Teaching statistics and data science in medical school
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.