Millions of people who enrolled in Medicaid during the COVID-19 pandemic risk losing coverage in the spring of 2023, leaving many worried about their healthcare coverage and many healthcare providers struggling to redetermine coverage. It's an anxiety-inducing situation, but robotic process automation (RPA) is on hand to help. States were required to keep people enrolled in Medicaid throughout the pandemic due to a decision made by the HHS declaring COVID-19 as a Public Health Emergency (PHE). However, PHE is set to end on April 11, 2023. And while the HHS has extended the PHE in the past, it's unlikely to do so again.

Effective complements to human judgment, artificial intelligence techniques have started to aid human decisions in complicated social problems across the world. In the context of United States for instance, automated ML/DL classification models offer complements to human decisions in determining Medicaid eligibility. However, given the limitations in ML/DL model design, these algorithms may fail to leverage various factors for decision making, resulting in improper decisions that allocate resources to individuals who may not be in the most need. In view of such an issue, we propose in this paper the method of \textit{fairgroup construction}, based on the legal doctrine of \textit{disparate impact}, to improve the fairness of regressive classifiers. Experiments on American Community Survey dataset demonstrate that our method could be easily adapted to a variety of regressive classification models to boost their fairness in deciding Medicaid Eligibility, while maintaining high levels of classification accuracy.