Oregon’s approach to leveraging system-level data to guide a social determinants of health-informed approach to children’s healthcare

Background Children’s health and healthcare use are impacted by both medical conditions and social factors, such as their home and community environment. As healthcare systems manage a pediatric population, information about these factors is crucial to providing quality care coordination. Methods The authors developed a novel methodology combining medical complexity (using the Pediatric Medical Complexity Algorithm) and social complexity (using available family social factors known to impact a child’s health and healthcare use) to create a new health complexity model at both the population-level and individual-level. System-level data from Oregon’s Medicaid Management Information Systems and Integrated Client Services database was analysed, examining claims data and service utilization, to calculate the health complexity of children enrolled in Medicaid/Child Health Insurance Program (CHIP) across Oregon. Results Of the 390 582 children ages 0 to 17 enrolled in Medicaid/CHIP in Oregon from July 2015 to June 2016, 83.4% (n=325 900) had some level of medical and/or social complexity and 22.1% (n=85 839) had health complexity (both medical and social complexity). Statistically significant (p Conclusions Given the high proportion of children with health complexity, these findings demonstrate that a large number of Medicaid/CHIP-insured children could benefit from targeted care coordination and differential resource allocation. Reports have been shared with state, county and health system leaders to drive work across the state. This paper describes the collaborative process necessary for other states considering similar work.