Algorithms for guiding health care decisions have come under increasing scrutiny for being unfair to certain racial and ethnic groups. The authors describe their multistep process, using data from 3,465 individuals, to reduce racial and ethnic bias in an algorithm developed to identify state Medicaid beneficiaries experiencing homelessness and chronic health needs who were eligible for coordinated health care and housing supports. Through an iterative process of adjusting inputs, reviewing outputs with diverse stakeholders, and performing quality assurance, the authors developed an algorithm that achieved racial and ethnic parity in the selection of eligible Medicaid beneficiaries.
View Article and Find Full Text PDFObjective: To develop and test predictive models of discontinuation of behavioral health service use within 12 months in transitional age youth with recent behavioral health service use.
Data Sources: Administrative claims for Medicaid beneficiaries aged 15-26 years in Connecticut.
Study Design: We compared the performance of a decision tree, random forest, and gradient boosting machine learning algorithms to logistic regression in predicting service discontinuation within 12 months among beneficiaries using behavioral health services.
Emerging adults (EA), individuals between the ages of 15-26, face many challenges in their transition to a new developmental stage, especially those with behavioral health concerns who do not receive the supports they need. Many EA drop out of services at 18, which is likely due in part to the need to transition to the adult service system and the lack of available transition support services in child/adolescent service systems. Though this is a clear disparity, research on EA service utilization, especially those enrolled in Medicaid and with co-occurring conditions, is rare.
View Article and Find Full Text PDFObjective: Youths are using emergency departments (EDs) for behavioral health services in record numbers, even though EDs are suboptimal settings for service delivery. In this article, the authors evaluated a mobile crisis service intervention implemented in Connecticut with the aim of examining whether the intervention was associated with reduced behavioral health ED use among those in need of services.
Methods: The authors examined two cohorts of youths: 2,532 youths who used mobile crisis services and a comparison sample of 3,961 youths who used behavioral health ED services (but not mobile crisis services) during the same fiscal year.
Children within the child welfare system are more likely to experience emotional and behavioral problems than children not involved with the system. Many states have adopted standardized risk and assessment measures to inform decision-making on appropriate levels of care related to placement or service intensity for children within the system. This study examined the relationship of caseworker ratings of risk across multiple domains to youth functioning and service use for a sample of children open to the child welfare system.
View Article and Find Full Text PDFIntroduction to the 3rd Biennial Conference of the Society for Implementation Research Collaboration: advancing efficient methodologies through team science and community partnerships Cara Lewis, Doyanne Darnell, Suzanne Kerns, Maria Monroe-DeVita, Sara J. Landes, Aaron R. Lyon, Cameo Stanick, Shannon Dorsey, Jill Locke, Brigid Marriott, Ajeng Puspitasari, Caitlin Dorsey, Karin Hendricks, Andria Pierson, Phil Fizur, Katherine A.
View Article and Find Full Text PDFResearch on implementation science has increased significantly over the past decade. In particular, psychologists have looked closely at the value and importance of bridging the gap between science and practice. As evidence-based practices (EBPs) become more prevalent, concrete mechanisms are needed to bring these scientifically supported treatments and interventions to community-based settings.
View Article and Find Full Text PDFRisk assessments allow child and youth services to identify children who are at risk for maltreatment (e.g., abuse, neglect) and help determine the restrictiveness of placements or need for services among youth entering a child welfare system.
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