Health Data

Big data is transforming and disrupting healthcare. The challenges and opportunities of this disruption are immense and exciting. The O’Reilly Strata blog raises awareness of technology issues we think are interesting and important, sheds light on the people doing interesting things at the intersection of big data and healthcare, and crystallizes the critical issues around emerging technologies. Understanding these emerging technologies—and how they will transform healthcare—has never been so important.

Visualizing Health IT: A holistic overview

O'Reilly report covers major trends and tries to connect the neurons

If visualization is key to comprehending data, the field of health IT calls for better visualization. I am not talking here of pretty charts and animations. I am talking, rather, of a holistic, unified understanding of the bustle taking place in different corners of health: the collection and analysis of genetic data, the design of slim medical devices that replace refrigerator-sized pieces of equipment, the data crunching at hospitals delving into demographic data to identify at-risk patients.

There is no dearth of health reformers offering their visions for patient engagement, information exchange, better public health, and disruptive change to health industries. But they often accept too freely the promise of technology, without grasping how difficult the technical implementations of their reforms would be. Furthermore, no document I have found pulls together the various trends in technology and explores their interrelationships.

I have tried to fill this gap with a recently released report: The Information Technology Fix for Health: Barriers and Pathways to the Use of Information Technology for Better Health Care. This posting describes some of the issues it covers.

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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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Healthcare Lessons from the Data Sages at Strata

Other industries can show health care the way

This article was written with Ellen M. Martin.

Most healthcare clinicians don’t often think about donating or sharing data. Yet, after hearing Stephen Friend of Sage Bionetworks talk about involving citizens and patients in the field of genetic research at StrataRx 2012, I was curious to learn more.

McKinsey points out the 300 billion dollars in potential savings from using open data in healthcare, while a recent IBM Institute of Business Value study showed the need for corporate data collaboration.

Also, during my own research for Big Data in Healthcare: Hype and Hope, the resounding request from all the participants I interviewed was to “find more data streams to analyze.”

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Health hackathon brings to life agile solutions for unmet needs

Winners of the Blue Button Innovation Challenge

I think the main achievement of hackathons can be measured not by what apps are developed–reportedly, few are commercialized and maintained–but by people who find each other. The Blue Button Innovation Challenge brought together a lot of professionals who had never met before, and many formed teams that created really fun and useful apps that make you think, “Why hasn’t anyone done this yet?”

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Health technology brings care plans alive

Finalists at Merck|Heritage Provider Network Innovation Challenge

Challenges and hackathons are meant to surprise you. If the winner is a known leader in the field with lists of familiar credentials festooning the team’s resumes, there was no point to starting the challenge in the first place.

Pharmaceutical company Merck and the Heritage Provider Network, the largest physician-led health network in the US, were looking for something new when they launched their challenge on diabetes and heart disease. These conditions are virtual epidemics, world-wide.

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How does the medical device tax affect innovation and the health/medical startup scene?

Comparative effectiveness research is key to reform

When the Affordable Care Act (ACA) was passed on a party line vote several years ago, it included a somewhat controversial provision to tax, at 2.3% starting in 2013, the sale of any medical device classified by the IRS as being taxable. The list of taxable devices includes a wide variety of products such as defibrillators, dental instruments, pacemakers, coronary stents, artificial hips, joints, and knees, surgical gloves, irradiation equipment, and advanced imaging technology. But it doesn’t stop there—patient monitoring, anesthesiology equipment, infusion pumps, and other hospital operating room digital devices are included in the IRS’s taxable device category. “Consumer” devices such as glucose monitors and potentially many upcoming “wearables” will likely also get taxed either now or soon. That’s where things get difficult for innovators and investors who want to offer next generation devices.

The medical device tax was levied partially to hinder the (over) prescription of medical devices. You and I are most familiar with devices like monitoring instruments or mobile phone sensors, but most dollars are spent on devices like stents, replacement knees, spinal fusion screws, proton beam accelerators, PET/CT scanners, etc. About $200 billion is spent on medical devices per year (about one-third the amount spent on pharmaceutical drugs). The idea behind the tax was twofold. One the one hand, Congress hoped to reduce health spending caused by the overuse of devices by taxing them. But in tandem, the influx of new patients into the health care system is expected to create more sales and revenue for device companies, allowing them to compensate for the excise the tax while bringing in more revenue for Uncle Sam.

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Using Big Data and Game Play to Improve Mental Fitness

Digital tools and data analysis to stay sharp, stay well, and overcome illness

This article was written together with Ellen M. Martin and Melinda Speckmann.

Games have been part of human culture for millennia. It is no surprise that elements of play can be powerful digital tools to grab our attention and keep us on a path to taking care of ourselves and others.

Big data is already behind brain games. The use of big data is becoming increasingly mainstream in health play applications. Once we are drawn in, game play (with big data under the hood) can help us to:

  1. Stay sharp,
  2. Stay well, and
  3. Overcome illness.

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Finding the Meaning in “Meaningful Use”

The 30,000-foot view and the nitty gritty details of working with electronic health data

Ever wonder what the heck “meaningful use” really means? By now, you’ve probably heard it come up in discussions of healthcare data. You might even know that it specifically pertains to electronic health records (EHRs). But what is it really about, and why should you care?

If you’ve ever had to carry a large folder of paper between specialists, or fill out the same medical history form in different offices over and over—with whatever details you happen to remember off the top of your head that day—then you already have some idea of why EHRs are a desirable thing. The idea is that EHRs will lead to better care—and better research data—through more complete and accurate record-keeping, and will eventually become part of health information exchanges (HIEs) with features like trend analysis and push-notifications. However, the mere installation of EHR software isn’t enough; we need not just cursory use but meaningful use of EHRs, and we need to ensure that the software being used meets certain standards of efficiency and security.

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The democratization of medical science

An interview with Ash Damle of Lumiata on the role of data in healthcare.

Vinod Khosla has stirred up some controversy in the healthcare community over the last several years by suggesting that computers might be able to provide better care than doctors. This includes remarks he made at Strata Rx in 2012, including that, “We need to move from the practice of medicine to the science of medicine. And the science of medicine is way too complex for human beings to do.”

So when I saw the news that Khosla Ventures has just invested $4M in Series A funding into Lumiata (formerly MEDgle), a company that specializes in healthcare data analytics, I was very curious to hear more about that company’s vision. Ash Damle is the CEO at Lumiata. We recently spoke by phone to discuss how data can improve access to care and help level the playing field of care quality.

Tell me a little about Lumiata: what it is and what it does.

Lumiata network graph of diagnosis interrelation.

A Lumiata network graph of diagnosis interrelation.

Ash Damle: We’re bringing together the best of medical science and graph analytics to provide the best prescriptive analysis to those providing care. We data-mine all the publicly available data sources, such as journals, de-identified records, etc. We analyze the data to make sure we’re learning the right things and, most importantly, what the relationships are among the data. We have fundamentally delved into looking at that whole graph, the way Google does to provide you with relevant search results. We curate those relationships to make sure they’re sensible, and take into account behavioral and social factors.

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23andMe flap at FDA indicates fundamental dilemma in health reform

We must go beyond hype for incentives to provide data to researchers

The FDA order stopping 23andM3 from offering its genetic test kit strikes right into the heart of the major issue in health care reform: the tension between individual care and collective benefit. Health is not an individual matter. As I will show, we need each other. And beyond narrow regulatory questions, the 23andMe issue opens up the whole goal of information sharing and the funding of health care reform.

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