ENTRIES TAGGED "watson"

Bringing the knowledge of best practices to the doctor’s point of care

Workflows, EHRS, and social networking

Impatience is said to be the stance of modern technology users, but a doctor sitting with a patient has good reason to be impatient. The afflicted person may be suffering from a condition where lost minutes could mean death, an amputated limb, or severe brain disfunction. Even if the patient’s condition is not so dire, there are probably a half dozen other people with painful complaints twiddling their thumbs in the waiting room while the doctor tries to guess at a course of treatment. And in the US today, guessing is often the only option.

Somewhere in the country, an expert has probably learned all about the medical condition at hand and even presented the solution at a conference. Relatively few medical crises are really new discoveries. But the current system of disseminating information through conferences, journals, and rotations, or even through newer media such as blogs and webinars, cannot reach the beleaguered doctor and patient at the point of care.

I got a glimpse of a solution in the book #SOCIALQI (which has an associate web site) by the multi-disciplinary biomedical researcher Brian McGowan. His first challenge to us is an assertion that the central problem holding back improvements in health care quality is the inadequate dissemination of knowledge. I could match this claim with several other urgent needs in the health care field: inconsistent and distorted recording of patient data, lack of standards for storage and data exchange, and resistance by doctors to patient engagement, to name a few. But McGowan’s first chapter makes a very persuasive argument: if the best practices of each site were instituted throughout the health care system, we’d save thousands of lives and drastically lower costs. Read more…

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Strata Week: Raising the world’s data privacy IQ

Celebrating Data Privacy Day, how data fits into Bill Gates' education plan, and why "long data" deserves our attention.

Data Privacy Day and the fight against “digital feudalism”

Data Privacy Day was celebrated this week. Led by the National Cyber Security Alliance, the day is meant to increase awareness of personal data protection and “to empower people to protect their privacy and control their digital footprint and escalate the protection of privacy and data as everyone’s priority,” according to the website.

Many companies used the day as an opportunity to issue transparency reports, re-informing users and customers about how their data is used and and how it’s protected. Google added a new section to its transparency report, a Q&A on how the company handles personal user data requests from government agencies and courts.

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Strata Week: AWS and Rackspace, compared

How Amazon Web Services and Rackspace measure up; IBM's Watson goes to school; Google researches data; and what will we call really, really big data?

Here are a few stories from the data space that caught my attention this week.

Rackspace vs Amazon

Rackspace LogoAs Rackspace continues to ramp up its services to compete with Amazon Web Services (AWS) — this week, announcing a partnership with Hortonworks to develop a cloud-based enterprise-ready Hadoop platform to compete against Amazon’s Elastic MapReduce — Derrick Harris at GigaOm compared apples to apples.

John Engates, CTO of Rackspace, told Harris the most fundamental difference between the two services is the level of control given to the customer. Harris writes that Rackspace’s new Hadoop services aims to give the customer “granular control over how their systems are configured and how their jobs run,” providing “the experience of owning a Hadoop cluster without actually owning any of the hardware.” Engates pointed out, “It’s not MapReduce as a service; it’s more Hadoop as a service.”

Harris also points out that Rackspace is considering making moves into NoSQL and looks at AWS’ DynamoDB service. He notes that Amazon and Rackspace aren’t the only players on any of these fields, pointing to the likes of Microsoft’s HDInsight, IBM’s BigInsights, Qubole, Infochimps, MongoDB, Cassandra and CouchDB-based services.

In related news, Rackspace announced its new Cloud Networks feature this week that allows customers to design their own networks on Rackspace’s Cloud Servers. In an interview with Jack McCarthy at CRN, Engates explained the background:

“When we went from dedicated physical networks to our public cloud, we lost the ability to segment these networks. We used to have a vLAN. As we moved to OpenStack, we wanted to give our customers the ability to enable segmented networks in the cloud. Cloud Networks gives customers a degree of control over how they build networks in the cloud, whether it’s building networks application servers or for Web servers or databases.”

Engates also points out the networks are software-defined, “so customers can program their network on the fly.” You can read more about the new feature on the Rackspace blog.

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The quiet rise of machine learning

The quiet rise of machine learning

Alasdair Allan on how machine learning is taking over the mainstream.

From Goodreads to Google to Orbitz, machine learning is slowly becoming part of everyday life. Alasdair Allan discusses current uses and how machine learning factors into his own robotic telescope network.

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