The US consistently lags nearly all developed nations in percentage of population covered by basic health insurance. To address this, ‘RomneyCare’ was implemented in Massachusetts (state level) in 2006, and later on ObamaCare’s similar major provisions came into effect in 2014 at the federal level. ObamaCare came under scrutiny and, therefore, properly quantifying its effects is paramount. In this paper a new way of doing so is proposed, by looking at health coverage outcomes in Massachusetts as part of an ‘always treated’ control group. Quantifying ObamaCare’s impacts by comparing against a control group, while using American Community Survey data, can only be done through this method due to constraints in data availability. While this method has been used long before, Kim and Lee’s (2019) article formalized this difference-in-differences in reverse (DDR) approach, thereby making it possible to attempt to fill a gap in this literature. Moreover, the implementation of this identification strategy with readily available public use individual data should strengthen the case for the use of this method for ex-post policy analyses. The quasi-experimental setting existent in neighboring states Massachusetts and Rhode Island allowed for the conclusion that this health policy did indeed increase the odds of an individual being insured by 88,5%, after controlling for several demographic and socioeconomic variables.