How do we motivate sustained behavior change when the external motivation disappears—like it's supposed to?
If you’ve ever tried to count calories, go on a diet, start a new exercise program, change your sleep patterns, spend less time sitting, or make any other type of positive health change, then you know how difficult it is to form new habits. New habits usually require a bit of willpower to get going, and we all know that that’s a scarce resource. (Or at least, a limited one.)
Change is hard. But the real challenge comes after you’ve got a new routine going—because now you’ve got to keep it going, even though your original motivations to change may no longer apply. Why keep dieting when you no longer need to lose weight? We’ve all had the idea at some point that we really should reward ourselves for that five-pound weight loss with a cupcake, right?
When the death of trust meets the birth of BYOD
Dr. Andrew Litt, Chief Medical Officer at Dell, made a thoughtful blog post last week about the trade-offs inherent in designing for both the security and accessibility of medical data, especially in an era of BYOD (bring your own device) and the IoT (internet of things). As we begin to see more internet-enabled diagnostic and monitoring devices, Litt writes, “The Internet of Things (no matter what you think of the moniker), is related to BYOD in that it could, depending on how hospitals set up their systems, introduce a vast array of new access points to the network. … a very scary thought when you consider the sensitivity of the data that is being transmitted.”
As he went on to describe possible security solutions (e.g., store all data in central servers rather than on local devices), I was reminded of a post my colleague Simon St.Laurent wrote last fall about “security after the death of trust.” In the wake of some high-profile security breaches, including news of NSA activities, St.Laurent says, we have a handful of options when it comes to data security—and you’re not going to like any of them.
If Health 2.0 meant adding devices, then the next wave means incorporating more than just technology
First there was health, which basically consisted of not dying, and also of being able to work and live alone (if need be) and generally function productively. Then there was Health 2.0, in which we added all kinds of gadgets—wrist bands, back bands, sleep monitors, calorie counters—in an attempt to quantify and alter our behavior patterns. But we were still completely focused on the body, and largely ignored the mind.
Health 3.0 is holistic. That means that it incorporates ideas not only about physical well-being, but also about mental well-being. It understands that the mind and body are deeply connected—even though there is still much we fail to understand about the brain. If nothing else, Health 3.0 takes into account that stress is a real thing, with real physical and chemical consequences. Reducing stress and seeking a life of balance is core to the next wave of health care.
In technology circles, we are at both an advantage and a disadvantage when it comes to implementing this next wave. Our advantage is that we understand and appreciate data; we’re prepared with our Health 2.0 sensors and accessories, prepared to deploy them in the name of something even newer and better. Our disadvantage is that, as in so many other industries, many of us still value workaholism and sweat equity. “Start-up” is practically a euphemism for lack of sleep, too much caffeine, and long hours in front of a monitor (hooray for you and your stand-up desk; you’re still probably awash in cortisol).
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.
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.
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.
Hypothesis-free data analysis turns up unexpected incidences of illness
This posting was written by guest author Arijit Sengupta, CEO of BeyondCore. Arijit will speak at Strata Rx 2013 on the kinds of data analysis discussed in this post.
Much of the effort in health reform in the United States has gone toward recruiting 18-to-35 year olds into the insurance pool so that the US economy and insurers can afford the Affordable Care Act (ACA). The assumption here is that health care costs will be less for this young population than for other people, but is this true? Our recent analysis of 6.8 million insured young adults, across 200,000 variable combinations, suggests that young adults may be more expensive to insure than we realize.
Our study shows a high occurrence of mental health diseases among 18-to-35 year olds who have insurance and therefore more affordable access to medical care. Moreover, expenses associated with mental health conditions are very high, especially when coupled with a physical ailment. As the previously uninsured 18-to-35 year olds get access to affordable care, we may see a similarly high rate of mental health diagnoses among this population. The bad news is that the true costs of insuring 18-to-35 year olds might be much higher than previously suspected. The good news is that previously undiagnosed and untreated mental health conditions may now actually get diagnosed and treated, creating a significant societal benefit.
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.
