Better medicine, brought to you by big data through new types of data analysis

 

A good overview of how improved data analysis and presentation is improving health care delivery.

I especially liked the slideshare presentation found below in Related Articles.
The 42 slides in Big data – a brief overview outlines what big data is, its sources and processes, how it is analyzed, current “players”,examples, market analysis, future, and opportunities.

From the 15 July 2012 blog post at Gigaom

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 isgetting cheaper by the day and produces mountains of data. Companies such asDNAnexus, 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.

BI[definition of business intelligence] 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…

..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 dothanks to its big data efforts…

and more!

Related articles

• Better medicine, brought to you by big data [GigaOM] (gigaom.com)
• Intel and NextBio seek Big Data upgrades in genomics (fiercebiotechit.com)
• Big Changes Are Ahead For The Health Care Industry, Courtesy Of Big Data (fastcompany.com)

• Big data – a brief overview (slideshare.net) [a slide presentation, 42 slides]

• Oracle adopts the popular R language for the enterprise and big data. (oracle.com)
• Presentation: Scalability Challenges in Big Data Science (architects.dzone.com)
• Salesforce intros Radian6 Insights for social big data (zdnet.com)
• Big Data Modeling – Part I – Defining “Big Data” and “Data Modeling” (infocus.emc.com)
• NextBio and Intel Announce Collaboration to Optimize Use of Hadoop Stack And Move Forward With Big Data Technologies in Genomics (ducknetweb.blogspot.com)
• A Beautiful Friendship: Big Data and Social Media (blogs.sap.com)
• Stanford rides Big Data wave in medical research (fiercebiotechit.com)
• Big Data? Big Deal! (clean-clouds.com)

The gender divide when it comes to health tracking online

From the 15 April 2012 article by Susannah Fox at KevinMD.com

Stephen Wolfram’s essay, The Personal Analytics of My Life, begins, “One day I’m sure everyone will routinely collect all sorts of data about themselves.”

A Pew Internet survey suggests we have a long way to go: a September 2010 survey found that 27% of internet users age 18+ track their own health data online. There may be more self-tracking happening offline — please post any measures of that phenomenon in the comments….

…

I did a quick search for more insights on this Mars/Venus divide and found Matthew Cornell’s post on the Quantified Self blog, Is There a Self-Experimentation Gender Gap? His rough analysis of QS comments, videos, and in-person meetings found a clear difference in participation: about 80% men, 20% women.

Christine McCaull echoed Schulte’s complaint in her comment:

… I’m just too damn busy to measure almost anything regularly except my bank balance, which is calculated for me. Like most women, I’m on a triple shift life plan. I work, I write, I keep a house and raise a big family…

And yet proponents of self-tracking in health need everyone to engage in it and see its worth, not just people with the leisure (or the extreme motivation of a life-changing diagnosis) to do so.

I went back to our data to see if there is a gender divide when it comes to health tracking online. Yes, there is: women are more likely than men to do it.

Breaking it down into the two categories we asked about, we find that 18% of women track their weight, diet, or exercise routine, compared with 13% of men. Twenty-one percent of women track some other health indicators online, compared with 12% of men…

 

Related articles

• Pew: Smartphones narrow digital divide (news.cnet.com)
• Smartphones bridge US digital divide (thehimalayantimes.com)
• Report: One In Five U.S. Adults Does Not Use The Internet (techcrunch.com)