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How Precision Medicine Can Save More Lives and Waste Less Money (Part 2 of 2)

Posted on August 10, 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 (http://oreilly.com/) 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.

The previous section of this article looked at how little help we get from genetic testing. Admittedly, when treatments have been associated with genetic factors, testing has often been the difference between life and death. Sometimes doctors can hone in with laser accuracy on a treatment that works for someone because a genetic test shows that he or she will respond to that treatment. Hopefully, the number of treatments that we can associate with tests will grow over time.

So genetics holds promise, but behavioral and environmental data are what we can use right now. One sees stories in the trade press all the time such as these:

These studies usually depend on straightforward combinations of data that are easy to get, either from the health care system (clinical or billing data) or from the patient (reports of medication adherence, pain level, etc.).

And we’ve only scratched the surface of the data available to us. Fitness devices, sensors in our neighborhoods, and other input will give us much more. We can also find new applications for data: for instance, to determine whether one institution is overprescribing certain high-cost drugs, or whether an asthma victim is using an inhaler too often, meaning the medication isn’t strong enough. We know that social factors, notably poverty (LGBTQ status is not mentioned in the article, but is another a huge contributor to negative health outcomes, due to discrimination and clinician ignorance) must be incorporated into models for diagnosis, prediction, and care.

President Obama promises that Precision Medicine features both genetics and personal information. One million volunteers are sought for DNA samples and information on age, race, income, education, sexual orientation, and gender identity.

There are other issues that critics have brought up with the Precision Medicine initiative. For instance, its focus on cure instead of prevention weakens its value for long-term public health improvements. We must also remember the large chasm between knowing what’s good for you and doing it. People don’t change notoriously unhealthy behaviors, such as smoking, even when told they are at increased risk. Some experts think people shouldn’t be told their DNA results.

Meanwhile, those genetic database can be used against you. But let’s consider our context, once again, in order to assess the situation responsibly. The data is being mined by police, but it’s probably not very useful because the DNA segments collected are different from what the police are looking for. Behavioral data, if abused, is probably more damning than genetic data.

Just as there are powerful economic forces biasing us toward genetics, social and political considerations weigh against behavioral and environmental data. We all know the weaknesses in the government’s dietary guidelines, heavily skewed by the food industry. And the water disaster in Flint, Michigan showed how cowardice and resistance by the guardians of public health to admitting changes raised the costs in public health measures. Industry lobbying and bureaucratic inertia work together to undermine the simplest and most effective ways of improving health. But let’s get behavioral and environmental measures on the right track before splurging on genetic testing.

How Precision Medicine Can Save More Lives and Waste Less Money (Part 1 of 2)

Posted on August 9, 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 (http://oreilly.com/) 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.

We all have by now seen the hype around the Obama Administration’s high-profile Precision Medicine Initiative and the related Cancer Moonshot, both of which plan to cull behavioral and genomic data on huge numbers of people in a secure manner for health research. Major companies have rushed to take advantage of the funds and spotlight what these initiatives offer. I think they’re a good idea so long as they focus on behavioral and environmental factors. (Scandalously, the Moonshot avoids environmental factors, which are probably the strongest contributors to cancer) . What I see is an unadvised over-emphasis on the genetic aspect of health analytics. This can be seen in announcements health IT vendors, incubators, and the trade press.

I can see why the big analytics firms are excited about increasing the health care field’s reliance on genomics: that’s where the big bucks are. Sequencing (especially full sequencing) is still expensive, despite dramatic cost reductions over the past decade. And after sequencing, analysis requires highly specialized expertise that relatively few firms possess. I wouldn’t say that genomics is the F-35 of health care, but is definitely an expensive path to our ultimate goals: reducing the incidence of disease and improving life quality.

Genomics offer incredible promise, but we’re still waiting to see just how it will help us. The problems that testing turns up, such as Huntington’s, usually lack solutions. One study states, “Despite the success of genome-wide association and whole-exome and whole-genome sequencing (WES/WGS) studies in revealing the DNA variants that underlie the genetic basis of disease, the development of effective treatments for most diseases has remained a challenge.” Another says, “Despite much progress in defining the genetic basis of asthma and atopy [predisposition to getting asthma] in the last decade, further research is required.”

