Better medicine, brought to you by big data
Slowly but surely, health care is becoming a killer app for big data. Whether it’s Hadoop, machine learning or natural-language processing, folks in the worlds of medicine and hospital administration understand that data is the key to helping them take their fields to the next level.
Slowly but surely, health care is becoming a killer app for big data. Whether it’s Hadoop, machine learning, natural-language processing or some other technique, folks in the worlds of medicine and hospital administration understand that new types of data analysis are the key to helping them take their fields to the next level.
Here are some of the interesting use cases we’ve written about over the past year or so, and a few others I’ve just come across recently. If you have a cool one — or a suggestion for a new use of big data within the healthcare space — share it in the comments:
- Genomics. This is the epitomic case for big data and health care. Genome sequencing is getting cheaper by the day and produces mountains of data. Companies such as DNAnexus, Bina Technologies, Appistry and NextBio want to make analyzing that data to discover cures for diseases faster, easier and cheaper than ever using lots cutting-edge algorithms and lots of cloud computing cores. Dell is providing computing power for two research centers to try and treat a particular form of pediatric cancer based on each child’s specific genetic profile.
- BI for doctors. Doctors and staff at Seattle Children’s Hospital are using Tableau to analyze and visualize terabytes of data dispersed across the institution’s servers and databases. Not only does visualizing the data help reduce medical errors and help the hospital plan trials but, as of this time last year, its focus on data had saved the hospital $3 million on supply chain costs.
- Semantic search. Imagine you’re a doctor trying to learn about a new patient or figure out who among your patients might benenfit from a new technique. But patient records have been scattered throughout departments, vary in format and, perhaps worst of all, all use the ontologies of the department that created the record. A startup called Apixio is trying to fix this by centralizing records in the cloud and applying semantic analysis to uncover everything doctors need, regardless who wrote it.
- Hadoop for everything. Cloudera is partnering with the Mount Sinai School of Medicine to help it develop new methods and systems for analyzing biological data. But that’s just the latest of Cloudera medical efforts, which also include working with the Food and Drug Administration to detect unsuspected adverse side effects from multi-drug combinations, and Emory University on helping pathologists more accurately analyze medical images. One of Cloudera’s customers, Explorys, built a business around aggregating and analyzing medical records, and Intel and NextBio are teaming to tune Hadoop for processing genomic datasets.
- Watson. IBM has dozens of irons in the healthcare fire, but its coolest might well be a partnership with WellPoint to put the Jeopardy! champion question-answering system in doctors’ offices. Watson could help doctors answer questions posed in natural language by analyzing them against mountains of medical research data that no individual doctor could possibly read and digest.
- Getting ahead of disease. It’s always good if you figure out how to diagnose diseases early without expensive tests, and that’s just what Seton Healthcare was able to do thanks to its big data efforts. Trying to find better ways to detect congestive heart failure early in order to save the exorbitant costs of treatment as the disease progresses, a team found that a distended jugular vein — something that can be spotted during any routine physical exam — is a particularly high risk factor.
- Data scientist in residence. Here’s a new title for a healthcare organization — chief data scientist. Yet, that’s exactly the position Alliance Health Networks just added in May. The company, which provides social networks focused on specific medical conditions, acquired medical research database Medify and decided it needed someone to lead the effort of analyzing all that data and providing valuable feedback to network users.
- Crowdsourced science. In a field where controlled experiments can be expensive and sometimes ineffective, it’s turning out there might be no substitute like the real-world data. Probably the most widely known company in this space is PatientsLikeMe, a social network designed to let individuals share their medical conditions so they can learn from others like themselves what treatments might work best in their particular circumstances. As a side effect, the company is able to conduct observational trials based on data users willingly volunteer.