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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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Rescooped by Roger D. Jones, PhD from healthcare technology
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Digital health is going to need medical approval and a great UI

Digital health is going to need medical approval and a great UI | Complex Systems and X-Events | Scoop.it

So far the internet of things hasn’t made much headway into patient care in the medical setting, but consumers are buying wellness devices for a variety of reasons. Will the medical world embrace that data?

 

The intersection of healthcare and connected devices was thrown into high relief these last few weeks as both Apple and Samsung unveiled ecosystems to take consumer health data and turn it into actionable intelligence.

 

But this week’s guests at the Weekly podacst at GigaOm are confident that as advanced as consumer-grade consumer grade health devices get, they won’t become something doctors are hot on for years to come — if ever.

 

In this week’s podcast Stacey Higginbotham discusses medical connected devices and where it may meet the consumer with Rick Valencia from Qualcomm Life. Will doctor’s prescribe our apps or devices? 


 Listen to the podcast at  http://soundcloud.com/gigaom-internet-of-things  Original article at http://gigaom.com/2014/06/09/digital-health-is-going-to-need-medical-approval-and-a-great-ui/ ;
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Vigisys's curator insight, June 15, 4:22 AM

Un podcast intéressant qui évoque les freins à l'utilisation médicale des objets connectés. On y évoque le besoin de valider les usages avec des études cliniques et d'adapter les interfaces à un usage professionnel. Que du bon sens !

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Case study: Big data improves cardiology diagnoses by 17%

Case study: Big data improves cardiology diagnoses by 17% | Complex Systems and X-Events | Scoop.it

Big data analytics technology has been able to find patterns and pinpoint disease states more accurately than even the most highly-trained physicians.

 

The human brain may be nature’s finest computer, but artificial intelligences fed on big data are making a convincing challenge for the crown. In the realm of healthcare, natural language processing, associative intelligence, and machine learning are revolutionizing the way physicians make decisions and diagnose complex patients, significantly improving accuracy and catching deadly issues before symptoms even present themselves.

 

In this case study examining the impact of big data analytics on clinical decision making, Dr. Partho Sengupta, Director of Cardiac Ultrasound Research and Associate Professor of Medicine in Cardiology at the Mount Sinai Hospital, has used an associative memory engine from Saffron Technology to crunch enormous datasets for more accurate diagnoses.

 

Using 10,000 attributes collected from 90 metrics in six different locations of the heart, all produced by a single, one-second heartbeat, the analytics technology has been able to find patterns and pinpoint disease states more quickly and accurately than even the most highly-trained physicians.

 

more at http://healthitanalytics.com/2014/07/07/case-study-big-data-improves-cardiology-diagnoses-by-17/

 


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Health Domains for Sale: The Need for Global Health Internet Governance

Health Domains for Sale: The Need for Global Health Internet Governance | Complex Systems and X-Events | Scoop.it

Current controversy surrounding health domains is rooted in the Internet’s growing importance as a health information source. In 2013, the International Telecommunication Union estimated that 38.8% (2.7 billion) of the world’s population used the Internet. Many of these users are seeking important health information online. In the United States, surveys report 72% of online adults accessed the Internet to find health information primarily on the subjects of diseases and treatments . Other regions, including the European Union and emerging markets, have also shown marked increases in online health information seeking and self-diagnosing behavior.


The importance of establishing an inclusive yet reliable presence for health information online is critical to future global health outcomes given the growing importance of the health Internet. However, .health and many other health-related gTLDs are now on sale to private sector entities that largely permit open and unrestricted use. Yet, the globalized nature of the Internet, the public health need for privacy, security, and quality health information, and the rapid expansion of online health technologies demonstrate a critical need to ensure proper governance of future health domains. Focusing on the public good can be a first and crucial step to ensure an accurate, reliable, and evidence-based online presence for health for this generation and the next.


more at http://www.jmir.org/2014/3/e62/


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