At a recent meeting of fellow surgeons in my department, an interesting difference of opinion arose. It relates to our trainees’ knowledge of statistics. Unfortunately, the meeting did not allow any time to properly discuss the topic.
Some background to illuminate your way. Registration as a medical specialist in South Africa is regulated by the Health Professions Council. In recent years, the Council has introduced the completion of a mandatory research project, culminating in a dissertation. This accompanies the usual prescribed formal examinations.
Universities in the country manage the research projects by way of a Master’s degree, for which all trainees must register.
The difference of opinion was simple. From the opposite corner of the ring, it was suggested that our trainees require no knowledge of statistical analysis and should hand in their data to a statistician and merely use the results in their reports.
I do not share this opinion and feel strongly that all medical professionals should have an understanding of the topic. While not all doctors and specialists are interested in research, I do believe that an understanding of statistics empowers the individual when evaluating published research. This in turns helps to inform and change their practice. As a surgeon, I know it does mine. With no formal program for statistical teaching in our department, I looked towards open education.
To this end, I was a leading proponent in getting the University of Cape Town to sign up with the Coursera and FutureLearn massive open online course platforms. The creation of twelve courses were funded by the Vice Chancellor and my course on Understanding Medical Research was the first to launch on Coursera. It has been a phenomenal experience and the feedback has been tremendous.
Unfortunately, austerity measures have curtailed these efforts. I funded my second course on Coursera through an external loan. It is on the use of Julia (mathematical biology using scientific computing) and was created in collaboration with the Applied Mathematics Department. The honors section of the course is on data management and statistical analysis.
To further my resolve in teaching medical statistics, I have taken to the Udemy platform with a course on medical statistics using Mathematica. In the last few days I have also launched a course on the use of SPSS in healthcare and life science statistics. Udemy is an interesting platform and I would encourage its use.
Link to the course: SPSS for healthcare and life science statistics
Course on SPSS for medical statistics
My opinion, though, is clear. Learning to analyze data, is an empowering skill for everyone in healthcare.
Understanding statistical analysis and interpreting the results of research papers are just as important as the ability to correctly diagnose the cause of acute abdominal pain.
Medical knowledge is expanding at a rapid pace. This is evident by the number of research papers being published every year. Although medical students and residents attend a formal education program, it is journal papers that serve as masters of education for the majority of a professional’s life.
The ability to understand the results section of a paper is crucial in deciding to change clinical practice. In order to do this effectively, knowledge of statistics is vital.
Yet, formal training is statistics takes a back seat when it comes to anatomy, physiology, and, clinical teaching. When statitics is part of the curriculum, it is often positioned as less important. It gets even worse when taught with mathematical emphasis. Whilst it may be rigorous to teach using equations, a subset of medical students are lost in this effort.
No medical school can look the other way. Data analysis and computational thinking is part of the future of healthcare. I was reminded of this when I came across this article again, after reading it almost two years ago: NYU medical students learning to analyze big data.
Our efforts at University of Cape Town are growing too. The massive open online course: Understanding clinical research on the Coursera platform, has now had more than 23,000 participants. In the division of General Surgery, I teach the use of data analysis and computational thinking to great effect, using IBM SPSS, Python, Julia, and Mathematica.
It’s time data science and statistical analysis to take its rightful place in medical school curricula.
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, I’ve started a new playlist on my YouTube® channel called The Julia Computer Language. For now, lessons 1 and 2 are up and as (limited) time allows, I’ll add some more.
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.