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Will Data Aggregation For Precision Medicine Compromise Patient Privacy?

Posted on April 10, 2017 I Written By

Anne Zieger is a healthcare journalist who has written about the industry for 30 years. Her work has appeared in all of the leading healthcare industry publications, and she's served as editor in chief of several healthcare B2B sites.

Like anyone else who follows medical research, I’m fascinated by the progress of precision medicine initiatives. I often find myself explaining to relatives that in the (perhaps far distant) future, their doctor may be able to offer treatments customized specifically for them. The prospect is awe-inspiring even for me, someone who’s been researching and writing about health data for decades.

That being the case, there are problems in bringing so much personal information together into a giant database, suggests Jennifer Kulynych in an article for OUPblog, which is published by Oxford University Press. In particular, bringing together a massive trove of individual medical histories and genomes may have serious privacy implications, she says.

In arguing her point, she makes a sobering observation that rings true for me:

“A growing number of experts, particularly re-identification scientists, believe it simply isn’t possible to de-identify the genomic data and medical information needed for precision medicine. To be useful, such information can’t be modified or stripped of identifiers to the point where there’s no real risk that the data could be linked back to a patient.”

As she points out, norms in the research community make it even more likely that patients could be individually identified. For example, while a doctor might need your permission to test your blood for care, in some states it’s quite legal for a researcher to take possession of blood not needed for that care, she says. Those researchers can then sequence your genome and place that data in a research database, and the patient may never have consented to this, or even know that it happened.

And there are other, perhaps even more troubling ways in which existing laws fail to protect the privacy of patients in researchers’ data stores. For example, current research and medical regs let review boards waive patient consent or even allow researchers to call DNA sequences “de-identified” data. This flies in the face of conventional wisdom that there’s no re-identification risk, she writes.

On top of all of this, the technology already exists to leverage this information for personal identification. For example, genome sequences can potentially be re-identified through comparison to a database of identified genomes. Law enforcement organizations have already used such data to predict key aspects of an individual’s face (such as eye color and race) from genomic data.

Then there’s the issue of what happens with EMR data storage. As the author notes, healthcare organizations are increasingly adding genomic data to their stores, and sharing it widely with individuals on their network. While such practices are largely confined to academic research institutions today, this type of data use is growing, and could also expose patients to involuntary identification.

Not everyone is as concerned as Kulynych about these issues. For example, a group of researchers recently concluded that a single patient anonymization algorithm could offer a “standard” level of privacy protection to patient, even when the organizations involved are sharing clinical data. They argue that larger clinical datasets that use this approach could protect patient privacy without generalizing or suppressing data in a manner that would undermine its usefulness.

But if nothing else, it’s hard to argue Kulynych’s central concern, that too few rules have been updated to reflect the realities of big genomic and medical data stories. Clearly, state and federal rules  need to address the emerging problems associated with big data and privacy. Otherwise, by the time a major privacy breach occurs, neither patients nor researchers will have any recourse.

What Data Do You Need in Order to Guide Behavioral Change?

Posted on June 2, 2016 I Written By

Andy Oram is an editor at O'Reilly Media, a highly respected book publisher and technology information provider. An employee of the company since 1992, Andy currently specializes in open source, software engineering, and health IT, but his editorial output has ranged from a legal guide covering intellectual property to a graphic novel about teenage hackers. His articles have appeared often on EMR & EHR and other blogs in the health IT space. Andy also writes often for O'Reilly's Radar site ( and other publications on policy issues related to the Internet and on trends affecting technical innovation and its effects on society. Print publications where his work has appeared include The Economist, Communications of the ACM, Copyright World, the Journal of Information Technology & Politics, Vanguardia Dossier, and Internet Law and Business. Conferences where he has presented talks include O'Reilly's Open Source Convention, FISL (Brazil), FOSDEM, and DebConf.

This is an exciting time for the health care field, as its aspirations toward value-based payments and behavioral responses to chronic conditions converge on a more and more precise solution. Dr. Joseph Kvedar has called this comprehensive approach connected health and has formed both a conference and a book around it. BaseHealth, a predictive analytics company in healthcare, has teamed up with TriVita to offer a consumer-based service around this approach, which combines access to peer-reviewed research with fine-tuned guidance that taps into personal health and behavioral data and leverages the individual interests of each participant.

I have previously written about BaseHealth’s assessment engine, which asks individuals for information about their activities, family history, and health conditions in order to evaluate their health profile and risk for common diseases. TriVita is a health coaching service with a wide-ranging assessment tool and a number of products, including cutely named supplements such as Joint Complex and Daily Cleanse. TriVita’s nutritionists, exercise coaches, and other staff are overseen by physicians, but their service is not medical: it does not enter the heavily regulated areas where clinicians practice.

I recently talked with BaseHealth’s CEO, Prakash Menon, and Dan Hoemke, its Vice President of Business Development. They describe BaseHealth’s predictive analytics as input that informs TriVita’s coaching service. What I found interesting is the sets of data that seem most useful for coaching and behavioral interventions.

In my earlier article, I wrote, “BaseHealth has trouble integrating EHR data.” Menon tells me that getting this data has become much easier over the past several months, because several companies have entered the market to gather and combine the data from different vendors. Still, BaseHealth focuses on a few sources of medical data, such as lab and biometric data. Overall, they focus on gathering data required to identify disease risk and guide behavior change, which in turn improves preventable conditions such as heart disease and diabetes.

Part of their choice springs from the philosophy driving BaseHealth’s model. Menon says, “BaseHealth wants to work with you before you have a chronic condition.” For instance, the American Diabetes Association estimated in 2012 that 86 million Americans over the age of 20 had prediabetes. Intervening before these people have developed the full condition is when behavioral change is easiest and most effective.

