Why Publish a Textbook on Biomedical Data Science?

Updated: Dec 22, 2019

I thought when the seventh edition of my Health Informatics: Practical Guide textbook came out in June 2018 that I was probably finished with publishing textbooks, given my age and the amount of time required to put together a new book.


The problem is that I have had a nagging concern for the past roughly 3 years that there is not enough written about data science that is tailored to the healthcare sector. For example, I was unable to find a pragmatic and current textbook for the audience I am most concerned about - clinicians and informatics professionals.


Furthermore, in spite of several informatics experts equating data science and informatics, the available evidence suggests otherwise. Not only is there a paucity of data science subjects taught at the graduate Health Informatics level, industry's job requirements for healthcare data science are very different. This was reinforced by an article this past spring by Meyer in JAMIA. According to Meyer "Based on the job posting sample, the primary skills these organizations required were statistics, R, machine learning, storytelling, and Python". While there may be a few graduate level traditional Biomedical Informatics courses that teach programming languages, most online Health Informatics courses do not.


Do Health Informatics graduates need to know a programming language? The answer is probably not, as there is a very steep learning curve for R and Python. Do they need to expand their knowledge of biostatistics and analytics? The answer is yes.


Over the past several years we have seen the rapid growth of graduate biomedical data science Master level degree courses (called healthcare data science at some universities) which will help fill the pipeline with more skilled workers but this will take some time.


Our textbook should be out by mid-December accompanied by resources for instructors: a PDF version of the textbook, Instructor Manual and PowerPoints. I plan to discuss each chapter via this blog in the near future as well as discuss late breaking issues of interest to the informatics and biomedical data science communities.


I was extremely fortunate to ask Bob Muenchen to contribute initially as an author and later as a co-editor. It became clear that his expertise in biostatistics and R programming was just the type of experience I needed to complement my skills set. We are also very fortunate to have great authors who had unique experiences and familiarity with the healthcare domain.



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