In 2014, states approving the expansion of Medicaid, a tenet of the Affordable Care Act, received monies to expand health care coverage. Using publicly available national datasets1, we mined data to explore the effects of expanded health care coverage on medical debt, and also to inform continued optimization of these data for health policy research. Between 2012 and 2015, there was a decrease in the percentage of individuals claiming medical debt. States adopting Medicaid expansion showed a 23.31% decrease, while states NOT adopting had a 19.01% decrease. Expansion of health coverage showed no statistical increase in the proportion of respondents with regards to the number of nights spent in a hospital in a year and the number of times visited a health professional in the past two weeks. Additionally, data illustrated significant findings in the proportion of individuals who responded in the affirmative to to the questions associated with cost of care and delay in care, trouble paying for medical bills, and losing coverage after pregnancy. While the data revealed substantive findings, our exploration detected two ways in which these publicly available datasets can be optimized for health policy research: include respondent’s state of residence, and specify codification of data with empty or null values. By including these two variables, health policy researchers can further leverage these invaluable datasets for informing impacts of Medicaid expansion, and possibly informing the plausibility of patient debt as a significant determinant of health. However, publicly available datasets were able to detect the positive ripples of Medicaid expansion within the first year of implementation. These findings can potentially impact public policy by strengthening the argument for expansion of coverage for individuals with scarce resources without drastically increasing the total financial cost of coverage, impacting the workflow of hospitals/clinics, and and limiting uncompensated care provided by hospitals/clinicians.