When we think about the value of knowing a gene or a genetic deviation, we are asking: “How much does this help predict the likelihood that I’ll get the disease, or that a particular treatment will work on me?” The most impressive “yes” is probably in this regard to the famous BRCA1 and BRCA2 genes. If you are unlucky enough to have certain mutations of these gene, you have a 70% lifetime risk for developing breast or ovarian cancer. This is why testing for the gene is so popular (as well as contentious from an intellectual property standpoint), and why so may women act on the results.

However–this is my key point–only a small percentage of women who get these cancers have these genetic mutations. Most are not helped by testing for the genes, and a negative result on such a test gives them only a slight extra feeling of relief that they might not get cancer. Still, because the incidence of cancer is so high among the unfortunate women with the mutations, testing is worthwhile. Most of the time, though, testing is not worth much, because the genetic component of the disease is small in relation to lifestyle choices, environmental factors, or other things we might know nothing about.

So, although it’s hard enough already to say with any assurance that a particular gene or combination of genes is associated with a disease, it’s even harder to say that testing will make a big difference. Maybe, as with breast or ovarian cancer, a lot of people will get the disease for reasons unrelated to the gene.

In short, several factors go into determining the value of testing: how often a positive test guarantees a result, how often a negative test guarantees a result, how common the disease is, and more. Is there some way to wrap all these factors up into a single number? Yes, there is: it’s called the odds ratio. The higher an odds ratio, the more helpful (using all the criteria I mentioned) an association is between gene and disease, or gene and treatment. For instance, one study found that certain genes have a significant association with asthma. But the odds ratios were modest: 3.203 and 5.328. One would want something an order of magnitude higher to show running a test for the genes would have a really strong value.

This reality check can explain why doctors don’t tend to recommend genetic testing. Many sense that the tests can’t help or aren’t good at predicting most things.

The next section of this article will turn to behavioral and environmental factors.

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 (http://oreilly.com/) 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.

Genomic Medicine

Posted on February 3, 2016 I Written By

John Lynn is the Founder of the HealthcareScene.com 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 InfluentialNetworks.com and Physia.com. John is highly involved in social media, and in addition to his blogs can also be found on Twitter: @techguy and @ehrandhit and LinkedIn.

Last month I was lucky to lead a panel discussion on the topic of genomics in medicine at CES. I was joined on the panel by Andy De, Global Managing Director and General Manager for Healthcare and Life Sciences at Tableau, and Aaron Black, Director, Informatics, Inova Translational Medicine Institute. There certainly wasn’t enough time in our session to get to everything that was really happening in genomics, but Andy and Aaron do a great job giving you an idea of what’s really happening with genomics and the baseline of genomic data that’s being set for the future. You can see what I mean in the video below:

Be sure to see all of the conferences where you can find Healthcare Scene.

Envisioning the Future of Personalized Healthcare – Predictive Analytics – Breakaway Thinking

Posted on December 16, 2015 I Written By

John Lynn is the Founder of the HealthcareScene.com 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 InfluentialNetworks.com and Physia.com. John is highly involved in social media, and in addition to his blogs can also be found on Twitter: @techguy and @ehrandhit and LinkedIn.

The following is a guest blog post by Jennifer Bergeron, Learning and Development Manager at The Breakaway Group (A Xerox Company). Check out all of the blog posts in the Breakaway Thinking series.
Jennifer Bergeron
As 2016 approaches, individuals and organizations are beginning to consider their New Year’s resolutions. In order to make a plan for change, we imagine ways we might reach our goals: “If I eat more vegetables, I’ll lower my cholesterol and have more energy. But if I eat more vegetables and skip the donuts, I will see the same improvements faster!” What if someone could tell us exactly what action or combination of actions would produce which results over a specific timeframe?

In healthcare, predictive analytics is doing just that – providing potential outcomes based on specific factors. The process involves more than gathering statistics that define group results, but research of patient outcomes that allows predictions for individuals. Both technology and statistics are used to sift through these results and turn them into meaningful insights. Considering big data and a patient’s own health information, diagnoses can be more accurate, patient outcomes improved, and readmission rates reduced.

Predictive analytics is being used to help improve patient safety, predict crises in the ICU, uncover hereditary diseases, and reveal correlations between diseases. Researchers at the University of California Davis are using electronic health record (EHR) data to create an algorithm to warn providers about sepsis. Genomic tests, an example of precision medicine, are now available for at-home DNA testing, which allows individuals to discover hereditary traits through genetic sequencing. Correlations can be found between illnesses using EHR data. Thirty thousand Type 2 diabetic patients were studied to predict the risk of dementia.

