Widening the health care data lens with social determinants and the IoT

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Many people are surprised to learn that a person’s social, environmental and behavioral characteristics contribute significantly more to causes of premature death than access to health care and genetic predisposition. Just as surprising, then, should be the fact that we typically focus resources across our industry on what accounts for only a small percentage of health outcomes.

Over the last 7 to 10 years, we’ve depended on individual health histories in the form of health plan claims and electronic medical records (EMR) to help our efforts to understand and influence population health. But if medical care makes up such a small part of overall health, it raises the question: Can we look at people in a more holistic way, taking into account their social, environmental and behavioral factors?

When it comes to understanding people, incorporating social determinants of health (SDOH) information provides meaningful context to help identify how best to engage with them. SDOH attributes give us important pieces of information on how to improve overall health and well-being, especially given the lack of clinical information on individuals who have little interaction with the health care system.

The big picture is a better picture

Social determinants aren’t a new concept. But they are getting more attention in the health care industry. That’s partially because the data are more readily available than ever before. It’s also because we now see how important SDOH truly are.

Social determinants are so important they’re now the focus of newly proposed ICD-10 codes (code sets providers use to classify diagnoses and symptoms when submitting payer claims for reimbursement). UnitedHealth Group and the American Medical Association believe the new codes will improve the way referrals are made to social services. That, they say, will lead to better care.

Even more information is becoming available through the Internet of Things (IoT). Wearables and other internet-connected devices can help providers stay a step ahead of chronic conditions such as diabetes and heart failure by providing more complete data in real time. For example, blood tests can tell us if glucose levels are healthy. And daily weights can signal worsening heart failure. But those numbers represent single moments in time. IoT and wearable device data can alert members of a care team to problems and prompt them to take quick corrective actions. They can move us from episodic to continuous care without increasing time in a doctor’s office.

When managed with the same care as claims and clinical data, information consumers agree to share for health purposes — like wearable device, demographic, health survey or behavioral data — provides more in-depth understanding of personal preferences and motivations, and helps us create more effective engagement strategies. An engaged person is more likely to commit to the behavior change that will have a lasting impact on their overall health and well-being.

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Key pieces of the health puzzle

Here’s a practical way to view all of this looking at cardiovascular disease, which costs our health care system $199 billion each year. We know that many of the heart attacks and strokes that occur are preventable. As we work to minimize these events for people, information about whether taking certain drugs lowers blood pressure and cholesterol will likely be useful.

Factors even more vital, though, may be whether people smoke, eat healthy foods and are physically active. And those behaviors may depend on education, occupation and even their zip code. An unsafe neighborhood or lack of access to a gym, for example, may discourage people from walking for exercise.

Factoring in data outside of health system walls holds great potential for improving overall health. By understanding SDOH and using new data in an ethical, safe and transparent way, health care stakeholders can help change behavior and gain insights to deliver more personalized care that results in a better consumer experience and improved outcomes. That can lead to improved operational efficiency and help manage costs in the process for everyone.

To learn more about the powerful impact nontraditional data can have on health care, view our latest article, previously see in Harvard Business Review.

 About the author:

Steve Griffiths HeadshotSteve Griffiths, PhD, MS

Senior Vice President, Chief Operating Officer

Optum Enterprise Analytics

Steve Griffiths has more than 20 years’ experience in health analytics management and currently heads up the Optum Enterprise Analytics organization. His main focus is driving growth and innovation through Optum products and services. Steve has a master’s degree in biostatistics from the University of Washington and a PhD in health services research, policy and administration from the University of Minnesota.

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