Development of administrative algorithm for identification of pediatric mental health avoidable hospital days.

Abstract

Youth hospitalized for a mental health (MH) condition frequently experience MH-related avoidable hospital days (MH-ADs), or days in which they remain hospitalized for a MH reason despite not requiring services unique to a medical hospital. However, there is currently no reliable method for identifying MH-ADs across healthcare systems, preventing investigation of this systemic problem. A universal and efficient method of determining MH-ADs is needed to guide improvements in access to care for youth with MH needs while reducing unnecessary medical hospital days. Our objectives were to create an administrative algorithm for identifying MH-ADs and to evaluate the algorithm's validity using clinical data from a single hospital site. The resulting algorithm, drawing on coding and billing data from the Pediatric Health Information System Database, identified MH-ADs with good sensitivity (79.9%), specificity (79.2%), and positive predictive value (95.1%), but low negative predictive value (43.5%) when compared to clinically determined MH-ADs.