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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.

Personalized Medicine Survey and Infographic

Posted on May 11, 2016 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.

SAP Healthcare has gotten together with Oxford Economics in a survey of 120 healthcare professionals to understand what they’re doing with personalized medicine. You can check out this whitepaper which details the findings of the research or check out the personalized medicine infographic below.

I was most interested in the tools section (near the end) of the infographic below. It’s not surprising that most healthcare professionals say that big data capture and storage tools and predictive analytics tools are essential to personalized medicine. I think that’s a reflection of where we’re at with personalized medicine. We’ll know we’ve entered the next phase of personalized medicine once more organizations want to include collaboration tools and decision support tools in their efforts.

What pieces of this infographic or the research linked above stood out to you?

Personalized Medicine Iinfographic

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 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.

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.