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Season 1, Episode 10




“Data models have to be built to solve the true problem and not just address the symptoms of a problem

Social determinants of health (SDoH) is an important variable to consider when interacting with Medicare Advantage populations and as the healthcare system increases its use of digital technologies, such as telehealth visits, online prescriptions and digital communications, digital inequality is becoming a forefront issue within SDoH.

Digital inequality examines populations to determine internet accessibility, internet literacy, and how are people of different sub-populations are using the internet. All of these factors influence how plans should develop messaging and outreach programs. 

In order to collect this information and develop solutions to reach this population, health plan data scientists have to be conscientious of SDoH when building algorithms to avoid biases. For example, a new strategy for closing gaps is to offer telehealth services.  To test the efficacy of this strategy, a health plan may build a model to predict a member's likelihood of using telehealth services using cohorts of urban versus rural member data. If they don't include data related to digital inequality, they may come to a faulty conclusion that telehealth is a good solution for its rural members. By not accounting for digital inequality,  the telehealth program may result in poor engagement. By including SDoH data, health plans can identify micro-segments of the population and innovate solutions to address each cohort. In this case, the health plan might plan to partner with an internet service provider and pay for a portion of the internet fees to enable their offline member's access to telehealth visits. 

The takeaway is the data used to feed algorithms must be consistent with the population the logic is applied to. One way to ensure that models are built accurately is to ensure data science teams are well rounded and include not just highly technical data scientists but also social scientists to account for the specific needs and challenges of any given population.

In this episode we'll discuss:

1:24  Digital inequality
2:53  Data doesn't solve problems, people do
3:54  Example of bias in healthcare algorithms
6:12  Case study: tele-health in rural vs. urban population
8:33  Ethical implications for broad messaging
10:02  Checks and balances for data models
12:08  Asking the right questions: closing medication gaps
13:39  Identifying pharmacy and food desserts, and transportation insufficiencies

About Our Guest: Brandon Brooks is the Data Scientist for Advantasure’s Member Acquisition & Engagement technology, a machine learning solution for Medicare Advantage plans. With over ten years of experience in computational social science research, he is an expert in human communications and engagement in digital ecosystems using data and behavioral sciences. He’s worked in several industries including healthcare, energy, environmental, education and information technology. With an appreciation for details and analytics, Brandon is highly skilled at telling stories with data and enjoys working on complex problems without a clear solution.

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This podcast is sponsored by Advantasure—managed service and technology solutions for government-sponsored health plans.