Certainly, BaseHealth wants to know your existing medical conditions. So they ask you about them when you sign up. Other vital signs, such as cholesterol, are also vital to BaseHealth’s analytics. Through a partnership with LabCo, a large diagnostics company in Europe, they are able to tap into lab systems to get these vital signs automatically. But users in the United States can enter them manually with little effort.

BaseHealth is not immune to the industry’s love affair with genetics and personalization, either. They take about 1500 genetic factors into account, helping them to quantify your risk of getting certain chronic conditions. But as a behavioral health service, Menon points out, BaseHealth is not designed to do much with genetic traits signifying a high chance of getting a disease. They deal with problems that you can do something about–preventable conditions. Menon cites a Health 2.0 presentation (see Figure 1) saying that our health can, on average, be attributed 60 percent to lifestyle, 30 percent to genetics, and 10 percent to clinical interventions. But genetics help to show what is achievable. Hoemke says BaseHealth likes to compare each person against the best she can be, whereas many sites just compare a user against the average population with similar health conditions.

Relative importance of health factors

Figure 1. Relative importance of health factors

BaseHealth gets most of its data from conditions known to you, your environment, family history, and more than 75 behavioral factors: your activity, food, over-the-counter meds, sleep activity, alcohol use, smoking, several measures of stress, etc. BaseHealth assessment recommendations and other insights are based on peer-reviewed research. BaseHealth will even point the individual to particular studies to provide the “why” for its recommendations.

So where does TriVita fit in? Hoemke says that BaseHealth has always stressed the importance of human intervention, refusing to fall into the fallacy that health can be achieved just through new technology. He also said that TriVita fits into the current trend of shifting accountability for health to the patient; he calls it a “health empowerment ecosystem.” As an example of the combined power of BaseHealth and TriVita, a patient can send his weight regularly to a coach, and both can view the implications of the changes in weight–such as changes in risk factors for various diseases–on charts. Some users make heavy use of the coaches, whereas others take the information and recommendations and feel they can follow their plan on their own.

As more and more companies enter connected health, we’ll get more data about what works. And even though BaseHealth and TriVita are confident they can achieve meaningful results with mostly patient-generated data, I believe that clinicians will use similar techniques to treat sicker people as well.

Healthcare IT at CES

Posted on January 19, 2012 I Written By

John Lynn is the Founder of the blog network which currently consists of 10 blogs containing over 8000 articles with John having written over 4000 of the articles himself. These EMR and Healthcare IT related articles have been viewed over 16 million times. John also manages Healthcare IT Central and Healthcare IT Today, the leading career Health IT job board and blog. John is co-founder of and John is highly involved in social media, and in addition to his blogs can also be found on Twitter: @techguy and @ehrandhit and LinkedIn.

While I definitely had quite a bit of excitement over this year’s CES and Digital Health Summit, I have to admit that I ended up leaving CES a bit disappointed. I’m trying to decide if it being the fifth year I’ve attended CES is making me immune to the hype that surrounds the event or if I’ve just been going to too many conferences in general and so I’ve already heard much of the hype. At the end of the day, I describe this year’s CES as incremental versus trans formative.

There were a few exceptions of things that caught my eye while navigating the CES circus that are worth mentioning.

Ion Proton Genetic Sequencer
Probably the most amazing thing I saw for healthcare was the Life Technologies Ion Proton Genetic Sequencer. Plus, I’m not alone with this feeling. Dan Costa of PC Mag called it “The Coolest Thing I Saw at CES 2012.” To be quite frank, it is pretty amazing. It’s part of the amazing movement happening in bringing genomic data to healthcare.

The Ion Proton Genetic Sequencer (they need a better name) is awesome cause you can do a full genome in a day on a machine that costs about the same as an MRI machine. Plus, I personally think they’re just getting started on optimizing the technology. As they continue to improve the technology the cost of the machine and the time and cost to do the analysis will continue to drop. We still don’t know exactly how to use the genomic data in healthcare, but machines like this are going to make it possible for us to find new ways to use this data for good.

I still can’t help but imagine an EHR having all of our genomic data available to it.

Probably the coolest general technology and innovation that I saw at CES was called Liquipel. Liquipel is a technology that makes your device repel water using a nano coating. The best way to understand how it works is to check out some of the Liquipel videos and I’ll embed one below that gives a nice overview.

Of course, they have the disclaimer that it should never be submerged in water, but it was amazing to see it repel the water and still work. Plus, probably the coolest demonstration they did was with a Kleenex. They’d applied the nano-coating to a Kleenex and then they placed it in water. You’d think it would shrivel up and absorb the water. Nothing. I then asked if I could touch the Kleenex to see if I could feel the coating. Nothing. It felt like a Kleenex.

Many health IT people would love this technology. Then, it wouldn’t be such a concern to put your iPad next to the sink in the exam room. I wonder if the nano technology can do anything with infection control with devices. I imagine it doesn’t solve that issue.

I’m sure many are wondering how they can get their device treated with Liquipel. Right now they said you have to drop it by their office in California to get it done over a lunch or something. However, they’re working with phone manufacturers to get their technology in every phone. Pretty amazing stuff.

John Sculley
Another highlight of CES for me was the chance to hear John Sculley talk at the Digital Health Summit. I can’t say he said anything too groundbreaking. Although, he did say that health IT companies should stop focusing their revenue model on corporate health programs. I found that interesting. The most interesting comment came from colleague Dan Munro after John Sculley’s talk. He commented how interesting it was that so many of these older ex-CIO’s of major tech companies are getting into healthcare. I carried the thought through for Dan that as you age, you start to care about healthcare a lot more than you did when you were younger and healthier. I wonder if we’ll see this trend continue as more tech people get older and start to care more about healthcare.