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

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.

About the author

Andy Oram

Andy Oram

Andy Oram writes and edits documents about many aspects of computing, ranging in size from blog postings to full-length books. Topics cover a wide range of computer technologies: data science and machine learning, programming languages, Web performance, Internet of Things, databases, free and open source software, and more. My editorial output at O'Reilly Media included the first books ever published commercially in the United States on Linux, the 2001 title Peer-to-Peer (frequently cited in connection with those technologies), and the 2007 title Beautiful Code. He is a regular correspondent on health IT and health policy for HealthcareScene.com. He also contributes to other publications about policy issues related to the Internet and about 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.

   

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