Re-evaluating our industries
Shining a light on racial disparities and issues of equity
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Exposing the causes of health inequities
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Healthcare utilization among Medicare FFS beneficiaries living in historically redlined neighborhoods
We present our key findings from a look at healthcare utilization among Medicare fee-for-service beneficiaries living in historically redlined neighborhoods.
The insurance industry’s renewed focus on disparate impacts and unfair discrimination
Insurers need to adapt to rising demand for transparency and accountability on how their business practices contribute to potential systemic societal inequities.
Racial disparities in preventive services for Medicare FFS beneficiaries with type 2 diabetes
An examination of racial disparities in preventive services for Medicare beneficiaries with type 2 diabetes.
Protecting consumers: Implementation of Colorado's antidiscrimination law in insurance
Optimizing health equity expertise for CMS initiatives
The U.S. government has recently amplified its commitment to health equity as evidenced by the release of documents such as the “CMS Framework for Health Equity 2022-2032”....
What Medicare plans should know about CMS’ recent health equity-focused initiatives
We delve into three recent CMS health-equity-focused initiatives, which have significant implications for Medicare Advantage organizations.
Summary of The U.S. Playbook to Address Social Determinants of Health
While data science techniques offer immense potential for risk managers, (re)insurers need a multidisciplinary approach to tackle challenges and ensure successful implementation.
ACO REACH: Leveraging data to reach the underserved and address disparities
Now that CMS requires REACH ACOs to measure and address health equity, providers face a steep learning curve with data analysis.
Self-reported health status of Medicare Part D beneficiaries
How do responses differ by demographic characteristics?
Developing fair models in an imperfect world: How to deal with bias in AI
This white paper discusses how to detect bias and build a fair machine-learning model.