BMC Medical Informatics & Decision Making reported on the use of EHRs as a prediction tool for readmission or death among adult patients. The model was built using specific criteria: candidate risk factors had to be available in the EHR system at each hospital, were routinely collected and available within 24 hours, and were predictors of adverse outcomes.

But predictive analytics can only be as good as the data it uses. Accurate, relevant data is necessary in order to receive valuable information from the algorithms. But the information can be hard to find, considering that healthcare data is expected to grow from 500 to 25,000 petabytes between 2012 and 2020 (A petabyte is a million billion bytes). In an effort to solve this challenge, more than $1.9 billion of capital has been raised since 2011 to fund companies that can gather, process, and interpret the increasing amount of information.

There are four principles to follow in order to optimize how information is captured, stored, and managed in the EHR system:

  • Ensure that leadership delivers the message to the organization about the importance and future impacts of the EHR system
  • Quickly bring staff up to speed
  • Measure and track the results of the staff’s learning
  • Continue to support and invest in EHR adoption.

The EHR stands as the first point of collection of much of this data. Given the importance of accuracy and consistency, it is critical that EHR education and use is made a priority in healthcare.

Xerox is a sponsor of the Breakaway Thinking series of blog posts. The Breakaway Group is a leader in EHR and Health IT training.

Dilbert DNA and Health Care Big Data Cartoon

Posted on December 11, 2015 I Written By

John Lynn is the Founder of the HealthcareScene.com 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 InfluentialNetworks.com and Physia.com. John is highly involved in social media, and in addition to his blogs can also be found on Twitter: @techguy and @ehrandhit and LinkedIn.

It’s Friday! Not just any Friday, but a Friday leading up to the holiday season. How’s your Christmas shopping going? You know on Fridays I often like to have a Fun Friday post. This week it comes in the form of a great Dilbert cartoon that incorporates genetic DNA testing with health care big data. Enjoy!

Dilbert Health Care Big Data Cartoon

Will We Be Maintaining Our Genomic Health Record?

Posted on May 4, 2015 I Written By

John Lynn is the Founder of the HealthcareScene.com 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 InfluentialNetworks.com and Physia.com. John is highly involved in social media, and in addition to his blogs can also be found on Twitter: @techguy and @ehrandhit and LinkedIn.

If you’re interested in Genomic Medicine like I am, be sure to check out my article on EMR and EHR called “When Will Genomic Medicine Become As Common As Antibiotics?” That’s a really interesting question that’s worth considering. We’re not there yet and won’t get there for a couple years. However, I think that genomic medicine will become as common as antibiotics and will have a massive impact on healthcare the way antibiotics have as well.

The article mentioned links to a genomics whitepaper that talks about a person’s genomic health record. I’d never heard the term before, but I’m definitely intrigued by the idea of everyone having their own genomic health record.

We’ve talked forever about people having a personal health record which they need to collect and maintain. Some people store it in a PHR on the web and others store it on a mobile phone. However, we’ve never really seen the personal health record take off. This is true for a number of reasons. The first is that it’s still quite difficult to aggregate your entire health record across multiple providers. I even read of one PHR that was paying doctors to provide them a patient’s record. The second problem is that patients don’t know what to do with all the records once they have them. Even if they go to their doctor and say they have their full patient record, the doctor hands them a stack of health history forms to fill out. Best case, they file a copy of the patients records in the chart (usually in some sort of PDF or paper copy).

Now let’s think about those challenges from the perspective of a genomic health record. If you’ve paid thousands of dollars for genomic tests and analysis, are you going to want to pay that again to the next doctor you see? No, they’re going to ask you for your copy of their genomic record and use that as part of your care. Patients won’t want to pay for another genomic test and it will be easier to get their record, so they’ll be more motivated to get and maintain it than they were with a simple personal health record. It’s pretty compelling to consider.

Some challenges and questions I have about how this will evolve. Will your PHR start to include your genomic health record or will it be something that’s stored separately? Will their be a standard for the genomic health record so that the doctor can easily use that record in the work they’re doing? Will the genomic health record be so large that it will have to be stored in the cloud?

What do you think of the concept of a genomic health record?