Case Study: Navigating Fairness in the Healthcare Domain

In this lesson, we will explore an exciting journey into the world of healthcare data and discover how we can use Fairlearn to address fairness issues in the results.

Research has shown that there are differences in how healthcare resources are allocated among different racial groups in various countries.

Our main focus is on using automated AI solutions to recommend patients for high-risk care management programs. These programs are designed to improve the quality of care for patients with complex health needs by giving them extra attention and resources from trained providers. However, because these programs can be costly, healthcare systems rely on algorithms to select the patients who would benefit the most from these programs.

While these algorithms have the potential to improve healthcare outcomes and reduce costs, they also have the potential to perpetuate disparities.

We need to consider certain factors to ensure that our AI algorithms for patient selection in high-risk care management programs are fair and equitable.

• First, we need to identify groups that are disproportionately affected, especially those based on race and ethnicity, as previous studies have shown.

• Secondly, we need to understand the different ways in which these algorithms can cause harm, particularly in terms of how they allocate resources. For example, we need to be aware of false negatives, where individuals who should receive care are not recommended for it.

By taking these fairness considerations into account, we can address potential disparities and improve healthcare outcomes for all patients.