A whole-person view reveals individual needs

For many people, the health care system is disjointed and confusing. It’s easy to get overwhelmed and frustrated, which leads to disengagement. This is literally bad for one’s health. To make matters even worse for consumers, they are assuming more of the financial risk than ever before to manage their health.

This comes also at a time when consumer expectations are rising. They want a relevant, personalized experience, whether they’re shopping for sneakers or making a health care decision. Health plans must go beyond the traditional approach of claims and clinical data, looking at condition and risk level. We need to see an individual as more than their disease or health status.

By integrating all available data points, we get a whole-person view of an individual’s needs. We bring together claims and clinical data with non-health care data such as:

  • Program engagement history
  • Channel preferences
  • Lifestyle behaviors
  • Purchasing behaviors
  • Socioeconomics
  • Demographics/Psychographics
  • Social Media/Interactions
  • Member needs and wants

The objective is to use data and analytics to drive the right message to the right consumer. To optimize engagement, we must deliver the message at the right time and through the right channel.Blog graphic.pptx

Our approach to consumer analytics and insights

We recommend that health plans consider various attributes for their predictive analytics. Along with machine learning, we bring together three key frameworks to optimize engagement:

  1. Segmentation to understand a person’s health attitudes and level of health ownership (used to customize messaging)
  2. Predictive clinical model to identify clinical gaps
  3. “Propensity to enroll” to identify consumers most/least likely to engage in a specific program service

We score a population via the segmentation algorithm. Then we determine what percentage of that population resides within each segment. This framework allows us to:

  1. Identify opportunities within a population
  2. Customize messaging to each segment
  3. Track increases in health ownership over time

Successful population management begins with identifying individuals who are most likely to benefit from program services. We can identify opportunities before high-cost medical utilization occurs. Once identified, we reach out to the member with education through nurses and physicians, mailings and emails, newsletters and online resources. When we engage members close to the time a change in health happens, they are more likely to engage and take action. That’s the right message to the right person at the right time via the right channel.

At the AHIP Consumer Experience & Digital Health Forum on December 5, Teri Kaslow is presenting “Using Data and Analytics to Drive Consumer Engagement.” The session will provide actionable best practices on how data and analytics can be used to create customized campaigns and predict the next best action for a consumer.


About the Author:


Teri Kaslow, Vice President, Consumer Analytics and Insights, Optum

Teri is Vice President of Consumer Analytics and Insights in Optum’s Consumer Experience discipline. In this role, she is responsible for “knowing” the consumer in order to create more meaningful consumer interactions through an integrated, omni-channel and personalized approach. Her analytic team develops and implements custom analytical solutions involving predictive modeling, segmentation and offer management. Her research team focuses on collecting voice of consumer through primary research. Prior to joining Optum, Teri spent 20+ years in the credit risk analytics, marketing analytics and loyalty marketing disciplines with corporations such as Fair Isaac Corporation (FICO) and Carlson Companies.

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