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Latest Big Data Healthcare News

3 reasons why health care data is such a security risk — and what health
This week, the FBI reminded the health care industry that it's a huge target for hackers. The briefing obtained by Reuters came on the heels of a wide-scale attack on Franklin, Tennesse-based Community Health Systems Inc. Millions of patient records …
Read more on Washington Business Journal (blog)

Update: Intel To Apply Big Data Into Medicine (INTC)
The data generated by IoT in healthcare can drive new efficiencies, while advancing research and improving care. Intel's Big Data analytics platform is capable of integrating a number of software components, including Cloudera CDH – an open-source …
Read more on Seeking Alpha (registration)

Celebs gin up votes for midterm push
In 2010, the under-30 vote dropped 27 percent since Obama first sailed to victory two years earlier, according to CIVIC analysis of Census data. Bernstein's group hopes to engage these so-called … A big push for the youth vote will be National Voter …
Read more on The Hill (blog)

Richardson cloud provider takes military approach to protect against cyberattacks
… boardrooms, FireHost executives say. And that leaves a big opening for a company like FireHost. … FireHost is focused on industries that deal with sensitive data and privacy compliance regulations, such as health care and retail. But executives …
Read more on Dallas Morning News

Latest Big Data Healthcare News

Medical white paper using big data to be drawn up
By releasing an analysis of such “big data,” which shows what kinds of medical treatment patients have received at hospitals, the government hopes local governments burdened with massive health care expenditures will be prompted to trim costs.
Read more on The Japan News

Failure of Aetna's CarePass platform might be a bad omen for Apple HealthKit
Last week, the health insurance giant Aetna said it would discontinue its CarePass consumer health data platform by the end of 2014. And when a player as big as Aetna dumps its health data platform, it's cause for discussion, if not concern, in digital …
Read more on VentureBeat

Pentagon Accepts Bids on Long-Awaited Health Records Contract
Major data gaps in patient records frequently occur when health care is delivered to DOD beneficiaries outside of the DOD network. That's been a big problem considering about half of the nearly 10 million DOD beneficiaries choose their care outside the …
Read more on Nextgov

Cell-Phone Data Might Help Predict Ebola's Spread

Cell-Phone Data Might Help Predict Ebola's Spread
The model created using the data is not meant to lead to travel restrictions, but rather to offer clues about where to focus preventive measures and health care. Indeed, efforts to restrict people's movements, such as Senegal's decision to close its …
Read more on MIT Technology Review

My Fitbit experience: Lost 27 pounds, gained a lot of questions
Despite not setting an intention for weight loss, I have lost weight. That wasn't a big surprise. After all, I'm doing more than 15,000 steps and 10 flights of stairs almost ever day — way more than the 6,000 to 7,000 steps a day that I was doing when …
Read more on CITEworld

Latest Big Data Healthcare News

Will the worlds of Hadoop and Big Data combine or collide? | #HPBigData2014
As far as Blue Cross Blue Shield was concerned, the health insurance provider never saw a risk of database collision with Big Data. Quite the contrary, it anticipated that things were all converging for the purposes of data management. Vellante wanted …
Read more on SiliconANGLE (blog)

The Morning Download: What CIOs Understand About the Plight of the 'Data
You can get The Morning Download emailed to you each weekday morning by clicking here. Good Morning. Companies are chasing Big Data technology and talent in a bid to tease business advantages out of a variety of databases, sensors and documents.
Read more on Wall Street Journal (blog)

The exchange factor: Electronic data system points to a future of managing
The health care and business communities in Michigan may be on the cusp of realizing the ultimate benefit of the free flow of online medical electronic data: Managing the health of patients as they move through the health care delivery system across …
Read more on Crain’s Detroit Business

Why CEO decided being a better dad was more important than his job

Why CEO decided being a better dad was more important than his job
Instead, the outgoing CEO of software company MongoDB decided to use his story to help advance the cause of engaged fathers. Video: In Today's Take, Al Roker, Tamron Hall and Natalie Morales discuss a male CEO's explanation for stepping down from …
Read more on Today.com

