Should we teach computational thinking and big data principles to medical students?

Abstract

The sources, nature, and scale of data are changing, and computing is deeply affecting the very nature of research from exploration, hypothesis creation, and literature review to collection, generation, and analysis of data, as well as reporting of results. In many fields no large-scale experiments are funded unless computer simulation shows high likelihood for results to turn out as expected. What is more, sensor technology and communication technology are making information technology ubiquitous, embedded in all sectors of life. As health care practitioners too have become generators of data, users of data-driven decisions, and researchers using data, there is a need to re-think our curricula. Medical curricula should embrace the fundamentals of big data, the info-computational paradigm of science, modern inferential techniques, and algorithmic thinking principles. By understanding such principles, healthcare practitioners will have improved skills for problem solving in computerized research, they will better understand the insights generated by the devices they use, will be able to design better research, and most importantly will deliver a better service to patients. That understanding is also important from the perspective of bottom-up innovation: The better healthcare practitioners understand the potential and caveats of computational thinking and big data, the better they are able to suggest new and improved ways of collecting data and dealing with it.

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