Australian Cardiac Care Study and other Health GIS News
QUT Associate Professor Robyn Clark measured access to cardiac care in Australia using GIS.
“We looked at the distance to cardiac treatment centre locations in all of Australia’s 20,000 population centres,” Associate Professor Clark said.
“By mapping the huge amounts of statistical data we collected with GIS technology we were able to identify critical patterns and relationships that would not have been so apparent in table form.
“More specifically, we were able to identify locations and groups of people with limited access to cardiac services.
“For example, we found that only 40 per cent of indigenous people reside within an hour of appropriate cardiac medical facilities and cardiac rehabilitation services, while 12 per cent of indigenous Australians live three or more hours from any kind of hospital.”
The IOM [Institute of Medicine] panel concluded a review of geographic adjustments to physician payments in the Medicare program by releasing its second report on the subject July 16. The committee had called on the Centers for Medicare & Medicaid Services to break up its large payment jurisdictions into nearly five times as many smaller regions to reflect more accurately the costs of practicing medicine. The second report shows how the recommendations would impact Medicare rates.
A team of computer-science researchers have reportedly discovered a way to use Twitter to figure out when you are going to catch a cold. Their application scans Twitter for telltale phrases — I’m guessing things like “How come nobody sells tissues at San Diego Comic-Con?” and “I’m so sick, I can barely lick all these doorknobs” — and uses them to map the progress of disease the way a weatherman maps the progress of his ratings.
That's the hot quicky version from Wired. Now the real story from New Scientist:
That may sound obvious, but Adam Sadilek at the University of Rochester in New York and colleagues have applied the idea to a pile of Twitter data from people in New York City, and found that they can predict when an individual person will come down with the flu up to eight days before they show symptoms.