ClusterHQ Launches Flocker to Facilitate Robust Stateful Docker Containers
Developers wishing to try out Flocker can follow the getting started guide, which uses Vagrant and VirtualBox to run VMs with Flocker demos using MongoDB and PostgreSQL. Marsden's recent presentation to the Edinburgh Docker Meetup on 'Data focused …
Read more on InfoQ.com

MongoDB当成纯内存数据库使用
如果这一切可以实现就真是太优雅了:我们就能够巧妙地在不涉及磁盘操作的情况下利用MongoDB的查询/检索功能。可能你也知道,在99%的情况下,磁盘IO(特别是随机IO)是系统的瓶颈,而且,如果你要写入数据的话,磁盘操作是 …
Read more on 比特网

Lahman: Wearable tech will change health care

Lahman: Wearable tech will change health care
Wearable electronics reflect a convergence among three major technological advances of this generation: the miniaturization of hardware and ability to put remarkable amounts of processing power in very small devices; big data's ability to collect and …
Read more on Rochester Democrat and Chronicle

Why Big Data Isn't Enough: Tomorrow's Technology Will Be Built Around
A new generation of technologies are starting to enable this scenario and changing the way healthcare and other data are mined and utilized, pulling data from various sources to create actionable real-time care plans for patients and providers based on …
Read more on Wired

4 recommendations to harness big data
The number of participants and records to analyze matters in health data networks. Stakeholders must create incentives for health care providers to participate and adopt standards of interoperability. Providers must be committed to educating patients …
Read more on FierceHealthIT

Four Ideas to Leverage the Maximum Potential of Data in Health Care
It is critical to design privacy controls into the structure of large data systems. This will give patients confidence about the security of their data. Because of the unique nature of medical data, it is also difficult to provide restitution in the …
Read more on Brookings Institution (blog)

Vice President of Community, MongoDB

Vice President of Community, MongoDB
Matt Asay is Vice President of Community at MongoDB. He was previously VP of Business Development at Nodeable. You can reach him at mjasay@mac.com and follow him on Twitter @mjasay. Articles by Matt Asay. View Content By Month …
Read more on Banktech (blog)

No, men aren't “having it all” either
Writing on his blog Tuesday, Schireson, the 44-year-old CEO of the billion dollar database company MongoDB, explains exactly why he is now leaving “the best job I ever had.” As he notes, his job requires him “to fly 300,000 miles this year, all the …
Read more on Salon

DevOps Weekly Round-Up: Combining big data and DevOps
EnterpriseDB (EDB) has released an updated PostgreSQL's ability for MongoDB to the open source PostgreSQL community and is planning to release one for Hadoop in coming months. The release called Foreign Data Wrapper (FDW) take advantage of …
Read more on SiliconANGLE (blog)

Latest Big Data Healthcare News

Hated the Facebook experiment? You'll hate what's next for health care.
I. Glenn Cohen: There are different definitions, but essentially "big data" in health care refers to the idea — particularly with the shift to electronic health records — that we have millions and millions of patient records that are now in the …
Read more on Vox

Predictive big data analytics in healthcare
The health sector is a leader in the deployment of big data technologies. Computing spoke to US health analytics firm Amara Health Analytics about the role of big data in hospital alert systems, and also to UK provider FlyingBinary about progress in …
Read more on Computing

Latest MongoDB News

Backed By YC And Rock Health, Aptible Handles The Hard Parts Of HIPAA
“We let you use the languages open source databases that everybody in tech uses, like PHP and MongoDB. If you want to migrate on or off Aptible, you don't have to learn the language or API that was invented just for HIPAA compliance. We do it all for …
Read more on TechCrunch

peHUB Second Opinion
Max Shireson gives a touching explanation as to why he's stepping down as CEO of MongoDB. And no, it's not the money. AOL sues Johnathon Woods, its former Director of Studio Technology, for fraudulently billing the company for over a million dollars.
Read more on Thomson Reuters’ peHUB (press release)

XOLO's HIVE UI Launched
Rip Curl Search GPS is backed and enabled by ObjectRocket, Rackspace Database-as-a-Service (DBaaS) for NoSQL MongoDB. ObjectRocket is a sharded and fully managed service for MongoDB, built with a set of tools and APIs designed to maximise …
Read more on Businessworld

