ENTRIES TAGGED "analytics"

Can data provide the trust we need in health care?

Collecting actionable data is a challenge for today's data tools

One of the problems dragging down the US health care system is that nobody trusts one another. Most of us, as individuals, place faith in our personal health care providers, which may or may not be warranted. But on a larger scale we’re all suspicious of each other:

  • Doctors don’t trust patients, who aren’t forthcoming with all the bad habits they indulge in and often fail to follow the most basic instructions, such as to take their medications.
  • The payers–which include insurers, many government agencies, and increasingly the whole patient population as our deductibles and other out-of-pocket expenses ascend–don’t trust the doctors, who waste an estimated 20% or more of all health expenditures, including some thirty or more billion dollars of fraud each year.
  • The public distrusts the pharmaceutical companies (although we still follow their advice on advertisements and ask our doctors for the latest pill) and is starting to distrust clinical researchers as we hear about conflicts of interest and difficulties replicating results.
  • Nobody trusts the federal government, which pursues two (contradictory) goals of lowering health care costs and stimulating employment.

Yet everyone has beneficent goals and good ideas for improving health care. Doctors want to feel effective, patients want to stay well (even if that desire doesn’t always translate into action), the Department of Health and Human Services champions very lofty goals for data exchange and quality improvement, clinical researchers put their work above family and comfort, and even private insurance companies are trying moving to “fee for value” programs that ensure coordinated patient care.

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Day-Long Immersions and Deep Dives at Strata Santa Clara 2014

Tutorials for designers, data scientists, data engineers, and managers

As the Program Development Director for Strata Santa Clara 2014, I am pleased to announce that the tutorial session descriptions are now live. We’re pleased to offer several day-long immersions including the popular Data Driven Business Day and Hardcore Data Science tracks. We curated these topics as we wanted to appeal to a broad range of attendees including business users and managers, designers, data analysts/scientists, and data engineers. In the coming months we’ll have a series of guest posts from many of the instructors and communities behind the tutorials.

Analytics for Business Users
We’re offering a series of data intensive tutorials for non-programmers. John Foreman will use spreadsheets to demonstrate how data science techniques work step-by-step – a topic that should appeal to those tasked with advanced business analysis. Grammar of Graphics author, SYSTAT creator, and noted Statistician Leland Wilkinson, will teach an introductory course on analytics using an innovative expert system he helped build.

Data Science essentials
Scalding – a Scala API for Cascading – is one of the most popular open source projects in the Hadoop ecosystem. Vitaly Gordon will lead a hands-on tutorial on how to use Scalding to put together effective data processing workflows. Data analysts have long lamented the amount of time they spend on data wrangling. But what if you had access to tools and best practices that would make data wrangling less tedious? That’s exactly the tutorial that distinguished Professors and Trifacta co-founders, Joe Hellerstein and Jeff Heer, are offering.

The co-founders of Datascope Analytics are offering a glimpse into how they help clients identify the appropriate problem or opportunity to focus on by using design thinking (see the recent Datascope/IDEO post on Design Thinking and Data Science). We’re also happy to reprise the popular (Strata Santa Clara 2013) d3.js tutorial by Scott Murray.

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Building the Brain of Dr. Algorithm

Archimedes advances evidence-based medicine to foster model-based medicine

This posting is by guest author Tuan Dinh, who will speak about this topic at the Strata Rx conference.

Legendary Silicon Valley investor Vinod Khosla caused quite a stir last year when he predicted at Strata Rx that “Dr. Algorithm”–artificial intelligence driven by large data sets and computational power–would replace doctors in the not-too-distant future. At that point, he said, technology will be cheaper, more accurate and objective, and will ultimately do a better job than the average human doctor at delivering routine diagnoses with standard treatments.

I not only support Khosla’s provocative prophecy, I’ll add one of my own: that Dr. Algorithm (aka Dr. A) will “come to life” in three to five years, by the time today’s first-year med school students are pulling 30-hour shifts as new interns. But what will it take to build the brain of Dr. A? And how can we teach Dr. A to account for increasingly complex medical inputs, such as laboratory tests results, genomic/genetic information, family and personal history, co-morbidities and patient preferences, so he can make optimal clinical decisions for living, breathing patients?

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OneHealth: Combining Patient-Generated Data With Communities and Feedback Loops

Evolution from a research tool to a platform for patient engagement

Bruce Springer of OneHealth will speak about this topic at the Strata Rx conference. This article was written by Patrick Bane of OneHealth in coordination with Bruce Springer.

According to a recent study performed by the Jesse Brown VA Medical Center and University of Illinois at Chicago, patient-centered care has demonstrated positive outcomes on patients’ health, patients’ self-report of health, and reduced healthcare utilization. The study’s results are consistent with previous research that the patient-centered care model improves the quality of care while simultaneously lowering the cost of care.

OneHealth’s behavior change platform extends the patient-centered model by connecting members anytime, anywhere through mobile and web applications. Member generate data in their daily lives, outside of a clinical setting, which creates a much richer dataset of behaviors that are required to understand the patients’ condition(s), and their readiness to change. Members freely choose what to do and their choices actively generate data in five classes of information:

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Denny Ausiello discusses phenotypes, pathways, and stratification

A video interview with Colin Hill

Last month, Strata Rx Program Chair Colin Hill, of GNS Healthcare, sat down with Dr. Dennis Ausiello, Jackson Professor of Clinical Medicine at the Harvard Medical School, Co-Director at CATCH, Pfizer Board of Directors Member, and Former Chief of Medicine at the Massachusetts General Hospital (MGH), for a fireside chat at a private reception hosted by GNS. Their insightful conversation covered a range of topics that all touched on or intersected with the need to create smaller and more precise cohorts, as well as the need to focus on phenotypic data as much as we do on genotypic data.

