Guest posting by Bailey Flynn
Medicine sits at the crossroads of many subjects. One of the reasons I love the field so much is because of its interdisciplinary nature. Medicine requires science, business, math, history, communication, engineering, logic, and more. However, these requirements also mean that it is an exceedingly complex, even intimidating, field.
One recent article has to do with the intersection of medicine and data science. Data science, a relatively new field, is the study of data and how it can be applied to solve problems. It has been employed in banking, school admissions, politics, and even social media. However, it’s been somewhat late to the field of medicine. The New York Times article, “On the Case at Mount Sinai, It’s Dr. Data,” follows the career of Dr. Hammerbacher, who is now applying data science to medicine, only after a long career in business, start-ups, and social networking. Similar to the field itself, Hammerbacher took some time to begin in medicine, however, the contributions he is now making are invaluable.
Data science, applied appropriately, has the potential to visualize disease progressions, formulate more effective treatments, project patient outcomes, and much more. The practice of medicine accumulates a tremendous amount of data – from patient vitals to more complex imaging and beyond. The application of data science would put this data to use to improve future patient treatments and outcomes based on past cases. In class, we watched a brief clip of Hans Rosling’s TED talk, in which he displays data about health in the developing world. It is at the same time attention grabbing and incredibly informative – overall an effective combination of medicine and data.
Specifically, for my project, data science would be useful to reduce the average length of stay necessary for patients after liver transplant. Collecting and analyzing data from current patients would allow doctors to see where the process is least efficient. It would also illuminate what a standard post-transplant stay looks like, allowing doctors to estimate when patients should be discharged.
It is critical that other disciplines, including data science, collaborate to make medicine as effective as possible. Of course, data science cannot single-handedly fix the healthcare system. It is only in conjunction with willing healthcare providers that it becomes effective. Even Dr. Hammerbacher admits, “We’re not the most important people, but we can help.”
New York Times article:
http://www.nytimes.com/2015/03/08/technology/on-the-case-at-mount-sinai-its-dr-data.html?ref=health
Hans Rosling TED talk: http://www.ted.com/talks/hans_rosling_shows_the_best_stats_you_ve_ever_seen