A Mixed Methods Evaluation of a Quality Improvement Model to Optimize Perinatal and Primary Care in the Community Health Setting

PURPOSE Many maternal deaths occur beyond the acute birth encounter. There are opportunities for improving maternal health outcomes through facilitated quality improvement efforts in community settings, particularly in the postpartum period. We used a mixed methods approach to evaluate a collaborative quality improvement (QI) model in 6 Chicago Federally Qualified Health Centers (FQHCs) that implemented workflows optimizing care continuity in the extended postpartum period for high-risk prenatal patients. METHODS The Quality Improvement Learning Collaborative focused on the implementation of a registry of high-risk prenatal patients to link them to primary care and was implemented in 2021; study data were collected in 2021-2022. We conducted a quantitative evaluation of FQHC-reported aggregate structure, process, and outcomes data at baseline (2020) and monthly (2021). Qualitative analysis of semistructured interviews of participating FQHC staff focused on the experience of participating in the collaborative. RESULTS At baseline, none of the 6 participating FQHCs had integrated workflows connecting high-risk prenatal patients to primary care; by the end of implementation of the QI intervention, such workflows had been implemented at 19 sites across all 6 FQHCs, and 54 staff were trained in using these workflows. The share of high-risk patients transitioned to primary care within 6 months of delivery significantly increased from 25% at baseline to 72% by the end of implementation. Qualitative analysis of interviews with 11 key informants revealed buy-in, intervention flexibility, and collaboration as facilitators of successful engagement, and staffing and data infrastructure as participation barriers. CONCLUSIONS Our findings show that a flexible and collaborative QI approach in the FQHC setting can help optimize care delivery. Future evaluations should incorporate the patient experience and patient-level data for comprehensive analysis.