Deep Dive: State Assistance
this post is part of a series of in depth posts about specific modules in our Social Determinant Platform
In many cases, patients put off or delay seeking care when they are not sure how they can pay for it. According to Commonwealth Fund surveys, 20% of patients did not see a doctor because of cost, and 18% did not get a recommended test. Other studies have reiterated these results. Even with employer and government programs for providing coverage, many people end up with no coverage or too little coverage when they may qualify for more. As many as 1⁄3 of adults, are underinsured meaning that even with coverage, medical expenses may be greater than 5% of their annual income - with members delaying care or skipping medication as a result.
Even an individual receiving coverage through Medicare may be able to qualify for a comprehensive special needs plans (SNP) or elder service plans (ESP).
The Algorex Health State Assistance algorithm identifies if a person may or may not receive state or federal assistance in support of their insurance premium, out of pocket health care costs, or other type of assistance such as SNAP.
For example, an individual in Vermont, estimated to earn 138% of the Federal Poverty Limit (FPL), may qualify for Medicaid. While in North Carolina, that threshold is 44% FPL. In Tennessee, non-disabled adults are not eligible for coverage while families and the disabled are, albeit with different FPL thresholds. In Arrostock County of Maine, there are only four Medicare Advantage plans available and no plans marketed with a zero dollar premium. Meanwhile in New York, NY (Manhattan), there 38 available plans with 10 marketing a zero dollar premium. Between federal, state and local rules, advanced modeling can give value-based healthcare organizations tools to improve enrollment in their populations.
Understanding the Output
Algorex Health uses information about income, household size, and state-specific rules to determine the likelihood each patient receives government assistance.
The algorithm outputs are normalized to a discrete scale from 0-10 with individuals over 5 considered likely to receive government assistance.
Tuning and Localization
The Algorex Health State Assistance algorithm includes weights determined through the analysis of state/local specific rules and past experience with Algorex Health engagements.
About this series
You can find all of the blog posts in our SDOH Deep Dive series at SDOH Deep Dive Series