MIT's Answer to Global Health Issues: Democratizing Big Data Analytics

Michael Kassner | Tech Republic | June 24, 2016

Discover how medical professionals and MIT researchers are using data-enabled systems to help doctors around the world make the best healthcare decisions.

If you think it's hard to keep up with all the new software and hardware innovations, imagine doctors trying to stay abreast of medical advances. "While wonderful new medical discoveries and innovations are in the news every day, doctors struggle with using information and techniques available right now," writes Leo Anthony Celi, assistant professor of medicine, Harvard Medical School, in the Conversation commentary Improving patient care by bridging the divide between doctors and data scientists. "As a practicing doctor, I deal with uncertainties and unanswered clinical questions all the time."

As to why there are uncertainties, Celi suggests the following reasons:

  • Doctors are busy helping patients. So, there is precious little time to find and digest information in books and online.
  • If there is time to wade through information, Celi feels more often than not the data is not specific enough to apply to a given patient or situation.
  • A significant number of health records are still paper-based, making it difficult to disseminate vital diagnostic information.

Because of the above, Celi says, "We must work with what we carry in our heads, from personal experience and education." Another constraint, perhaps even more important, is that the information available is usually not focused on the specific individual or situation at hand. Celi feels there are opportunities for big data and information analytics in the healthcare field. "A digital system would collect and store as much clinical data as possible from as many patients as possible," writes Celi. "It could then use information from the past—such as blood pressure, blood sugar levels, heart rate, and other measurements of patients' body functions—to guide future doctors to the best diagnosis and treatment of similar patients"...