Health care decision-making should include real-world evidence (RWE) for clinical and policy decisions. Indeed, randomized controlled clinical trials (RCTs) represent one thread in a large nomological network of evidence informing clinical and policy decisions. While RCT data represent the epitome of internal validity, real world data should be more generalizable to actual field settings. The question is, how can we extract usable information from messy but abundant real-world data? Nearly 50 years ago, a cadre of scientists at Northwestern University began working on RWE problem applied social changes promised by the Johnson Great Society programs of the 1960’s. Thus, were principles and methodology of quasi-experiments developed. Hence, RWE data analysis techniques do not need inventing, but rather rediscovery and suitable application to health care. This presentation includes a short history and purpose of quasi-experimental designs and their potential for applications to health system outcomes. Several designs will be discussed, along with their associated strengths and weaknesses: instrumental variables, regression discontinuity, propensity score analysis, likelihood ratios.