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Wednesday
Apr252018

Five Questions for Erin Benson and Rich Morino with LexisNexis Health Care: Post-Webinar Interview

Five Questions for Erin Benson and Rich Morino with LexisNexis Health Care: Post-Webinar Interview
 

Last week, Erin Benson, Director Marketing Planning and Rich Morino, Director, Strategic Solutions, LexisNexis Health Care, participated in a Healthcare Web Summit webinar discussion on opportunities for health plans to leverage social determinants of health data to attain quality goals while managing cost and enhancing member experience.  If you missed this engaging webinar presentation, watch the On-Demand version here. After the webinar, we interviewed Erin and Rich on five key takeaways from the webinar:

 

1. What are some of the ways that member health is impacted on a daily basis by social, economic and environmental factors?

 

Erin Benson and Rich Morino: The environment in which a person lives impacts their likelihood to develop health conditions as well as their likelihood to effectively manage those conditions. Care recommendations need to be a good fit for a member’s environment, not just their medical condition. If recommendations won’t work within the person’s physical environment, aren’t affordable or conveniently located, and are provided in a way that is hard for the member to understand, they won’t be effective at improving health. Studies support this fact. For example, 75-90% of primary care visits are the result of stress-related factors (JAOA Evaluating the Impact of Stress on Systemic Disease: The MOST Protocol in Primary Care). Money, work and family responsibilities – all reflective of social determinants of health -- are cited as the top three causes of stress (APA 2015).

 

2. We've heard reference to aggregating data at the zip code level for use in personalizing care for members. However, this is one of your top five myths about socio determinants of health. Can you tell us more?

 

Erin Benson and Rich Morino: While aggregate data can be useful in certain capacities, it isn’t recommended as a best practice for personalizing care. Within a single zip code, it is not unusual to see variance in income levels, crime rates and other factors impacting an individual’s neighborhood and built environment, so we recommend looking at an individual’s neighborhood from the perspective of their specific address. Focusing on zip code alone also ignores the influences of education, economic stability and social and community context so we recommend incorporating these other social determinants of health into decision-making in order to view the member holistically and create a more comprehensive plan of care outreach.  

 

3. Can you briefly explain why previous generations of SDOH have failed to improve health outcomes?

 

Erin Benson and Rich Morino: There are two primary reasons why previous generations of SDOH have failed to improve health outcomes, data and workflow.   In order to get sufficient value, the data needs to address all 5 categories of SDOH to properly draw useful insights.  The data should also be at the member level, and address who the member’s family and close associations.  Without that information, we cannot tell if someone is socially isolated or living with caregivers, for instance.

 

The second reason why previous generations of SDOH have failed is how they are deployed in the workflow.  An example would be a plan simply adding them to an existing claims-based model to achieve an increase in lift.  The lift is nice, but no changes in process are filtering down to the Care Management team interacting with the members.   In this scenario, a lot of value was ignored.

 

A better method would be if the plan also built models identifying members with barriers to improved health outcomes.  If you now apply this to your chronic or at-risk population you can determine not just who is sick and in need of help, but how to most likely achieve success in an intervention program.  Care Managers would immediately know the challenges to success, and what type of intervention program the member should be in enrolled in from the start.

 

4. One of the SDOH models to uncover health barriers referenced during your webinar was social isolation. Can you provide more context for us here?

 

Erin Benson and Rich Morino: Studies have shown that social isolation can increase risk of heart disease by 29% and stroke by 32% (New York Times How Social Isolation Is Killing Us). By understanding factors about an individual such as who else is living in the household with them, their predicted marital status, and how close their nearest relatives and associates live to them, healthcare organizations can identify who may be socially isolated. This allows care providers to ask the right questions to determine if that person needs access to social support systems such as support groups or community resources to improve their health outcomes.

 

5. What are some ways social determinants can help health plans enhance predictions and improve care management?

 

Erin Benson and Rich Morino: The most common way of utilizing SDOH data so far has been to incorporate it into existing claims-based predictive models to improve predictive accuracy or to use it to create new predictive models. The second use is for care management purposes and this is where social determinants of health can be truly transformational. We recommend as a best practice to use social determinants of health insights to also build models that identify health barriers. The combination of models allows healthcare organizations to better stratify the risk of their members and then better tailor care to their medical and social needs.

 

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