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Using AI to help assess the impact of Medicaid cuts 


How to use the value in the data

Some takeaways from what we learned analyzing and thinking through how to use the data:

  1. We see clear evidence of a gap between the ratings people give Medicaid and the reality of what they tell us about their experience.

    1. Based on this "rating inflation," a strong case can be built to stop or delay the proposed cuts 

  2. The idea of rating inflation is not a new issue to the conversation. It has been approached as in play but not quantifiable.

    1. Until now. This report not only offers data to support the idea of rating inflation, it uses people’s own words to do it.

  3. If I were preparing testimony, talking points about Medicaid for a panel, etc; I’d use this data to speak truth to power about quality of care.

  4. If I were preparing a communications plan for other at-risk Medicaid recipients, I’d integrate copy themes that make them feel safe with rating services without fear of loss or retaliation.

    1. “ We change the study participant names and hide their identifiable information. Please rate your experience by what really happened, otherwise we can’t fix it,” or something better than that.

  5. Broaden the scope to include more than at-risk users

    1. If evidence of this inflation gap holds with other user groups, it offers an opportunity to advocate for improved services with lawmakers and Medicaid professionals.

  6. User interviews, though more involved, will result in richer responses better targeted to the question

    1. The back and forth of a conversation gives us a stronger platform to extract better data. 

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