Jvion Releases Top Three Lessons in Predictive Population Health Analytics


Atlanta, GA (PRWEB) July 31, 2014

Atlanta-based Jvion, a leader in clinical predictive algorithms and machine learning, released their top three lessons in predictive population health analytics as part of an ongoing series dedicated to understanding and applying predictive analytics in healthcare. Ritesh Sharma, Jvion COO, commented, “it is critical that we work together as an industry to understand the impact of and potential within predictive analytics. There is a lot of information out there and a good portion of it is conflicting or confusing. The effectiveness of predictive capabilities in improving health outcomes starts with understanding where we can effectively apply these new and powerful technologies. When applied right, predictive analytics empower hospitals to proactively do a lot of things.”

Top Three Lessons – Summary Findings

Why focus on population health?

Population health initiatives have the overarching goal of targeting specific at risk populations to apply low cost interventions across the care continuum. New value-based payment models and systems like accountable care organizations are forcing many providers to rethink their approach to prevention and evaluate the effectiveness of predictive technologies in targeting specific, high and rising-risk segments of the community.

What role do predictive analytics play in population health?

There are three types of population health solutions in the market: first, traditional analytic solutions (retrospective data analysis); second, Enterprise Data Warehouse (EDW) based predictive analytics; and third, (new) Machine Learning algorithm powered solutions. Traditional analytic solutions involve retrospective data analysis, benchmarks, and trending and are primarily focused on looking back in time to understand how things worked in the past. The problem with this approach is that it only helps maintain a scorecard; it does not provide enough actionable information for organizations to proactively plan and change future results.

The other two forms of population health solutions are predictive: EDW and Machine Learning. EDW based solutions compile large amounts of data into a single data warehouse. This data is then consolidated and analyzed for patterns that lead to predictive insights. Machine Learning based solutions start by building models and clusters, and analyzing individual risk levels for all patients across a population. This individual-level risk is then aggregated and stratified into risk cohorts that can be targeted for specific interventions.

Feedback and findings suggest that Machine Learning solutions are better suited for the healthcare industry because they tend to deliver more detailed and more accurate results that don’t require a heavy investment in an EDW. The time it takes to deliver value is also significantly different. EDW solutions take a minimum of 18-24 months to stand up whereas Machine Learning solutions can start to deliver outputs in weeks. Additionally, the more advanced Machine Learning solutions currently available are able to use publically and readily available data elements to quickly stratify risk and define cohorts at accuracy levels that are much higher than earlier generation models.

How do you articulate the Return on Investment (ROI) for these solutions?

The ROI for a predictive analytic solution can seem a little “fuzzy” because it is about cost avoidance. The abstracted benefits of population health initiatives are well known. Employers benefit from lower absenteeism and injury rates, and the subsequent increase in productivity. Taxpayers benefit by lowering the number of chronic conditions treated through the Medicare and Medicaid systems. And communities benefit from the overall strengthening of the economy and associated influx of federal investment/benefits.

For individual hospitals, the numbers can get a little blurry and slippery if they are not already proactively managing population health. However, with risk-based and value-based contracting practices becoming more prevalent across payors, population health initiative ROI becomes much more straightforward and concrete for hospitals. In addition, findings suggest that predictive analytics actually enable better ROI measures. This is because they can quickly and comprehensively analyze historical data to determine the dollars saved if preventative measures were applied based on predicted risk insights. Using this approach, a hospital can assign a hard dollar ROI not only to population health overall, but down to a specific disease and subset of a risk stratified cohort.

For more information on Jvion and their population health solutions, please click here. And for information on the firm’s entire suite of predictive analytic solutions, please visit http://www.jvion.com.

About Jvion

Jvion is a healthcare technology company that develops software designed to predict and prevent patient-level disease and financial losses leading to increased waste. The company offers a suite of big-data enabled solutions that combine clinical intelligence with deep machine learning to help providers protect their revenues while improving patient health outcomes. The objective is simple—stop the waste of resources and lives by predicting and stopping losses before they ever happen.

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