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Rescooped by Roger D. Jones, PhD from healthcare technology
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How big data is beginning to change how medicine works

How big data is beginning to change how medicine works | Complex Systems and X-Events | Scoop.it

The face of medical care is rapidly changing thanks to major advancements in the capture, proliferation, and analysis of medical data. Technologies like the electronic health records (EHRs) and personal health records (PHRs) are drastically improving the way data is aggregated and shared.

 

Now the hope is that big data analytics will help to make sense of seemingly endless streams of medical information.


As many doctors are painfully aware, outcome-oriented care is no longer a buzzword but a reality. The Center for Medicare and Medicaid Services has started to implement a program where payments are based on the ability of providers to meet key National Quality Strategy Domains (e.g. care criteria). Public payers are testing this new methodology, and private payers are expected to soon follow.

 

These big data analytics applications can also be relevant for the FDA, which may want to see how drugs perform in a non-test environment to ensure the appropriate patient populations are receiving the drug. I also expect pharmaceutical companies to actively scour this data to track drug efficacy post-release or identify markets that could “benefit” from increased penetration.

 

I am eager to see how the data evolution improves outcomes for doctors and patients.

 

 

more at http://venturebeat.com/2014/10/16/how-big-data-is-beginning-to-change-how-medicine-works/ ;
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Rescooped by Roger D. Jones, PhD from Non-Equilibrium Social Science
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Urban #Observatory Compare Cities

Urban #Observatory Compare Cities | Complex Systems and X-Events | Scoop.it

The focus of the Urban Observatory is on the people who live in cities, the work they do there, the movement made possible through transportation networks, the public facilities needed to run the city, and the natural systems which are impacted by the city's footprint. If you are a city with mappable data in any of these categories, we urge you to contribute maps to the project.


Via bart rosseau, luiy, @backbook, NESS
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bart rosseau's curator insight, July 15, 2013 4:14 AM

great concept, great layout and design. Curious to see if this will last!

luiy's comment, March 1, 2014 11:36 AM
I can see the interesting application in the are of #eDemocracy
Rescooped by Roger D. Jones, PhD from healthcare technology
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Where Will Healthcare's Data Scientists Find The Rich Phenotypic Data They Need?

Where Will Healthcare's Data Scientists Find The Rich Phenotypic Data They Need? | Complex Systems and X-Events | Scoop.it

The big hairy audacious goal of most every data scientist I know in healthcare is what you might call the Integrated Medical Record, or IMR, a dataset that combines detailed genetic data and rich phenotypic information, including both clinical and “real-world” (or, perhaps, “dynamic”) phenotypic data (the sort you might get from wearables).

 

The gold standard for clinical phenotyping are academic clinical studies (like ALLHAT and the Dallas Heart Study).  These studies are typically focused on a disease category (e.g. cardiovascular), and the clinical phenotyping on these subjects – at least around the areas of scientific interest — is generally superb.  The studies themselves can be enormous, are often multi-institutional, and typically create a database that’s independent of the hospital’s medical record.

 

 

Inevitably, large, prospective studies can take many years to complete.  In addition, there’s generally not much real world/dynamic measurement.

 

 

The other obvious source for phenotypic data is the electronic medical record (EMR).  The logic is simple: every patient has a medical record, and increasingly, especially in hospital systems, this is electronic – i.e. an EMR.  EMRs (examples include Epic and Cerner) generally contain lab values, test reports, provider notes, and medication and problem lists.  In theory, this should offer a broad, rich, and immediately available source of data for medical discovery.

 

 

DIY (enabled by companies such as PatientsLikeMe) represents another approach to phenotyping, and allows patients to share data with other members of the community.  The obvious advantages here include the breadth and richness of data associated with what can be an unfiltered patient perspective – to say nothing of the benefit of patient empowerment.  An important limitation is that the quality and consistency of the data is obviously highly dependent upon the individuals posting the information.

 

Pharma clinical trials would seem to represent another useful opportunity for phenotyping, given the focus on specific conditions and the rigorous attention to process and detail characteristic of pharmaceutical studies.  However, pharma studies tend to be extremely focused, and companies are typically reluctant to expand protocols to pursue exploratory endpoints if there’s any chance this will diminish recruitment or adversely impact the development of the drug.

 more at http://www.forbes.com/sites/davidshaywitz/2014/10/10/where-will-healthcares-data-scientists-find-the-rich-phenotypic-data-they-need/ ;
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