An Interview with Julie Steele
A week or two ago, I got to correspond with Danielle Brooks of Disruptive Women in Health Care about the work I do here at O’Reilly. The following interview is reprinted here with their kind permission.
Tell us about your work. What drew you to the area?
I have mostly worked as a book editor, until just a year or two ago. I was working on books about databases, machine learning, visualization, and other relevant topics when O’Reilly launched its Strata conference on data science, and so I became involved in that conference. But as Strata took off, it became apparent to us that certain communities — and certain types of data — were special. Health care is one of those areas: the insights that data analysis can give us about ourselves and the things that ail us are enormous, but the risks of over-sharing and the resulting constraints such as HIPAA also present very real challenges.
In 2012, O’Reilly decided to launch a new edition of its data science conference to focus on health care, and that’s how Strata Rx was born. I was asked to become its Program Chair, along with Colin Hill, CEO of GNS Health care, and so I have spent that last 18 months learning everything I can about the (very complicated!) health care industry. Colin and I are great partners because of the complimentary backgrounds we bring together — Colin from the health care industry side and myself from the technology side. Ultimately, that’s what Strata Rx aims to do, too: we hope that by bringing together professionals from all parts of the industry (payers, providers, researchers, analysts, advocates, developers, investors, and caregivers, just to name a few) we can begin to solve some of the large and complex problems facing us in this area.
How our vision for this important conference is shaping the program we hope to present, and how you can get involved
After a strong inaugural event in October 2012, Strata Rx is heading into its second year. My fellow chair, Colin Hill, and I have spent a lot of time thinking about and discussing what we’d like to see on the program this year, and I thought I’d share some of those thoughts for anyone considering submitting a proposal or attending the event. (The Call for Proposals is currently open until April 10.)
One of the most interesting challenges in creating a program about data science in healthcare has been deciding what to leave out. Topics like genomics and cancer research are so vast and complex that they can and do have entire conferences about just them. While we won’t reject a talk for centering on a topic like this, it has to be relevant to one of our larger goals, as well.
What we hope to accomplish with Strata Rx
So what are those larger goals? Well, here are a few of the key ones.
Promote dialog across silos
Right now, there are already a lot of niche conferences for specific groups in healthcare. There are events for specific areas of research, such as oncology and genomics, as previously mentioned. There are also events for specific kinds of people, like pharmaceutical reps, or insurance providers. Those conferences that do cut across the industry are only for one level of people, such as Chief Officers.
We want Strata Rx to convene a broad swath of people with an interest and a stake in the healthcare system: researchers, funders, providers, application developers, patient advocates, board members, insurers, IT staff, legislators, and everyone in between. By starting conversations among these different specialists, and by combining their relative expertise, we believe we can build a stronger community that is better able to solve problems.
We aim to be fire-starters, igniting connections and conversations.
An interview with Fred Smith of the CDC on their open content APIs.
Health care data liquidity (the ability of data to move freely and securely through the system) is an increasingly crucial topic in the era of big data. Most conversations about data liquidity focus on patient data, but other kinds of information need to be able to move freely and securely, too. Enter several government initiatives, including efforts at agencies within the Department of Health and Human Services (HHS) to make their content more easily available.
Fred Smith is team lead for the Interactive Media Technology Team in the Division of News and Electronic Media in the Office of the Associate Director for Communication for the U.S. Centers for Disease Control and Prevention (CDC) in Atlanta. We recently spoke by phone to discuss ways in which the CDC is working to make their information more “liquid”: easier to access, easier to repurpose, and easier to combine with other data sources.
Which data is available from the CDC APIs?
Fred Smith: In essence, what we’re doing is taking our unstructured web content and turning it into a structured database, so we can call an API into it for reuse. It’s making our content available for our partners to build into their websites or applications or whatever they’re building.
Todd Park likes to talk about “liberating data” — well, this is liberating content. What is a more high-value dataset than our own public health messaging? It incorporates not only HTML-based text, but also we’re building this to include multimedia — whether it’s podcasts, images, web badges, or other content — and have all that content be aware of other content based on category or taxonomy. So it will be easy to query, for example: “What content does the CDC have on smoking prevention?”