certainly mimics our constrained environment, where it is almost impossible to release trainees for sustained periods during which they do not provide service delivery.
Certainly some food for though. Alas, it is my humble opinion that the Canadian Medical Education System, through CANMEDS, far exceeds our local effort. At this time, our dire need lies in establishing proper education in conducting research and statistical analysis. No formal education exists in this regard.