Improving Postpartum Depression Screening and Referral in the Pediatric Setting

Postpartum depression (PPD) affects up to 20% of American mothers and, if left untreated, can have serious, lifelong effects on women and their children (Earls, 2010). For the latter, PPD can negatively affect behavior, language and cognitive development, and physical health (O’Hara & McCabe, 2013). It is estimated that less than half of PPD cases are even identified—resulting from screening and treatment recommendation discrepancies from major domestic and international organizations (Gjerdingen & Yawn, 2007). Due to their longitudinal relationship with their patients and their patients’ families, pediatric providers are uniquely situated to effectively screen mothers for PPD while educating them on symptoms, treatments, and resources (Fernandez y Garcia et al., 2015). Therefore, the American Academy of Pediatrics recommends that pediatric practices screen for PPD at the one-, two-, four-, and six-month well child checks; however, few pediatric practices oblige (Earls, 2010). Using quality improvement methodologies and the Lewin Change Theory, this project standardized the PPD screening schedule and developed a novel referral algorithm that was concurrently implemented at a rural pediatric clinic in Virginia. The project significantly increased the clinic’s screening rate from 33% to 80% (p<0.001) and, although not statistically significant, improved referral rates from 66% to 79%. The referral algorithm was functional for providers and can be replicated by other pediatric practices. Effective PPD screening can take as little as one minute. This is the first study to study the effectiveness of a referral algorithm and one of only a handful of studies quantifying the effectiveness of standardizing screening schedules in pediatrics. By standardizing PPD screening and implementing a referral algorithm in the ambulatory pediatric setting, more PPD cases can be identified, further evaluated, and treated—ultimately improving maternal and infant health outcomes while demonstrating that the small changes the project represents can be duplicated by pediatric practices in any setting

In Data Veritas - Data Driven Testing for Distributed Systems

ABSTRACT The increasing deployment of distributed systems to solve large data and computational problems has not seen a concomitant increase in tools and techniques to test these systems. In this paper, we propose a data driven approach to testing. We translate our intuitions and expectations about how the system should behave into invariants, the truth of which can be verified from data emitted by the system. Our particular implementation of the invariants uses Q, a highperformance analytical database, programmed with a vector language. To show the practical value of this approach, we describe how it was used to test Helix, a distributed cluster manager deployed at LinkedIn. We make the case that looking at testing as an exercise in data analytics has the following benefits. It (a) increases the expressivity of the tests (b) decreases their fragility and (c) suggests additional, insightful ways to understand the system under test. As the title of the paper suggests, there is truth in the data -we only need to look for it