12 Scaling improvement: lessons from home visiting collaborative improvement and innovation network. (30th November 2022) - Finnerty, Patricia; Mackrain, Mary; Tandon, Darius; Arbour, MaryCatherine
Abstract : Background: Scaling up effective public health interventions to improve population health poses ongoing challenges to practitioners, policy makers and funders. Since 2013, the Home Visiting Collaborative Improvement and Innovation Network (HV CoIIN) uses the Breakthrough Series Model (BTS) to accelerate improvements in home visiting practices and outcomes for high-risk families. HV CoIIN 1.0 supported 14 home visiting programs in eight states to improve maternal depression identification, referral, service access and symptom alleviation. This paper presents HV CoIIN 2.0's effort to scale those improvements to four additional states and 47 local implementing agencies (LIAs). Objectives to describe HV CoIIN 2.0's approach to scaling improvements in maternal depression and examine its execution, participant experience and effectiveness. Methods: HV CoIIN 2.0 supported four Maternal, Infant and Early Childhood Home Visiting Program Grantees to facilitate a scale-up effort in their state using a defined theory of change ( figure 1 ). Grantees received a structural framework, training, tools, measures, a peer-to-peer community of practice, and coaching. We analyzed execution with programmatic data, participant experience via qualitative interviews, and effectiveness using control charts. Results: HV CoIIN 2.0's scale execution resulted in high participation, data and PDSA submission ( figure 1 ). Grantees reported positive experience of peer learning, sharing resources, coaching and new competencies. P-charts illustrate improvements in depression screening (88.2-91.5%), referral (80.5-90.1%), 30-day check-ins (45.4-72.5%) and symptom alleviation (65.5-79.7%) ( figures 2, 3, & 4 ). Conclusions: HV CoIIN 2.0 approach to scale is a promising methodology to build systemic and sustainable capacity to use QI techniques to scale data-driven practices that improve services to families and have a measurable impact on maternal and child health outcomes.