The full video appears below.

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Ginger.io: Putting the Patient-Provider connection at the center

A tool for outreach to patients produces unexpected benefits

This posting was written by guest author Julia Bernstein of Ginger.io. Ginger.io’s CEO Anmol Madan will be speaking on this topic at Strata Rx.

The traditional, office-based model for health care is episodic. The provider-patient relationship exists almost completely within the walls of the exam room, with little or no follow-up between visits. Data is primarily episodic as well, based on blood pressure reading done at a specific time or surveys administered there and then, with little collected out of the office. And even the existing data collection tools—paper diaries or clunky meters—are focused more on storing data that on connecting the patient and provider through that data in real time.

There is no way to get in touch when, for instance, a patient’s blood sugar starts varying wildly or pain levels change. The provider often depends on the patient reaching out to them. And even when a provider does put into place an outreach protocol, it is usually very crude, based on a general approach to managing a population as opposed to an understanding of a patient. The end result is a system that, while doing its best within a difficult setting, is by default reactive instead of proactive.

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Clinical discovery in the age of “Big Data”

Modern data processing tools, many of them open source, allow more clinical studies at lower costs

This guest posting was written by Yadid Ayzenberg (@YadidAyzenberg on Twitter). Yadid is a PhD student in the Affective Computing Group at the MIT Media Lab. He has designed and implemented cloud platforms for the aggregation, processing and visualization of bio-physiological sensor data. Yadid will speak on this topic at the Strata Rx conference.

A few weeks ago, I learned that the Framingham Heart Study would lose $4 million (a full 40 percent of its funding) from the federal government due to automatic spending cuts. This seminal study, begun in 1948, set out to identify the contributing factors to Cardiovascular Disease (CVD) by following a group of 5,209 men and woman and tracking their life style habits, performing regular physical examinations and lab tests. This study was responsible for finding the major risk factors for CVD, such as high blood pressure and lack of exercise. The costs associated with such large-scale clinical studies are prohibitive, making them accessible only to organizations with sufficient financial resources or through government funding.
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The promise of Big Data to the CMO

A game changer for a marketer to pinpoint what a customer wants, when they want it, and how they want to hear about it

By Michael Gold, Farsite

Michael Gold

Michael Gold

My 2 and a half year old daughter loves the Mickey Mouse Clubhouse. She watches episodes on TV and our iPad. She wears Minnie Mouse flip flops and giggles just about every time she sees anything with Mickey, Daisy, Goofy…you get the idea. And when she’s old enough to go to Disney World, Minnie might walk right up to her and say “Hi Jemma!” and give her a big hug.

Creating a personal interaction between a child and a beloved Disney character exemplifies the company’s recent initiative to deliver a personalized, hassle-free experience at their theme parks. 1 With the wireless tracking wristband ‘MagicBand,’ families are able to reserve spots in lines for popular attractions, purchase items at the parks, and unlock their hotel rooms. The MagicBand is part of the MyMagic+ system, which enables Disney to collect data on visitors’ purchasing habits and real-time location, among other things. Disney will use this vast trove of information to deliver a personalized experience at the parks and tailor marketing messages and promotions.

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On reading Mike Barlow’s “Real-Time Big Data Analytics: Emerging Architecture”

Barlow's distilled insights regarding the ever evolving definition of real time big data analytics

Reading Barlow on a Sunday Afternoon

Reading Barlow on a Sunday afternoon

During a break in between offsite meetings that Edd and I were attending the other day, he asked me, “did you read the Barlow piece?”

“Umm, no.” I replied sheepishly. Insert a sidelong glance from Edd that said much without saying anything aloud. He’s really good at that.

In my utterly meager defense, Mike Loukides is the editor on Mike Barlow’s Real-Time Big Data Analytics: Emerging Architecture. As Loukides is one of the core drivers behind O’Reilly’s book publishing program and someone who I perceive to be an unofficial boss of my own choosing, I am not really inclined to worry about things that I really don’t need to worry about. Then I started getting not-so-subtle inquiries from additional people asking if I would consider reviewing the manuscript for the Strata community site. This resulted in me emailing Loukides for a copy and sitting in a local cafe on a Sunday afternoon to read through the manuscript.

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Your analytics talent pool is not made up of misanthropes

Tips for interacting with analytics colleagues

To quote Pride and Prejudice, businesses have for many years “labored under the misapprehension” that their analytics talent was made up of misanthropes with neither the will nor the ability to communicate or work with others on strategic or creative business problems. These employees were meant to be kept in the basement out of sight, fed bad pizza, and pumped for spreadsheets to be interpreted in the sunny offices aboveground.

This perception is changing in industry as the big data phenomenon has elevated data science to a C-level priority. Suddenly folks once stereotyped by characters like Milton in Office Space are now “sexy.” The truth is there have always been well-rounded, articulate, friendly analytics professionals (they may just like Battlestar more than you), and now that analytics is an essential business function, personalities of all types are being attracted to practice the discipline.

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