Metadata about: This article analyzes mid-implementation course corrections in a quality improvement innovation for a maternal and child health network working in a large Midwestern metropolitan area. Participating organizations received restrictive funding from this network to screen pregnant women and new mothers for depression, make appropriate referrals, and log screening and referral data into a project-wide data system over a one-year pilot program. This paper asked three research questions: (1) What problems emerged by mid-implementation of this program that required course correction? (2) How were advocacy targets developed to influence network and agency responses to these mid-course problems? (3) What specific course corrections were identified and implemented to get implementation back on track? This ethnographic case study employs qualitative methods including participant observation and interviews. Data were analyzed using the analytic method of qualitative description, in which the goal of data analysis is to summarize and report an event using the ordinary, everyday terms for that event and the unique descriptions of those present. Three key findings are noted. First, network participants quickly responded to the emerged problem of under-performing screening and referral completion statistics. Second, they shifted advocacy targets away from executive appeals and toward the line staff actually providing screening. Third, participants endorsed two specific course corrections, using “opt out, not opt in” choice architecture at intake and implementing visual incentives for workers to track progress. Opt-out choice architecture and visual incentives served as useful means of focusing organizational collaboration and correcting mid-implementation problems.

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