Digital Divide Data Helps Researchers Capture an Unprecedented Opportunity to Study State Health Insurance Users

We’re thrilled to share our new case study on a project this year working with a group of researchers from the Oregon Health Study (OHS), a multimillion-dollar effort to capitalize on a unique research opportunity.

We’re thrilled to share our new case study on a project this year working with a group of researchers from the Oregon Health Study (OHS), a multimillion-dollar effort to capitalize on a unique research opportunity.

In 2008, the State of Oregon opened a waiting list for enrollment in the Oregon Health Plan, Oregon’s public health insurance program for low-income adults. Over 85,000 people put their names on the list – many more than the state could afford to insure at the time. In these circumstances, the state decided that the fairest procedure was a random one: 35,000 individuals were randomly selected from the list to receive applications for the health plan.

Several researchers around the country, including Amy Finkelstein (MIT), Katherine Baicker (Harvard School of Public Health), and Bill Wright (Providence Health & Services in Portland, OR) realized that Oregon’s random selection procedure could serve as the basis for a randomized, controlled study of the effects of health insurance on a variety of outcomes –- health, access to healthcare, financial status, etc. Randomized, controlled experiments are considered the “gold standard” in medicine and the physical sciences, but are generally difficult to arrange in the social sciences. The events in Oregon were an unprecedented chance to apply these rigorous methods to the study of health insurance.

Read more about how DDD helped these researchers process close to 70,000 surveys.