Nudging New York: adaptive models and the limits of behavioral interventions to reduce no-shows and health inequalities

Missed healthcare appointments (no-shows) are costly and operationally inefficient for health systems. No-show rates are particularly high for vulnerable populations, even though these populations often require additional care. Few studies on no-show behavior or potential interventions exist specifically for Federally Qualified Health Centers (FQHCs), which care for over 24 million disadvantaged individuals in the United States. The purpose of this study is to identify predictors of no-show behavior and to analyze the effects of a reminder intervention in urban FQHCs in order to design effective policy solutions to a protracted issue in healthcare. This is a retrospective observational study using electronic medical record data from 11 facilities belonging to a New York City-based FQHC network between June 2017 to April 2018. This data includes 53,149 visits for 41,495 unique patients. Seven hierarchical generalized linear models and generalized additive models were used to predict no-shows, and multiple regression models evaluated the effectiveness of a reminder. All analyses were conducted in R. The strongest predictor of no-show rates in FQHCs is whether or not patients are assigned to empaneled providers (z = - 91.45, p